From 60735ab7ea8fd386bf426a2ae3c621fcc2dc0c86 Mon Sep 17 00:00:00 2001 From: qdjinghao <343386707@qq.com> Date: Tue, 18 Jun 2024 10:19:35 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E7=94=B5=E5=AE=B9=E9=A2=84?= =?UTF-8?q?=E6=B5=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .ipynb_checkpoints/20231227-checkpoint.ipynb | 141 +- .ipynb_checkpoints/20240102-checkpoint.ipynb | 170 +- .../20240123-煤沥青-CBA-checkpoint.ipynb | 1257 ++ .../20240123_煤沥青-checkpoint.ipynb | 730 + .../20240123_煤炭-checkpoint.ipynb | 549 + .../20240617_电容器-checkpoint.ipynb | 464 + .../CBA_4feature-checkpoint.ipynb | 2 +- .../CBA_vad_fcad-checkpoint.ipynb | 182 +- .ipynb_checkpoints/TEST-checkpoint.csv | 150 + .../multi-task-NN-0123-checkpoint.ipynb | 1226 ++ .../multi-task-NN-checkpoint.ipynb | 4693 +++-- .../multi-task0102-checkpoint.ipynb | 129 +- .../multioutput_regression-checkpoint.ipynb | 232 +- .ipynb_checkpoints/plot71-checkpoint.png | Bin 0 -> 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changed, 35945 insertions(+), 3839 deletions(-) create mode 100644 .ipynb_checkpoints/20240123-煤沥青-CBA-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/20240123_煤沥青-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/20240123_煤炭-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/20240617_电容器-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/TEST-checkpoint.csv create mode 100644 .ipynb_checkpoints/multi-task-NN-0123-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/plot71-checkpoint.png delete mode 100644 .ipynb_checkpoints/旧数据建模-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/未命名-checkpoint.ipynb create mode 100644 20240123-煤沥青-CBA.ipynb create mode 100644 20240123_煤沥青.ipynb create mode 100644 20240123_煤炭.ipynb create mode 100644 20240617_电容器.html create mode 100644 20240617_电容器.ipynb create mode 100644 TEST.csv create mode 100644 data/20240123/煤沥青数据.xlsx create mode 100644 data/20240123/煤炭数据.xlsx create mode 100644 data/20240617/电容性能新.xlsx delete mode 100644 model.png create mode 100644 multi-task-NN-0123.ipynb create mode 100644 plot71.png create mode 100644 rst/两个指标.xlsx create mode 100644 rst/决策树预测吸附量.xlsx create mode 100644 rst/总孔体积_比表.csv create mode 100644 test.zip create mode 100644 总孔体积.png delete mode 100644 旧数据建模.ipynb create mode 100644 未命名.ipynb create mode 100644 比表面积.png create mode 100644 氮气吸附量.png diff --git a/.ipynb_checkpoints/20231227-checkpoint.ipynb b/.ipynb_checkpoints/20231227-checkpoint.ipynb index bbd1042..77752ff 100644 --- a/.ipynb_checkpoints/20231227-checkpoint.ipynb +++ b/.ipynb_checkpoints/20231227-checkpoint.ipynb @@ -241,6 +241,27 @@ { "cell_type": "code", "execution_count": 3, + "id": "b1a0903a-596f-4d6f-98b1-a668a26f4175", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 26)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "8b113a99-8c56-455c-9ee8-3ac32a686e24", "metadata": {}, "outputs": [], @@ -250,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "e134c799-b11c-46c8-898d-ac1d9e47e527", "metadata": {}, "outputs": [], @@ -261,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "id": "359c9cc6-2694-46a6-9f18-6361e220542a", "metadata": {}, "outputs": [ @@ -270,18 +291,13 @@ "text/plain": [ "Index(['热处理条件-热处理次数', '热处理条件-是否是中温停留', '第一次热处理-温度', '第一次热处理-升温速率',\n", " '第一次热处理-保留时间', '第二次热处理-温度', '第二次热处理-升温速率·', '第二次热处理-保留时间',\n", - " '共碳化-是否是共碳化物质', '共碳化-共碳化物质/沥青', '模板剂-与沥青比例', '活化剂-是否KOH活化', '活化剂-比例',\n", - " '混合方式-混合方式', '碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积',\n", - " '碳材料结构特征-平均孔径', '共碳化-种类_2-甲基咪唑', '共碳化-种类_三聚氰胺', '共碳化-种类_尿素',\n", - " '共碳化-种类_硫酸铵', '共碳化-种类_聚磷酸铵', '模板剂-模板剂制备方式_无', '模板剂-模板剂制备方式_溶液合成',\n", - " '模板剂-模板剂制备方式_热分解', '模板剂-模板剂制备方式_直接购买', '模板剂-模板剂制备方式_自己合成',\n", - " '模板剂-种类_Al2O3', '模板剂-种类_TiO2', '模板剂-种类_α-Fe2O3', '模板剂-种类_γ-Fe2O3',\n", - " '模板剂-种类_二氧化硅', '模板剂-种类_氢氧化镁', '模板剂-种类_氧化钙', '模板剂-种类_氧化锌', '模板剂-种类_氧化镁',\n", - " '模板剂-种类_氯化钠', '模板剂-种类_氯化钾', '模板剂-种类_碱式碳酸镁', '模板剂-种类_碳酸钙', '模板剂-种类_纤维素'],\n", + " '共碳化-是否是共碳化物质', '共碳化-种类', '共碳化-共碳化物质/沥青', '模板剂-模板剂制备方式', '模板剂-种类',\n", + " '模板剂-与沥青比例', '活化剂-是否KOH活化', '活化剂-比例', '混合方式-混合方式', '碳材料结构特征-比表面积',\n", + " '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径'],\n", " dtype='object')" ] }, - "execution_count": 34, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "6cc22ecb-1070-4e63-a496-efc838094958", "metadata": {}, "outputs": [], @@ -306,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "24f58281-9f13-49ef-b44d-81d0644d6976", "metadata": {}, "outputs": [], @@ -316,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "3368163e-85a1-4487-8078-be51cb5fb560", "metadata": {}, "outputs": [], @@ -326,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", "metadata": {}, "outputs": [], @@ -337,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", "metadata": {}, "outputs": [], @@ -347,7 +363,38 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, + "id": "e545ccba-07b2-4c49-bd48-f49b3892fafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 42)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f786afc1-d805-4dcd-a4b4-8170f87fbc23", + "metadata": {}, + "outputs": [], + "source": [ + "train_data.to_csv('./train_data.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", "metadata": {}, "outputs": [], @@ -358,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", "metadata": {}, "outputs": [], @@ -369,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "42597842-1acb-4263-bdad-bfca7b11bcb5", "metadata": {}, "outputs": [], @@ -379,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", "metadata": {}, "outputs": [], @@ -397,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", "metadata": {}, "outputs": [], @@ -407,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", "metadata": {}, "outputs": [], @@ -418,13 +465,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 17, "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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"text/plain": [ "
" ] @@ -509,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 18, "id": "dcce8331-256f-4e22-8ac5-f07dca12f3cf", "metadata": {}, "outputs": [ @@ -544,27 +591,27 @@ " \n", " \n", " 碳材料结构特征-比表面积\n", - " 303184.717382\n", - " 544.657385\n", - " 421.657610\n", - " 0.847088\n", - " 0.372696\n", + " 333217.197067\n", + " 545.874683\n", + " 419.177758\n", + " 0.602743\n", + " 0.369714\n", " \n", " \n", " 碳材料结构特征-总孔体积\n", - " 0.128721\n", - " 0.354206\n", - " 0.280979\n", - " 0.900388\n", - " 0.435381\n", + " 0.115585\n", + " 0.326015\n", + " 0.260617\n", + " 0.855863\n", + " 0.426921\n", " \n", " \n", " 碳材料结构特征-微孔体积\n", - " 0.042627\n", - " 0.205114\n", - " 0.160595\n", - " 3.522258\n", - " 0.497659\n", + " 0.047409\n", + " 0.216009\n", + " 0.172998\n", + " 2.598594\n", + " 0.487892\n", " \n", " \n", " 碳材料结构特征-平均孔径\n", @@ -580,13 +627,13 @@ ], "text/plain": [ " MSE RMSE MAE MAPE R2\n", - "碳材料结构特征-比表面积 303184.717382 544.657385 421.657610 0.847088 0.372696\n", - "碳材料结构特征-总孔体积 0.128721 0.354206 0.280979 0.900388 0.435381\n", - "碳材料结构特征-微孔体积 0.042627 0.205114 0.160595 3.522258 0.497659\n", + "碳材料结构特征-比表面积 333217.197067 545.874683 419.177758 0.602743 0.369714\n", + "碳材料结构特征-总孔体积 0.115585 0.326015 0.260617 0.855863 0.426921\n", + "碳材料结构特征-微孔体积 0.047409 0.216009 0.172998 2.598594 0.487892\n", "碳材料结构特征-平均孔径 0.675843 0.763879 0.509585 0.172561 0.307433" ] }, - "execution_count": 33, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -620,7 +667,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/20240102-checkpoint.ipynb b/.ipynb_checkpoints/20240102-checkpoint.ipynb index f8dc9bd..703dfb1 100644 --- a/.ipynb_checkpoints/20240102-checkpoint.ipynb +++ b/.ipynb_checkpoints/20240102-checkpoint.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "6a94278b-8f51-4edc-966b-4a32876a4536", "metadata": {}, "outputs": [ @@ -215,7 +215,7 @@ "[228 rows x 8 columns]" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "f72789a6-f3fa-4ab1-8b62-999413958608", "metadata": {}, "outputs": [ @@ -244,7 +244,7 @@ " '固定炭Fcad(%)']" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "6ffb1989-3f45-4d1c-84c9-59b1045b7d9e", "metadata": {}, "outputs": [], @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "9c708cc0-9f1b-4669-a350-6d24cb720794", "metadata": {}, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "103349e1-aa4a-427a-a489-9ab28787088b", "metadata": {}, "outputs": [ @@ -286,7 +286,7 @@ "['氢Had(%)', '碳Cad(%)', '氮Nad(%)', '氧Oad(%)', '弹筒发热量Qb,adMJ/kg']" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -298,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 7, "id": "839e45dc-e9c8-4956-950b-035687469c81", "metadata": {}, "outputs": [ @@ -409,7 +409,7 @@ "4 54.78 " ] }, - "execution_count": 44, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -421,17 +421,7 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "24233d12-9468-49b8-a371-0c6c508c387e", - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "id": "54cd27a6-1a8a-47c0-93d9-c948960a7842", "metadata": {}, "outputs": [], @@ -441,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "id": "bba14f71-9d69-4c82-b6bc-b9b74c725b25", "metadata": {}, "outputs": [], @@ -451,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "id": "e3a9ad55-0132-430f-ac57-c2e7f8e8590a", "metadata": {}, "outputs": [], @@ -461,13 +451,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 25, "id": "013c6a58-65f6-48e9-8d7f-b56c87de5b11", "metadata": {}, "outputs": [], "source": [ - "param_xgb = {\"silent\": True,\n", - " \"obj\": 'reg:linear',\n", + "params_xgb = {\"objective\": 'reg:squarederror',\n", " \"subsample\": 1,\n", " \"max_depth\": 15,\n", " \"eta\": 0.3,\n", @@ -475,12 +464,12 @@ " \"lambda\": 1,\n", " \"alpha\": 0,\n", " \"colsample_bytree\": 0.9,}\n", - "num_round = 1000" + "num_boost_round = 1000" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 26, "id": "086f1901-8388-47e9-ae7c-1b2709bc1e22", "metadata": {}, "outputs": [], @@ -491,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 27, "id": "fb7b06af-84bc-483c-b086-7826d7befc9c", "metadata": {}, "outputs": [ @@ -499,30 +488,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 1.9436, RMSE: 1.3941, MAE: 1.1861, MAPE: 3.94 %, R_2: 0.6095\n", - "MSE: 1.8735, RMSE: 1.3688, MAE: 1.132, MAPE: 3.77 %, R_2: 0.495\n", - "MSE: 2.0587, RMSE: 1.4348, MAE: 1.0706, MAPE: 4.08 %, R_2: 0.7862\n", - "MSE: 1.9298, RMSE: 1.3892, MAE: 1.1469, MAPE: 3.84 %, R_2: 0.5332\n", - "MSE: 1.4583, RMSE: 1.2076, MAE: 1.097, MAPE: 3.67 %, R_2: 0.6894\n", - "MSE: 2.0822, RMSE: 1.443, MAE: 1.1645, MAPE: 3.88 %, R_2: 0.5975\n", - "MSE: 1.3521, RMSE: 1.1628, MAE: 0.9905, MAPE: 3.37 %, R_2: 0.7479\n", - "MSE: 1.4057, RMSE: 1.1856, MAE: 0.9998, MAPE: 3.3 %, R_2: 0.2946\n", - "MSE: 2.2274, RMSE: 1.4925, MAE: 1.2638, MAPE: 4.19 %, R_2: 0.6785\n", - "MSE: 1.4866, RMSE: 1.2193, MAE: 1.0797, MAPE: 3.67 %, R_2: 0.7261\n" + "MSE: 0.475, RMSE: 0.6892, MAE: 0.5507, MAPE: 1.86 %, R_2: 0.9046\n", + "MSE: 1.1415, RMSE: 1.0684, MAE: 0.9133, MAPE: 3.06 %, R_2: 0.6923\n", + "MSE: 0.7247, RMSE: 0.8513, MAE: 0.6606, MAPE: 2.32 %, R_2: 0.9247\n", + "MSE: 1.3652, RMSE: 1.1684, MAE: 0.9609, MAPE: 3.24 %, R_2: 0.6698\n", + "MSE: 0.4552, RMSE: 0.6747, MAE: 0.5732, MAPE: 1.94 %, R_2: 0.903\n", + "MSE: 0.6357, RMSE: 0.7973, MAE: 0.6374, MAPE: 2.2 %, R_2: 0.8771\n", + "MSE: 0.9972, RMSE: 0.9986, MAE: 0.752, MAPE: 2.47 %, R_2: 0.8141\n", + "MSE: 1.5218, RMSE: 1.2336, MAE: 1.0569, MAPE: 3.45 %, R_2: 0.2363\n", + "MSE: 0.6891, RMSE: 0.8301, MAE: 0.6825, MAPE: 2.22 %, R_2: 0.9005\n", + "MSE: 1.6864, RMSE: 1.2986, MAE: 1.0004, MAPE: 3.51 %, R_2: 0.6893\n" ] }, { "data": { "text/plain": [ - "MSE 1.781792\n", - "RMSE 1.329760\n", - "MAE 1.113084\n", - "MAPE 0.037719\n", - "R_2 0.615796\n", + "MSE 0.969172\n", + "RMSE 0.961023\n", + "MAE 0.778783\n", + "MAPE 0.026288\n", + "R_2 0.761188\n", "dtype: float64" ] }, - "execution_count": 43, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -558,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 28, "id": "90841cb7-4f28-4a33-93ac-93df69f1a5a1", "metadata": {}, "outputs": [ @@ -566,30 +555,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSE: 4.6724, RMSE: 2.1616, MAE: 1.7297, MAPE: 3.42 %, R2: 0.8346\n", - "MSE: 3.0512, RMSE: 1.7468, MAE: 1.4485, MAPE: 2.62 %, R2: 0.8011\n", - "MSE: 7.6672, RMSE: 2.769, MAE: 1.951, MAPE: 4.56 %, R2: 0.8856\n", - "MSE: 4.0334, RMSE: 2.0083, MAE: 1.487, MAPE: 2.77 %, R2: 0.8216\n", - "MSE: 2.6382, RMSE: 1.6243, MAE: 1.1551, MAPE: 2.12 %, R2: 0.846\n", - "MSE: 5.8097, RMSE: 2.4103, MAE: 1.8683, MAPE: 3.8 %, R2: 0.83\n", - "MSE: 2.3446, RMSE: 1.5312, MAE: 1.1294, MAPE: 2.28 %, R2: 0.9069\n", - "MSE: 3.0069, RMSE: 1.734, MAE: 1.3782, MAPE: 2.46 %, R2: 0.6541\n", - "MSE: 4.1652, RMSE: 2.0409, MAE: 1.5685, MAPE: 3.2 %, R2: 0.859\n", - "MSE: 4.2023, RMSE: 2.05, MAE: 1.6284, MAPE: 3.2 %, R2: 0.869\n" + "MSE: 0.9821, RMSE: 0.991, MAE: 0.7698, MAPE: 1.44 %, R2: 0.9652\n", + "MSE: 1.2674, RMSE: 1.1258, MAE: 0.8756, MAPE: 1.64 %, R2: 0.9174\n", + "MSE: 0.9137, RMSE: 0.9559, MAE: 0.757, MAPE: 1.46 %, R2: 0.9864\n", + "MSE: 1.6012, RMSE: 1.2654, MAE: 1.0173, MAPE: 1.89 %, R2: 0.9292\n", + "MSE: 1.4694, RMSE: 1.2122, MAE: 0.8524, MAPE: 1.59 %, R2: 0.9142\n", + "MSE: 0.7552, RMSE: 0.869, MAE: 0.7202, MAPE: 1.39 %, R2: 0.9779\n", + "MSE: 0.5474, RMSE: 0.7398, MAE: 0.5467, MAPE: 1.0 %, R2: 0.9783\n", + "MSE: 1.2779, RMSE: 1.1305, MAE: 0.9452, MAPE: 1.73 %, R2: 0.853\n", + "MSE: 1.1908, RMSE: 1.0912, MAE: 0.9004, MAPE: 1.72 %, R2: 0.9597\n", + "MSE: 3.9312, RMSE: 1.9827, MAE: 1.2707, MAPE: 2.65 %, R2: 0.8775\n" ] }, { "data": { "text/plain": [ - "MSE 4.159107\n", - "RMSE 2.007631\n", - "MAE 1.534427\n", - "MAPE 0.030424\n", - "R2 0.830794\n", + "MSE 1.393623\n", + "RMSE 1.136351\n", + "MAE 0.865538\n", + "MAPE 0.016509\n", + "R2 0.935872\n", "dtype: float64" ] }, - "execution_count": 48, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -625,61 +614,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "aa67bc97-1258-44bb-9dae-14ace1661ff6", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSERMSEMAEMAPER2
十折交叉验证均值4.1591072.0076311.5344270.0304240.830794
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" - ], - "text/plain": [ - " MSE RMSE MAE MAPE R2\n", - "十折交叉验证均值 4.159107 2.007631 1.534427 0.030424 0.830794" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { @@ -707,7 +645,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/20240123-煤沥青-CBA-checkpoint.ipynb b/.ipynb_checkpoints/20240123-煤沥青-CBA-checkpoint.ipynb new file mode 100644 index 0000000..12ec842 --- /dev/null +++ b/.ipynb_checkpoints/20240123-煤沥青-CBA-checkpoint.ipynb @@ -0,0 +1,1257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6b84fefd-5936-4da4-ab6b-5b944329ad1d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "import matplotlib.pyplot as plt\n", + "#新增加的两行\n", + "from pylab import mpl\n", + "# 设置显示中文字体\n", + "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n", + "\n", + "mpl.rcParams[\"axes.unicode_minus\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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碳源共碳化物质共碳化物/煤沥青加热次数是否有碳化过程模板剂种类模板剂比例KOH与煤沥青比例活化温度升温速率活化时间混合方式比表面积总孔体积微孔体积平均孔径
0煤沥青无0.01否自制氧化钙1.01.050052.0溶剂908.070.400.341.75
1煤沥青无0.01否自制氧化钙1.00.560052.0溶剂953.950.660.352.76
2煤沥青无0.01否自制氧化钙1.01.060052.0溶剂1388.620.610.531.77
3煤沥青无0.01否自制氧化钙1.02.060052.0溶剂1444.630.590.551.62
4煤沥青无0.02是自制碱式碳酸镁1.01.060052.0溶剂1020.990.450.351.77
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" + ], + "text/plain": [ + " 碳源 共碳化物质 共碳化物/煤沥青 加热次数 是否有碳化过程 模板剂种类 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 \\\n", + "0 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 500 5 \n", + "1 煤沥青 无 0.0 1 否 自制氧化钙 1.0 0.5 600 5 \n", + "2 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 600 5 \n", + "3 煤沥青 无 0.0 1 否 自制氧化钙 1.0 2.0 600 5 \n", + "4 煤沥青 无 0.0 2 是 自制碱式碳酸镁 1.0 1.0 600 5 \n", + "\n", + " 活化时间 混合方式 比表面积 总孔体积 微孔体积 平均孔径 \n", + "0 2.0 溶剂 908.07 0.40 0.34 1.75 \n", + "1 2.0 溶剂 953.95 0.66 0.35 2.76 \n", + "2 2.0 溶剂 1388.62 0.61 0.53 1.77 \n", + "3 2.0 溶剂 1444.63 0.59 0.55 1.62 \n", + "4 2.0 溶剂 1020.99 0.45 0.35 1.77 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤沥青数据.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b016e6bd-b4de-4a6e-a3d5-2fc544042eb8", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=['碳源'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d042e811-9548-480f-8a0b-27b72454fe43", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['共碳化物质', '是否有碳化过程', '模板剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4ccf1708-e9cd-49d5-bdf0-27f6fdb60e1c", + "metadata": {}, + "outputs": [], + "source": [ + "data_0102 = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['共碳化物/煤沥青',\n", + " '加热次数',\n", + " '模板剂比例',\n", + " 'KOH与煤沥青比例',\n", + " '活化温度',\n", + " '升温速率',\n", + " '活化时间',\n", + " '共碳化物质_2-甲基咪唑',\n", + " '共碳化物质_三聚氰胺',\n", + " '共碳化物质_尿素',\n", + " '共碳化物质_无',\n", + " '共碳化物质_硫酸铵',\n", + " '共碳化物质_聚磷酸铵',\n", + " '是否有碳化过程_否',\n", + " '是否有碳化过程_是',\n", + " '模板剂种类_Al2O3',\n", + " '模板剂种类_TiO2',\n", + " '模板剂种类_α-Fe2O3',\n", + " '模板剂种类_γ-Fe2O3',\n", + " '模板剂种类_二氧化硅',\n", + " '模板剂种类_无',\n", + " '模板剂种类_氯化钾',\n", + " '模板剂种类_纤维素',\n", + " '模板剂种类_自制氢氧化镁',\n", + " '模板剂种类_自制氧化钙',\n", + " '模板剂种类_自制氧化锌',\n", + " '模板剂种类_自制氧化镁',\n", + " '模板剂种类_自制碱式碳酸镁',\n", + " '模板剂种类_购买氢氧化镁',\n", + " '模板剂种类_购买氧化钙',\n", + " '模板剂种类_购买氧化锌',\n", + " '模板剂种类_购买氧化镁',\n", + " '模板剂种类_购买氯化钠',\n", + " '模板剂种类_购买碳酸钙',\n", + " '混合方式_溶剂',\n", + " '混合方式_研磨']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积', '平均孔径']\n", + "feature_cols = [x for x in data_0102.columns if x not in out_cols]\n", + "feature_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['比表面积', '总孔体积', '微孔体积', '平均孔径']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-23 12:21:49.081644: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-01-23 12:21:49.083823: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-01-23 12:21:49.125771: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-01-23 12:21:49.126872: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-01-23 12:21:50.338510: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow.keras.backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.initializers import Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", + "metadata": {}, + "outputs": [], + "source": [ + "def get_prediction_model():\n", + " inputs = layers.Input(shape=(1, len(feature_cols)), name='input')\n", + " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", + " # x = layers.Dropout(rate=0.1)(x)\n", + " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", + " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " # transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", + " # out = transformer_block(lstm_out)\n", + " # out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(lstm_out)\n", + " out = layers.Dense(64, activation='relu')(out)\n", + " bet = layers.Dense(1, activation='sigmoid', name='vad')(out)\n", + " model = Model(inputs=[inputs], outputs=[bet])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7a9915ee-0016-44e5-a6fb-5ee90532dc14", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-23 12:22:03.707721: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input (InputLayer) [(None, 1, 36)] 0 \n", + " \n", + " conv1d (Conv1D) (None, 1, 64) 2368 \n", + " \n", + " bidirectional (Bidirection (None, 1, 128) 66048 \n", + " al) \n", + " \n", + " dense (Dense) (None, 1, 128) 16512 \n", + " \n", + " dropout (Dropout) (None, 1, 128) 0 \n", + " \n", + " dense_1 (Dense) (None, 1, 64) 8256 \n", + " \n", + " vad (Dense) (None, 1, 1) 65 \n", + " \n", + "=================================================================\n", + "Total params: 93249 (364.25 KB)\n", + "Trainable params: 93249 (364.25 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = get_prediction_model()\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "372011ea-9876-41eb-a4e6-83ccd6c71559", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.python.keras.utils.vis_utils import plot_model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "965b1d6e-8b9f-4536-8205-06b314aeab51", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data_0102.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", + "metadata": {}, + "outputs": [], + "source": [ + "maxs = train_data.max()\n", + "mins = train_data.min()\n", + "for col in train_data.columns:\n", + " if maxs[col] - mins[col] == 0:\n", + " continue\n", + " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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共碳化物/煤沥青加热次数模板剂比例KOH与煤沥青比例活化温度升温速率活化时间比表面积总孔体积微孔体积...模板剂种类_自制氧化镁模板剂种类_自制碱式碳酸镁模板剂种类_购买氢氧化镁模板剂种类_购买氧化钙模板剂种类_购买氧化锌模板剂种类_购买氧化镁模板剂种类_购买氯化钠模板剂种类_购买碳酸钙混合方式_溶剂混合方式_研磨
00.00.00.10.0666670.0000000.30.3333330.2734370.1376280.270767...0.00.00.00.00.00.00.00.01.00.0
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30.00.00.10.1333330.1666670.30.3333330.4360810.2045050.438498...0.00.00.00.00.00.00.00.01.00.0
40.01.00.10.0666670.1666670.30.3333330.3076660.1552270.278754...0.01.00.00.00.00.00.00.01.00.0
..................................................................
1440.00.00.00.2666670.1666670.30.0000000.5923010.3312210.638179...0.00.00.00.00.00.00.00.00.01.0
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149 rows × 40 columns

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" + ], + "text/plain": [ + " 共碳化物/煤沥青 加热次数 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 活化时间 比表面积 \\\n", + "0 0.0 0.0 0.1 0.066667 0.000000 0.3 0.333333 0.273437 \n", + "1 0.0 0.0 0.1 0.033333 0.166667 0.3 0.333333 0.287345 \n", + "2 0.0 0.0 0.1 0.066667 0.166667 0.3 0.333333 0.419103 \n", + "3 0.0 0.0 0.1 0.133333 0.166667 0.3 0.333333 0.436081 \n", + "4 0.0 1.0 0.1 0.066667 0.166667 0.3 0.333333 0.307666 \n", + ".. ... ... ... ... ... ... ... ... \n", + "144 0.0 0.0 0.0 0.266667 0.166667 0.3 0.000000 0.592301 \n", + "145 0.0 0.0 0.0 0.266667 0.333333 0.3 0.000000 0.843589 \n", + "146 0.0 0.0 0.0 0.266667 0.500000 0.3 0.000000 0.682631 \n", + "147 0.0 0.0 0.0 0.200000 0.333333 0.3 0.000000 0.569567 \n", + "148 0.0 0.0 0.0 0.333333 0.333333 0.3 0.000000 0.776902 \n", + "\n", + " 总孔体积 微孔体积 ... 模板剂种类_自制氧化镁 模板剂种类_自制碱式碳酸镁 模板剂种类_购买氢氧化镁 \\\n", + "0 0.137628 0.270767 ... 0.0 0.0 0.0 \n", + "1 0.229145 0.278754 ... 0.0 0.0 0.0 \n", + "2 0.211545 0.422524 ... 0.0 0.0 0.0 \n", + "3 0.204505 0.438498 ... 0.0 0.0 0.0 \n", + "4 0.155227 0.278754 ... 0.0 1.0 0.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0.331221 0.638179 ... 0.0 0.0 0.0 \n", + "145 0.472017 0.941693 ... 0.0 0.0 0.0 \n", + "146 0.376980 0.781949 ... 0.0 0.0 0.0 \n", + "147 0.292503 0.614217 ... 0.0 0.0 0.0 \n", + "148 0.394579 0.885783 ... 0.0 0.0 0.0 \n", + "\n", + " 模板剂种类_购买氧化钙 模板剂种类_购买氧化锌 模板剂种类_购买氧化镁 模板剂种类_购买氯化钠 模板剂种类_购买碳酸钙 混合方式_溶剂 \\\n", + "0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 1.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "145 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "146 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "147 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "148 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " 混合方式_研磨 \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + ".. ... \n", + "144 1.0 \n", + "145 1.0 \n", + "146 1.0 \n", + "147 1.0 \n", + "148 1.0 \n", + "\n", + "[149 rows x 40 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "70414dca-d785-4f70-9521-6e58221486be", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import optimizers\n", + "from tensorflow.python.keras.utils.vis_utils import plot_model\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7f985922-75d4-45f2-9603-4a38ca84f696", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import ReduceLROnPlateau\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "266cd0ae-5681-402b-a4f7-ef705e3ac0cb", + "metadata": {}, + "outputs": [], + "source": [ + "def print_eva(y_true, y_pred, tp):\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(MSE)\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n", + " print(f'RMSE: {round(RMSE, 3)}', end=',')\n", + " print(f'MAPE: {round(MAPE * 100, 3)} %', end=',')\n", + " print(f'MAE: {round(MAE, 3)}', end=',')\n", + " print(f'R_2: {round(R_2, 3)}')\n", + " return [MSE, RMSE, MAE, MAPE, R_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a1e4e900-b97d-4c52-88ad-439b80866f6b", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.losses import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c47baf5e-8557-46d7-b67d-85530baf1af0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "比表面积\n", + "1/1 [==============================] - 1s 583ms/step\n", + "COL: 比表面积, MSE: 5.79E+05,RMSE: 761.2230224609375,MAPE: 253.027 %,MAE: 653.417,R_2: 0.086\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[30], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m prediction_model \u001b[38;5;241m=\u001b[39m get_prediction_model()\n\u001b[1;32m 16\u001b[0m prediction_model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39mmean_squared_error)\n\u001b[0;32m---> 17\u001b[0m hist \u001b[38;5;241m=\u001b[39m \u001b[43mprediction_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m120\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_valid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_valid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mreduce_lr\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m rst \u001b[38;5;241m=\u001b[39m prediction_model\u001b[38;5;241m.\u001b[39mpredict(X_valid)\n\u001b[1;32m 22\u001b[0m pred_rst \u001b[38;5;241m=\u001b[39m rst \u001b[38;5;241m*\u001b[39m (maxs[pred_col] \u001b[38;5;241m-\u001b[39m mins[pred_col]) \u001b[38;5;241m+\u001b[39m mins[pred_col]\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/keras/src/engine/training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1736\u001b[0m epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1739\u001b[0m _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 1740\u001b[0m ):\n\u001b[1;32m 1741\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1742\u001b[0m tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m 1744\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 825\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 859\u001b[0m \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 860\u001b[0m \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m 861\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m 146\u001b[0m (concrete_function,\n\u001b[1;32m 147\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m 1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1347\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1351\u001b[0m args,\n\u001b[1;32m 1352\u001b[0m possible_gradient_type,\n\u001b[1;32m 1353\u001b[0m executing_eagerly)\n\u001b[1;32m 1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m 195\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 196\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[43m 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\u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m 1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m 1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1458\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1459\u001b[0m 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name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 1467\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1471\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m 1472\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "for pred_col in out_cols:\n", + " print(pred_col)\n", + " use_cols = feature_cols + [pred_col]\n", + " use_data = train_data[use_cols].dropna().reset_index(drop=True)\n", + " kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + " vad_eva_list = list()\n", + " for (train_index, test_index) in kf.split(use_data):\n", + " train = use_data.loc[train_index]\n", + " valid = use_data.loc[test_index]\n", + " X = np.expand_dims(train[feature_cols].values, axis=1)\n", + " Y = train[pred_col].values.T\n", + " X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n", + " Y_valid = valid[pred_col].values.T\n", + " prediction_model = get_prediction_model()\n", + " prediction_model.compile(optimizer='adam', loss=mean_squared_error)\n", + " hist = prediction_model.fit(X, Y, epochs=120, batch_size=8, verbose=0, \n", + " validation_data=(X_valid, Y_valid),\n", + " callbacks=[reduce_lr]\n", + " )\n", + " rst = prediction_model.predict(X_valid)\n", + " pred_rst = rst * (maxs[pred_col] - mins[pred_col]) + mins[pred_col]\n", + " real_rst = valid[pred_col] * (maxs[pred_col] - mins[pred_col]) + mins[pred_col]\n", + " y_pred_vad = pred_rst.reshape(-1,)\n", + " y_true_vad = real_rst.values.reshape(-1,)\n", + " vad_eva = print_eva(y_true_vad, y_pred_vad, tp=pred_col)\n", + " vad_eva_list.append(vad_eva)\n", + " del prediction_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", + "metadata": {}, + "outputs": [], + "source": [ + "vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", + "vad_df.sort_values(by='R_2').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MSE 0.317628\n", + "RMSE 0.557178\n", + "MAE 0.412263\n", + "MAPE 0.007993\n", + "R_2 0.986373\n", + "dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", + "fcad_df.sort_values(by='R_2').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=1, \n", + " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[0.73740077],\n", + " [0.89292204],\n", + " [0.7599046 ],\n", + " [0.67802393],\n", + " [0.6815233 ],\n", + " [0.88627005],\n", + " [0.6121343 ],\n", + " [0.7072234 ],\n", + " [0.8561135 ],\n", + " [0.52762157],\n", + " [0.8325021 ],\n", + " [0.50241977],\n", + " [0.8242289 ],\n", + " [0.68957335],\n", + " [0.6980361 ],\n", + " [0.82116604],\n", + " [0.8566438 ],\n", + " [0.53687835],\n", + " [0.56832707],\n", + " [0.78476715],\n", + " [0.85638577]], dtype=float32),\n", + " array([[0.68600863],\n", + " [0.78454906],\n", + " [0.8179163 ],\n", + " [0.94351083],\n", + " [0.86383885],\n", + " [0.69705516],\n", + " [0.6913491 ],\n", + " [0.80277354],\n", + " [0.93557894],\n", + " [0.82278305],\n", + " [0.82674253],\n", + " [0.93518937],\n", + " [0.8094449 ],\n", + " [0.9206344 ],\n", + " [0.7747319 ],\n", + " [0.9137207 ],\n", + " [0.9491073 ],\n", + " [0.93225 ],\n", + " [0.6185102 ],\n", + " [0.8867341 ],\n", + " [0.82890105]], dtype=float32)]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", + "rst" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.9991559102070927, 0.9998196796918477]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['挥发分Vad(%)', '固定炭Fcad(%)'], dtype='object')" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "294813b8-90be-4007-9fd6-c26ee7bb9652", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)\n", + "y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)\n", + "y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)\n", + "y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COL: 挥发分Vad, MSE: 3.35E-01,RMSE: 0.579,MAPE: 1.639 %,MAE: 0.504,R_2: 0.87\n", + "COL: 固定炭Fcad, MSE: 1.11E+00,RMSE: 1.055,MAPE: 1.497 %,MAE: 0.814,R_2: 0.876\n" + ] + } + ], + "source": [ + "pm25_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')\n", + "pm10_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac4a4339-ec7d-4266-8197-5276c2395288", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/20240123_煤沥青-checkpoint.ipynb b/.ipynb_checkpoints/20240123_煤沥青-checkpoint.ipynb new file mode 100644 index 0000000..100a20f --- /dev/null +++ b/.ipynb_checkpoints/20240123_煤沥青-checkpoint.ipynb @@ -0,0 +1,730 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4713d33-c5a2-4f49-8aed-873069543bec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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碳源共碳化物质共碳化物/煤沥青加热次数是否有碳化过程模板剂种类模板剂比例KOH与煤沥青比例活化温度升温速率活化时间混合方式比表面积总孔体积微孔体积平均孔径
0煤沥青无0.01否自制氧化钙1.01.050052.0溶剂908.070.400.341.75
1煤沥青无0.01否自制氧化钙1.00.560052.0溶剂953.950.660.352.76
2煤沥青无0.01否自制氧化钙1.01.060052.0溶剂1388.620.610.531.77
3煤沥青无0.01否自制氧化钙1.02.060052.0溶剂1444.630.590.551.62
4煤沥青无0.02是自制碱式碳酸镁1.01.060052.0溶剂1020.990.450.351.77
\n", + "
" + ], + "text/plain": [ + " 碳源 共碳化物质 共碳化物/煤沥青 加热次数 是否有碳化过程 模板剂种类 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 \\\n", + "0 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 500 5 \n", + "1 煤沥青 无 0.0 1 否 自制氧化钙 1.0 0.5 600 5 \n", + "2 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 600 5 \n", + "3 煤沥青 无 0.0 1 否 自制氧化钙 1.0 2.0 600 5 \n", + "4 煤沥青 无 0.0 2 是 自制碱式碳酸镁 1.0 1.0 600 5 \n", + "\n", + " 活化时间 混合方式 比表面积 总孔体积 微孔体积 平均孔径 \n", + "0 2.0 溶剂 908.07 0.40 0.34 1.75 \n", + "1 2.0 溶剂 953.95 0.66 0.35 2.76 \n", + "2 2.0 溶剂 1388.62 0.61 0.53 1.77 \n", + "3 2.0 溶剂 1444.63 0.59 0.55 1.62 \n", + "4 2.0 溶剂 1020.99 0.45 0.35 1.77 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤沥青数据.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1a0903a-596f-4d6f-98b1-a668a26f4175", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 16)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "359c9cc6-2694-46a6-9f18-6361e220542a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['碳源', '共碳化物质', '共碳化物/煤沥青', '加热次数', '是否有碳化过程', '模板剂种类', '模板剂比例',\n", + " 'KOH与煤沥青比例', '活化温度', '升温速率', '活化时间', '混合方式', '比表面积', '总孔体积', '微孔体积',\n", + " '平均孔径'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7ca6d610-060a-4540-bf1d-4f51cc2c55d1", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=['碳源'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['共碳化物质', '是否有碳化过程', '模板剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3368163e-85a1-4487-8078-be51cb5fb560", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积', '平均孔径']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e545ccba-07b2-4c49-bd48-f49b3892fafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 40)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=666)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "42597842-1acb-4263-bdad-bfca7b11bcb5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "params_xgb = {\"objective\": 'reg:squarederror',\n", + " \"subsample\": 0.9,\n", + " \"max_depth\": 20,\n", + " \"eta\": 0.01,\n", + " \"colsample_bytree\": 0.9,}\n", + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 146052.1781, RMSE: 382.1677, MAE: 335.8072, MAPE: 27.62 %, R_2: 0.3237\n", + "MSE: 457536.2053, RMSE: 676.4142, MAE: 524.2504, MAPE: 436.7 %, R_2: 0.4597\n", + "MSE: 426986.1964, RMSE: 653.4418, MAE: 517.7005, MAPE: 28.25 %, R_2: 0.1735\n", + "MSE: 276509.2691, RMSE: 525.8415, MAE: 387.3172, MAPE: 32.43 %, R_2: 0.4786\n", + "MSE: 300204.7099, RMSE: 547.9094, MAE: 395.1222, MAPE: 314.87 %, R_2: 0.3381\n", + "MSE: 243884.6623, RMSE: 493.8468, MAE: 382.9586, MAPE: 1077.01 %, R_2: 0.6543\n", + "MSE: 380516.2705, RMSE: 616.86, MAE: 528.3397, MAPE: 42.43 %, R_2: 0.294\n", + "MSE: 457352.6686, RMSE: 676.2785, MAE: 515.0433, MAPE: 547.78 %, R_2: 0.5355\n", + "MSE: 275148.3579, RMSE: 524.5459, MAE: 464.9701, MAPE: 48.3 %, R_2: 0.3033\n", + "MSE: 215299.6743, RMSE: 464.004, MAE: 385.4702, MAPE: 20.69 %, R_2: 0.4055\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0565, RMSE: 0.2377, MAE: 0.1623, MAPE: 30.73 %, R_2: 0.7346\n", + "MSE: 0.1443, RMSE: 0.3798, MAE: 0.2874, MAPE: 154.87 %, R_2: 0.801\n", + "MSE: 0.3168, RMSE: 0.5628, MAE: 0.4358, MAPE: 43.0 %, R_2: 0.4067\n", + "MSE: 0.1148, RMSE: 0.3389, MAE: 0.2794, MAPE: 29.47 %, R_2: 0.597\n", + "MSE: 0.1082, RMSE: 0.329, MAE: 0.2451, MAPE: 125.28 %, R_2: 0.3208\n", + "MSE: 0.0987, RMSE: 0.3141, MAE: 0.2595, MAPE: 338.62 %, R_2: 0.6563\n", + "MSE: 0.1457, RMSE: 0.3817, MAE: 0.2933, MAPE: 40.45 %, R_2: 0.3009\n", + "MSE: 0.1538, RMSE: 0.3922, MAE: 0.3011, MAPE: 441.83 %, R_2: 0.4244\n", + "MSE: 0.1302, RMSE: 0.3609, MAE: 0.2923, MAPE: 46.21 %, R_2: 0.1553\n", + "MSE: 0.0737, RMSE: 0.2715, MAE: 0.2209, MAPE: 22.16 %, R_2: 0.6708\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0334, RMSE: 0.1829, MAE: 0.1388, MAPE: 28.81 %, R_2: -0.1911\n", + "MSE: 0.0604, RMSE: 0.2457, MAE: 0.1958, MAPE: 1278.96 %, R_2: 0.6126\n", + "MSE: 0.0646, RMSE: 0.2542, MAE: 0.1992, MAPE: 41.7 %, R_2: 0.3841\n", + "MSE: 0.0459, RMSE: 0.2142, MAE: 0.153, MAPE: 38.47 %, R_2: 0.563\n", + "MSE: 0.0213, RMSE: 0.1459, MAE: 0.1258, MAPE: 216.58 %, R_2: 0.6774\n", + "MSE: 0.0332, RMSE: 0.1822, MAE: 0.1545, MAPE: 1355.85 %, R_2: 0.7458\n", + "MSE: 0.0534, RMSE: 0.231, MAE: 0.1976, MAPE: 63.72 %, R_2: 0.19\n", + "MSE: 0.0217, RMSE: 0.1474, MAE: 0.1131, MAPE: 1044.2 %, R_2: 0.7267\n", + "MSE: 0.09, RMSE: 0.3, MAE: 0.261, MAPE: 76.62 %, R_2: 0.058\n", + "MSE: 0.0616, RMSE: 0.2482, MAE: 0.1984, MAPE: 44.34 %, R_2: 0.3462\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.7362, RMSE: 0.858, MAE: 0.5955, MAPE: 23.28 %, R_2: 0.6539\n", + "MSE: 0.6157, RMSE: 0.7847, MAE: 0.4994, MAPE: 15.91 %, R_2: 0.3943\n", + "MSE: 0.2767, RMSE: 0.526, MAE: 0.4052, MAPE: 18.6 %, R_2: 0.6936\n", + "MSE: 0.5095, RMSE: 0.7138, MAE: 0.4774, MAPE: 16.58 %, R_2: 0.7721\n", + "MSE: 2.0145, RMSE: 1.4193, MAE: 0.9327, MAPE: 24.89 %, R_2: 0.0073\n", + "MSE: 1.4449, RMSE: 1.202, MAE: 0.5396, MAPE: 13.41 %, R_2: 0.2873\n", + "MSE: 0.4101, RMSE: 0.6404, MAE: 0.4024, MAPE: 16.32 %, R_2: -1.8128\n", + "MSE: 1.4384, RMSE: 1.1993, MAE: 0.8923, MAPE: 39.29 %, R_2: -0.313\n", + "MSE: 0.3805, RMSE: 0.6168, MAE: 0.4343, MAPE: 16.0 %, R_2: 0.6971\n", + "MSE: 0.0937, RMSE: 0.3061, MAE: 0.2214, MAPE: 9.25 %, R_2: 0.9435\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eva_total = list()\n", + "index_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for col in out_cols:\n", + " eva_list = list()\n", + " train_data = train_data[~train_data[col].isna()].reset_index(drop=True)\n", + " cur_test = list()\n", + " cur_real = list()\n", + " for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[col]\n", + " X_valid, Y_valid = valid[feature_cols], valid[col]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " cur_test.extend(y_pred[:7])\n", + " cur_real.extend(y_true[:7])\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n", + " plt.figure(figsize=(12, 8))\n", + " plt.plot(range(len(cur_test)), cur_real, 'o-', label='real')\n", + " plt.plot(range(len(cur_test)), cur_test, '*-', label='pred')\n", + " plt.legend(loc='best')\n", + " plt.title(f'{col}')\n", + " plt.show()\n", + " eva_total.append(np.mean(eva_list, axis=0))\n", + " index_list.append(f\"{col}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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比表面积315103.064636547.014045398.0156470.9769550.466536
总孔体积0.1279850.3537570.2686681.1792380.470801
微孔体积0.0446550.2075950.1649634.5485830.536350
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灰分(d)挥发分(daf）活化剂种类活化剂比例混合方式活化温度活化时间升温速率比表面积总孔体积微孔体积Unnamed: 11
011.2517.06KOH3.0研磨8001.05.02784.01.08300.853刘宇昊\\n煤基活性炭的制备及其电化学性能研究 学位论文
18.5313.46KOH3.0研磨8001.05.02934.01.22901.074NaN
218.0813.85KOH3.0研磨8001.05.03059.01.30441.011NaN
311.4212.31KOH3.0研磨8001.05.02365.00.80300.605NaN
411.608.49KOH3.0研磨8001.05.02988.01.28200.944NaN
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" + ], + "text/plain": [ + " 灰分(d) 挥发分(daf） 活化剂种类 活化剂比例 混合方式 活化温度 活化时间 升温速率 比表面积 总孔体积 微孔体积 \\\n", + "0 11.25 17.06 KOH 3.0 研磨 800 1.0 5.0 2784.0 1.0830 0.853 \n", + "1 8.53 13.46 KOH 3.0 研磨 800 1.0 5.0 2934.0 1.2290 1.074 \n", + "2 18.08 13.85 KOH 3.0 研磨 800 1.0 5.0 3059.0 1.3044 1.011 \n", + "3 11.42 12.31 KOH 3.0 研磨 800 1.0 5.0 2365.0 0.8030 0.605 \n", + "4 11.60 8.49 KOH 3.0 研磨 800 1.0 5.0 2988.0 1.2820 0.944 \n", + "\n", + " Unnamed: 11 \n", + "0 刘宇昊\\n煤基活性炭的制备及其电化学性能研究 学位论文 \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤炭数据.xlsx', header=[1])\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1a0903a-596f-4d6f-98b1-a668a26f4175", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=data.columns[-1], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "359c9cc6-2694-46a6-9f18-6361e220542a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['灰分(d)', '挥发分(daf）', '活化剂种类', '活化剂比例', '混合方式', '活化温度', '活化时间', '升温速率',\n", + " '比表面积', '总孔体积', '微孔体积'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['活化剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3368163e-85a1-4487-8078-be51cb5fb560", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e545ccba-07b2-4c49-bd48-f49b3892fafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(174, 12)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=6, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "42597842-1acb-4263-bdad-bfca7b11bcb5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "params_xgb = {\"objective\": 'reg:squarederror',\n", + " \"subsample\": 0.8,\n", + " \"max_depth\": 20,\n", + " \"eta\": 0.01,\n", + " \"colsample_bytree\": 0.9,}\n", + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 159642.0815, RMSE: 399.5524, MAE: 274.5969, MAPE: 24.34 %, R_2: 0.7942\n", + "MSE: 193553.7041, RMSE: 439.9474, MAE: 328.694, MAPE: 35.35 %, R_2: 0.814\n", + "MSE: 220477.246, RMSE: 469.55, MAE: 306.6159, MAPE: 22.42 %, R_2: 0.7381\n", + "MSE: 290738.4856, RMSE: 539.2017, MAE: 385.6497, MAPE: 63.87 %, R_2: 0.6771\n", + "MSE: 198924.8773, RMSE: 446.01, MAE: 301.0048, MAPE: 31.69 %, R_2: 0.5602\n", + "MSE: 129420.5336, RMSE: 359.7507, MAE: 268.3835, MAPE: 38.83 %, R_2: 0.8213\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0829, RMSE: 0.2879, MAE: 0.193, MAPE: 55.19 %, R_2: 0.7327\n", + "MSE: 0.0702, RMSE: 0.2649, MAE: 0.1854, MAPE: 20.28 %, R_2: 0.8429\n", + "MSE: 0.0386, RMSE: 0.1964, MAE: 0.1393, MAPE: 19.43 %, R_2: 0.8072\n", + "MSE: 0.0497, RMSE: 0.2229, MAE: 0.1722, MAPE: 26.26 %, R_2: 0.8521\n", + "MSE: 0.0634, RMSE: 0.2519, MAE: 0.1608, MAPE: 37.87 %, R_2: 0.4996\n", + "MSE: 0.0488, RMSE: 0.2209, MAE: 0.1573, MAPE: 56.68 %, R_2: 0.8303\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0212, RMSE: 0.1456, MAE: 0.1027, MAPE: 19.78 %, R_2: 0.6947\n", + "MSE: 0.0632, RMSE: 0.2514, MAE: 0.1479, MAPE: 22.03 %, R_2: 0.6062\n", + "MSE: 0.0898, RMSE: 0.2997, MAE: 0.1758, MAPE: 27.82 %, R_2: 0.3597\n", + "MSE: 0.0234, RMSE: 0.153, MAE: 0.1014, MAPE: 21.71 %, R_2: 0.499\n", + "MSE: 0.0434, RMSE: 0.2083, MAE: 0.1318, MAPE: 46.08 %, R_2: 0.5236\n", + "MSE: 0.056, RMSE: 0.2366, MAE: 0.1601, MAPE: 23.9 %, R_2: 0.2317\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eva_total = list()\n", + "index_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for col in out_cols:\n", + " eva_list = list()\n", + " train_data = train_data[~train_data[col].isna()].reset_index(drop=True)\n", + " cur_test = list()\n", + " cur_real = list()\n", + " for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[col]\n", + " X_valid, Y_valid = valid[feature_cols], valid[col]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " cur_test.extend(y_pred[:7])\n", + " cur_real.extend(y_true[:7])\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n", + " plt.figure(figsize=(12, 8))\n", + " plt.plot(range(len(cur_test)), cur_real, 'o-', label='real')\n", + " plt.plot(range(len(cur_test)), cur_test, '*-', label='pred')\n", + " plt.legend(loc='best')\n", + " plt.title(f'{col}')\n", + " plt.show()\n", + " eva_total.append(np.mean(eva_list, axis=0))\n", + " index_list.append(f\"{col}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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比表面积198792.821359442.335351310.8241340.3608280.734146
总孔体积0.0589310.2408250.1679950.3594980.760793
微孔体积0.0494970.2157640.1366190.2688890.485802
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" + ], + "text/plain": [ + " MSE RMSE MAE MAPE R2\n", + "比表面积 198792.821359 442.335351 310.824134 0.360828 0.734146\n", + "总孔体积 0.058931 0.240825 0.167995 0.359498 0.760793\n", + "微孔体积 0.049497 0.215764 0.136619 0.268889 0.485802" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame.from_records(eva_total, index=index_list, columns=eva_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python38", + "language": "python", + "name": "python38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/20240617_电容器-checkpoint.ipynb b/.ipynb_checkpoints/20240617_电容器-checkpoint.ipynb new file mode 100644 index 0000000..4a0c9ce --- /dev/null +++ b/.ipynb_checkpoints/20240617_电容器-checkpoint.ipynb @@ -0,0 +1,464 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "077f5f8a-ffe5-4405-8806-1b5559140a5d", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install pandas hyperopt xgboost scikit-learn matplotlib numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4713d33-c5a2-4f49-8aed-873069543bec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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比表面积总孔体积微孔体积平均孔径氮掺杂量at氧掺杂量ID/IG电流密度比电容
01141.80.460.421.611.743.841.10.5206.5
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" + ], + "text/plain": [ + " 比表面积 总孔体积 微孔体积 平均孔径 氮掺杂量at 氧掺杂量 ID/IG 电流密度 比电容\n", + "0 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 0.5 206.5\n", + "1 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 1.0 179.1\n", + "2 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 2.0 163.3\n", + "3 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 5.0 146.0\n", + "4 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 10.0 137.8" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240617/电容性能新.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比电容']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a140942f-4206-49e5-a51b-932c55170436", + "metadata": {}, + "outputs": [], + "source": [ + "# 定义超参数的搜索空间\n", + "space = {\n", + " 'eta': hp.loguniform('eta', -5, 0), # 学习率，搜索范围是 [1e-5, 1]\n", + " 'max_depth': hp.choice('max_depth', range(5, 30)), # 树的最大深度，搜索范围是 [1, 10]\n", + " 'min_child_weight': hp.uniform('min_child_weight', 0, 10), # 子节点最小的权重和\n", + " 'gamma': hp.loguniform('gamma', -5, 0), # 叶子节点分裂所需的最小损失减少\n", + " 'subsample': hp.uniform('subsample', 0.5, 1), # 训练集的采样率\n", + " 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1), # 特征的采样率\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7dd2dc64-0e80-4504-b84a-1d549f2cf90d", + "metadata": {}, + "outputs": [], + "source": [ + "# 划分训练集和测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], \n", + " train_data[out_cols], \n", + " test_size=0.3, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "68669fde-52ab-4e62-8efc-b60dcf13734b", + "metadata": {}, + "outputs": [], + "source": [ + "# 定义目标函数，用于评估模型的性能\n", + "def objective(params):\n", + " # 创建决策树分类器实例\n", + " gbr = xgb.XGBRegressor(**params)\n", + " # 训练模型\n", + " gbr.fit(X_train, y_train)\n", + " # 使用模型进行预测\n", + " y_pred = gbr.predict(X_test)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " return {'loss': mae, 'status': STATUS_OK}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e89097ea-fee2-4298-81a2-ff528688857e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.55trial/s, best loss: 12.132344347686164]\n" + ] + } + ], + "source": [ + "# 创建 Trials 对象来记录搜索历史\n", + "trials = Trials()\n", + "\n", + "# 使用 fmin 函数进行超参数优化\n", + "best_params = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0ccfb873-6f5a-4606-9b17-a63cdbcf8acc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'colsample_bytree': 0.8857035476046763, 'eta': 0.11588664776521924, 'gamma': 0.007847746718601799, 'max_depth': 10, 'min_child_weight': 6.396614191886977, 'subsample': 0.7070880429614513}\n" + ] + } + ], + "source": [ + "print(best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=10, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 164.2816, RMSE: 12.8172, MAE: 9.1819, MAPE: 4.33 %, R_2: 0.9534\n", + "MSE: 172.8146, RMSE: 13.1459, MAE: 8.4597, MAPE: 4.24 %, R_2: 0.9475\n", + "MSE: 105.637, RMSE: 10.278, MAE: 7.1138, MAPE: 3.19 %, R_2: 0.9736\n", + "MSE: 306.2548, RMSE: 17.5001, MAE: 10.3353, MAPE: 4.27 %, R_2: 0.9348\n", + "MSE: 212.1827, RMSE: 14.5665, MAE: 10.452, MAPE: 4.64 %, R_2: 0.9467\n", + "MSE: 311.2193, RMSE: 17.6414, MAE: 10.62, MAPE: 3.97 %, R_2: 0.929\n", + "MSE: 479.0079, RMSE: 21.8862, MAE: 11.6752, MAPE: 5.11 %, R_2: 0.8952\n", + "MSE: 153.6563, RMSE: 12.3958, MAE: 8.8708, MAPE: 4.44 %, R_2: 0.9502\n", + "MSE: 285.905, RMSE: 16.9087, MAE: 10.4152, MAPE: 5.35 %, R_2: 0.9522\n", + "MSE: 570.9538, RMSE: 23.8946, MAE: 12.4216, MAPE: 5.98 %, R_2: 0.8954\n" + ] + } + ], + "source": [ + "eva_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[out_cols]\n", + " X_valid, Y_valid = valid[feature_cols], valid[out_cols]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(best_params, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [], + "source": [ + "eva_df = pd.DataFrame.from_records(eva_list, columns=eva_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6c7c4910-81a2-4703-948a-152ccc7b859d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MSE 276.191297\n", + "RMSE 16.103459\n", + "MAE 9.954548\n", + "MAPE 0.045525\n", + "R2 0.937810\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eva_df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.plot(range(len(y_true)), y_true, 'o-', label='real')\n", + "plt.plot(range(len(y_pred)), y_pred, '*-', label='pred')\n", + "plt.legend(loc='best')\n", + "plt.title(f'{out_cols}')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b69f612e-3548-41d6-9512-7591c47ca50e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/CBA_4feature-checkpoint.ipynb b/.ipynb_checkpoints/CBA_4feature-checkpoint.ipynb index 43d01bb..e078751 100644 --- a/.ipynb_checkpoints/CBA_4feature-checkpoint.ipynb +++ b/.ipynb_checkpoints/CBA_4feature-checkpoint.ipynb @@ -1432,7 +1432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/CBA_vad_fcad-checkpoint.ipynb b/.ipynb_checkpoints/CBA_vad_fcad-checkpoint.ipynb index 872ea5c..f6ca139 100644 --- a/.ipynb_checkpoints/CBA_vad_fcad-checkpoint.ipynb +++ b/.ipynb_checkpoints/CBA_vad_fcad-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "6b84fefd-5936-4da4-ab6b-5b944329ad1d", "metadata": {}, "outputs": [], @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", "metadata": {}, "outputs": [ @@ -236,7 +236,7 @@ "[228 rows x 8 columns]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "972f1e9c-3ebc-45cf-8d1f-7611645e5238", "metadata": {}, "outputs": [ @@ -265,7 +265,7 @@ " '固定炭Fcad(%)']" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "c95f1106-b3a4-43c6-88ec-3cdebf91d79a", "metadata": {}, "outputs": [], @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "2e96af0a-feda-4a1f-a13e-9c8861c6f4d4", "metadata": {}, "outputs": [ @@ -479,7 +479,7 @@ "[228 rows x 8 columns]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -490,18 +490,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", "metadata": {}, "outputs": [], "source": [ - "out_cols = ['挥发分Vad(%)']\n", - "# out_cols = ['固定炭Fcad(%)']" + "# out_cols = ['挥发分Vad(%)']\n", + "# drop_cols = ['化验编号', '固定炭Fcad(%)']\n", + "out_cols = ['固定炭Fcad(%)']\n", + "drop_cols = ['挥发分Vad(%)', '化验编号']" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", "metadata": {}, "outputs": [ @@ -511,7 +513,7 @@ "['挥发分Vad(%)']" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "feaedd50-f999-45bf-b465-3d359b0c0110", "metadata": {}, "outputs": [], @@ -532,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", "metadata": {}, "outputs": [], @@ -542,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", "metadata": {}, "outputs": [ @@ -550,7 +552,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 17:02:16.953831: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-01-08 18:03:14.359273: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], @@ -563,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", "metadata": {}, "outputs": [], @@ -591,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", "metadata": {}, "outputs": [], @@ -601,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", "metadata": {}, "outputs": [], @@ -611,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", "metadata": {}, "outputs": [], @@ -651,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "a190207e-5a59-4813-9660-758760cf1b73", "metadata": {}, "outputs": [], @@ -661,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 18, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], @@ -709,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", "metadata": {}, "outputs": [ @@ -781,7 +783,7 @@ "1 0.674897 0.794606 " ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -792,19 +794,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", "metadata": {}, "outputs": [], "source": [ "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", - "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols and x not in drop_cols]\n", "use_cols = feature_cols + out_cols" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", "metadata": {}, "outputs": [], @@ -816,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "50daf170-efec-49e5-8f8e-9a45938cacfc", "metadata": {}, "outputs": [], @@ -827,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "0f863423-be12-478b-a08d-e3c6f5dfb8ee", "metadata": {}, "outputs": [], @@ -839,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "id": "2c89b32a-017c-4d05-ab78-8b9b8eb0dcbb", "metadata": {}, "outputs": [], @@ -850,34 +852,49 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 27, "id": "ae24eea7-7dc1-4e33-9d41-3baff07ebb88", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:03:45.062553: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-08 18:03:45.094631: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-08 18:03:45.094657: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-08 18:03:45.094661: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-08 18:03:45.094825: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-08 18:03:45.094846: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-08 18:03:45.094849: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n", + "2024-01-08 18:03:45.095157: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"model_2\"\n", + "Model: \"model\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "input (InputLayer) [(None, 1, 7)] 0 \n", + "input (InputLayer) [(None, 1, 5)] 0 \n", "_________________________________________________________________\n", - "conv1d_3 (Conv1D) (None, 1, 64) 512 \n", + "conv1d (Conv1D) (None, 1, 64) 384 \n", "_________________________________________________________________\n", - "bidirectional_3 (Bidirection (None, 1, 128) 66048 \n", + "bidirectional (Bidirectional (None, 1, 128) 66048 \n", "_________________________________________________________________\n", - "dense_5 (Dense) (None, 1, 128) 16512 \n", + "dense (Dense) (None, 1, 128) 16512 \n", "_________________________________________________________________\n", - "dropout_3 (Dropout) (None, 1, 128) 0 \n", + "dropout (Dropout) (None, 1, 128) 0 \n", "_________________________________________________________________\n", - "dense_6 (Dense) (None, 1, 64) 8256 \n", + "dense_1 (Dense) (None, 1, 64) 8256 \n", "_________________________________________________________________\n", "vad (Dense) (None, 1, 1) 65 \n", "=================================================================\n", - "Total params: 91,393\n", - "Trainable params: 91,393\n", + "Total params: 91,265\n", + "Trainable params: 91,265\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -890,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "id": "ca6ce434-80b6-4609-9596-9a5120680462", "metadata": {}, "outputs": [], @@ -911,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "id": "503bbec7-2020-44c8-b622-05bb41082e43", "metadata": {}, "outputs": [], @@ -921,17 +938,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:03:50.956250: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-01-08 18:03:50.974801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "COL: 挥发分Vad, MSE: 2.49E-01,RMSE: 0.499,MAPE: 1.336 %,MAE: 0.398,R_2: 0.946\n", - "COL: 挥发分Vad, MSE: 3.81E-01,RMSE: 0.617,MAPE: 1.597 %,MAE: 0.455,R_2: 0.954\n", - "COL: 挥发分Vad, MSE: 5.71E-01,RMSE: 0.756,MAPE: 2.077 %,MAE: 0.621,R_2: 0.854\n" + "COL: 挥发分Vad, MSE: 5.84E-01,RMSE: 0.764,MAPE: 2.111 %,MAE: 0.633,R_2: 0.874\n", + "COL: 挥发分Vad, MSE: 1.06E+00,RMSE: 1.028,MAPE: 2.941 %,MAE: 0.869,R_2: 0.872\n", + "COL: 挥发分Vad, MSE: 6.70E-01,RMSE: 0.819,MAPE: 2.217 %,MAE: 0.658,R_2: 0.829\n", + "COL: 挥发分Vad, MSE: 5.96E-01,RMSE: 0.772,MAPE: 2.07 %,MAE: 0.607,R_2: 0.896\n", + "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7f6e8d6f8940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 挥发分Vad, MSE: 8.56E-01,RMSE: 0.925,MAPE: 2.335 %,MAE: 0.717,R_2: 0.805\n", + "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7f6e8f6e4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 挥发分Vad, MSE: 7.24E-01,RMSE: 0.851,MAPE: 2.435 %,MAE: 0.713,R_2: 0.851\n" ] } ], @@ -971,20 +1001,22 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 31, "id": "f7132465-89e9-4193-829b-c6e7606cd266", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "COL: 固定炭Fcad, MSE: 2.10E-01,RMSE: 0.458,MAPE: 0.687 %,MAE: 0.361,R_2: 0.992\n", - "COL: 固定炭Fcad, MSE: 3.45E-01,RMSE: 0.587,MAPE: 0.865 %,MAE: 0.404,R_2: 0.993\n", - "COL: 固定炭Fcad, MSE: 3.77E-01,RMSE: 0.614,MAPE: 0.837 %,MAE: 0.465,R_2: 0.973\n", - "COL: 固定炭Fcad, MSE: 2.15E-01,RMSE: 0.463,MAPE: 0.693 %,MAE: 0.35,R_2: 0.994\n", - "COL: 固定炭Fcad, MSE: 2.75E-01,RMSE: 0.525,MAPE: 0.746 %,MAE: 0.41,R_2: 0.987\n", - "COL: 固定炭Fcad, MSE: 4.84E-01,RMSE: 0.696,MAPE: 0.968 %,MAE: 0.483,R_2: 0.979\n" + "ename": "KeyError", + "evalue": "\"None of [Index(['固定炭Fcad(%)'], dtype='object')] are in the [columns]\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[31], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m valid \u001b[38;5;241m=\u001b[39m train_data\u001b[38;5;241m.\u001b[39mloc[test_index]\n\u001b[1;32m 7\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexpand_dims(train[feature_cols]\u001b[38;5;241m.\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 8\u001b[0m Y \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtrain\u001b[49m\u001b[43m[\u001b[49m\u001b[43mout_cols\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mT]\n\u001b[1;32m 9\u001b[0m X_valid \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mexpand_dims(valid[feature_cols]\u001b[38;5;241m.\u001b[39mvalues, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 10\u001b[0m Y_valid \u001b[38;5;241m=\u001b[39m [x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m valid[out_cols]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mT]\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/frame.py:3030\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3028\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[1;32m 3029\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[0;32m-> 3030\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_listlike_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 3032\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[1;32m 3033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/indexing.py:1265\u001b[0m, in \u001b[0;36m_LocIndexer._get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1262\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1263\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[0;32m-> 1265\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_read_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_missing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraise_missing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1266\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m keyarr, indexer\n", + "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/pandas/core/indexing.py:1307\u001b[0m, in \u001b[0;36m_LocIndexer._validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m 1305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[1;32m 1306\u001b[0m axis_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis_name(axis)\n\u001b[0;32m-> 1307\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1309\u001b[0m ax \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m 1311\u001b[0m \u001b[38;5;66;03m# We (temporarily) allow for some missing keys with .loc, except in\u001b[39;00m\n\u001b[1;32m 1312\u001b[0m \u001b[38;5;66;03m# some cases (e.g. setting) in which \"raise_missing\" will be False\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['固定炭Fcad(%)'], dtype='object')] are in the [columns]\"" ] } ], @@ -1020,22 +1052,22 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 32, "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MSE 0.394351\n", - "RMSE 0.625663\n", - "MAE 0.507130\n", - "MAPE 0.017249\n", - "R_2 0.920159\n", + "MSE 0.747816\n", + "RMSE 0.859839\n", + "MAE 0.699474\n", + "MAPE 0.023513\n", + "R_2 0.854338\n", "dtype: float64" ] }, - "execution_count": 51, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1047,26 +1079,10 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MSE 0.317628\n", - "RMSE 0.557178\n", - "MAE 0.412263\n", - "MAPE 0.007993\n", - "R_2 0.986373\n", - "dtype: float64" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", "fcad_df.sort_values(by='R_2').mean()" diff --git a/.ipynb_checkpoints/TEST-checkpoint.csv b/.ipynb_checkpoints/TEST-checkpoint.csv new file mode 100644 index 0000000..c3bffe2 --- /dev/null +++ b/.ipynb_checkpoints/TEST-checkpoint.csv @@ -0,0 +1,150 @@ +,共碳化物/煤沥青,加热次数,模板剂比例,KOH与煤沥青比例,活化温度,升温速率,活化时间,共碳化物质_2-甲基咪唑,共碳化物质_三聚氰胺,共碳化物质_尿素,共碳化物质_无,共碳化物质_硫酸铵,共碳化物质_聚磷酸铵,是否有碳化过程_否,是否有碳化过程_是,模板剂种类_Al2O3,模板剂种类_TiO2,模板剂种类_α-Fe2O3,模板剂种类_γ-Fe2O3,模板剂种类_二氧化硅,模板剂种类_无,模板剂种类_氯化钾,模板剂种类_纤维素,模板剂种类_自制氢氧化镁,模板剂种类_自制氧化钙,模板剂种类_自制氧化锌,模板剂种类_自制氧化镁,模板剂种类_自制碱式碳酸镁,模板剂种类_购买氢氧化镁,模板剂种类_购买氧化钙,模板剂种类_购买氧化锌,模板剂种类_购买氧化镁,模板剂种类_购买氯化钠,模板剂种类_购买碳酸钙,混合方式_溶剂,混合方式_研磨,比表面积 +0,0.0,0.0,0.1,0.06666667,0.0,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2734374 +1,0.0,0.0,0.1,0.033333335,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.28734466 +2,0.0,0.0,0.1,0.06666667,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.41910276 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'PCB_BUS_ID'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "import matplotlib.pyplot as plt\n", + "#新增加的两行\n", + "from pylab import mpl\n", + "# 设置显示中文字体\n", + "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n", + "\n", + "mpl.rcParams[\"axes.unicode_minus\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('./data/20240102/train_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = [x for x in data.columns if '碳材料' in x]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.dropna(subset=out_cols).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-04 16:22:35.199530: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow.keras.backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2318ce6-60d2-495c-91cd-67ca53609cf8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /tmp/ipykernel_44444/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.config.list_physical_devices('GPU')` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-04 16:22:36.097926: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-01-04 16:22:36.142225: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-04 16:22:36.232036: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-04 16:22:36.232061: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-04 16:22:36.232065: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-04 16:22:36.232185: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-04 16:22:36.232204: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-04 16:22:36.232207: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.test.is_gpu_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", + "metadata": {}, + "outputs": [], + "source": [ + "class TransformerBlock(layers.Layer):\n", + " def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1):\n", + " super().__init__()\n", + " self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name)\n", + " self.ffn = keras.Sequential(\n", + " [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n", + " )\n", + " self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n", + " self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n", + " self.dropout1 = layers.Dropout(rate)\n", + " self.dropout2 = layers.Dropout(rate)\n", + "\n", + " def call(self, inputs, training):\n", + " attn_output = self.att(inputs, inputs)\n", + " attn_output = self.dropout1(attn_output, training=training)\n", + " out1 = self.layernorm1(inputs + attn_output)\n", + " ffn_output = self.ffn(out1)\n", + " ffn_output = self.dropout2(ffn_output, training=training)\n", + " return self.layernorm2(out1 + ffn_output)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.initializers import Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", + "metadata": {}, + "outputs": [], + "source": [ + "# Custom loss layer\n", + "class CustomMultiLossLayer(layers.Layer):\n", + " def __init__(self, nb_outputs=2, **kwargs):\n", + " self.nb_outputs = nb_outputs\n", + " self.is_placeholder = True\n", + " super(CustomMultiLossLayer, self).__init__(**kwargs)\n", + " \n", + " def build(self, input_shape=None):\n", + " # initialise log_vars\n", + " self.log_vars = []\n", + " for i in range(self.nb_outputs):\n", + " self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,),\n", + " initializer=tf.initializers.he_normal(), trainable=True)]\n", + " super(CustomMultiLossLayer, self).build(input_shape)\n", + "\n", + " def multi_loss(self, ys_true, ys_pred):\n", + " assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs\n", + " loss = 0\n", + " for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars):\n", + " mse = (y_true - y_pred) ** 2.\n", + " pre = K.exp(-log_var[0])\n", + " loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1))\n", + " return K.mean(loss)\n", + "\n", + " def call(self, inputs):\n", + " ys_true = inputs[:self.nb_outputs]\n", + " ys_pred = inputs[self.nb_outputs:]\n", + " loss = self.multi_loss(ys_true, ys_pred)\n", + " self.add_loss(loss, inputs=inputs)\n", + " # We won't actually use the output.\n", + " return K.concatenate(inputs, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a190207e-5a59-4813-9660-758760cf1b73", + "metadata": {}, + "outputs": [], + "source": [ + "num_heads, ff_dim = 1, 12" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", + "metadata": {}, + "outputs": [], + "source": [ + "def get_prediction_model():\n", + " def build_output(out, out_name):\n", + " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " out = self_block(out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(32, activation=\"relu\")(out)\n", + " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", + " return out\n", + " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", + " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", + " # x = layers.Dropout(rate=0.1)(x)\n", + " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", + " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", + " out = transformer_block(lstm_out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(64, activation='relu')(out)\n", + " out = K.expand_dims(out, axis=1)\n", + "\n", + " bet = build_output(out, 'bet')\n", + " mesco = build_output(out, 'mesco')\n", + " micro = build_output(out, 'micro')\n", + " avg = build_output(out, 'avg')\n", + "\n", + " bet = layers.Dense(1, activation='sigmoid', name='bet')(bet)\n", + " mesco = layers.Dense(1, activation='sigmoid', name='mesco')(mesco)\n", + " micro = layers.Dense(1, activation='sigmoid', name='micro')(micro)\n", + " avg = layers.Dense(1, activation='sigmoid', name='avg')(avg)\n", + "\n", + " model = Model(inputs=[inputs], outputs=[bet, mesco, micro, avg])\n", + " return model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "264001b1-5e4a-4786-96fd-2b5c70ab3212", + "metadata": {}, + "outputs": [], + "source": [ + "def get_trainable_model(prediction_model):\n", + " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", + " bet, mesco, micro, avg = prediction_model(inputs)\n", + " bet_real = layers.Input(shape=(1,), name='bet_real')\n", + " mesco_real = layers.Input(shape=(1,), name='mesco_real')\n", + " micro_real = layers.Input(shape=(1,), name='micro_real')\n", + " avg_real = layers.Input(shape=(1,), name='avg_real')\n", + " out = CustomMultiLossLayer(nb_outputs=4)([bet_real, mesco_real, micro_real, avg_real, bet, mesco, micro, avg])\n", + " return Model([inputs, bet_real, mesco_real, micro_real, avg_real], out)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", + "metadata": {}, + "outputs": [], + "source": [ + "maxs = train_data.max()\n", + "mins = train_data.min()\n", + "for col in train_data.columns:\n", + " if maxs[col] - mins[col] == 0:\n", + " continue\n", + " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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热处理条件-热处理次数热处理条件-是否是中温停留第一次热处理-温度第一次热处理-升温速率第一次热处理-保留时间第二次热处理-温度第二次热处理-升温速率·第二次热处理-保留时间共碳化-是否是共碳化物质共碳化-共碳化物质/沥青...模板剂-种类_二氧化硅模板剂-种类_氢氧化镁模板剂-种类_氧化钙模板剂-种类_氧化锌模板剂-种类_氧化镁模板剂-种类_氯化钠模板剂-种类_氯化钾模板剂-种类_碱式碳酸镁模板剂-种类_碳酸钙模板剂-种类_纤维素
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41.00.00.1666670.30.50.6666670.50.6666670.00.0...00.00.000.00.001.00.00.0
..................................................................
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1470.00.00.5000000.30.00.0000000.00.0000000.00.0...00.00.000.00.000.00.00.0
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" + ], + "text/plain": [ + " 热处理条件-热处理次数 热处理条件-是否是中温停留 第一次热处理-温度 第一次热处理-升温速率 第一次热处理-保留时间 \\\n", + "0 0.0 0.0 0.166667 0.3 0.5 \n", + "1 0.0 0.0 0.333333 0.3 0.5 \n", + "2 0.0 0.0 0.333333 0.3 0.5 \n", + "3 0.0 0.0 0.333333 0.3 0.5 \n", + "4 1.0 0.0 0.166667 0.3 0.5 \n", + ".. ... ... ... ... ... \n", + "144 0.0 0.0 0.333333 0.3 0.0 \n", + "145 0.0 0.0 0.500000 0.3 0.0 \n", + "146 0.0 0.0 0.666667 0.3 0.0 \n", + "147 0.0 0.0 0.500000 0.3 0.0 \n", + "148 0.0 0.0 0.500000 0.3 0.0 \n", + "\n", + " 第二次热处理-温度 第二次热处理-升温速率· 第二次热处理-保留时间 共碳化-是否是共碳化物质 共碳化-共碳化物质/沥青 ... \\\n", + "0 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "1 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "2 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "3 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "4 0.666667 0.5 0.666667 0.0 0.0 ... \n", + ".. ... ... ... ... ... ... \n", + "144 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "145 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "146 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "147 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "148 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "\n", + " 模板剂-种类_二氧化硅 模板剂-种类_氢氧化镁 模板剂-种类_氧化钙 模板剂-种类_氧化锌 模板剂-种类_氧化镁 模板剂-种类_氯化钠 \\\n", + "0 0 0.0 1.0 0 0.0 0.0 \n", + "1 0 0.0 1.0 0 0.0 0.0 \n", + "2 0 0.0 1.0 0 0.0 0.0 \n", + "3 0 0.0 1.0 0 0.0 0.0 \n", + "4 0 0.0 0.0 0 0.0 0.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0 0.0 0.0 0 0.0 0.0 \n", + "145 0 0.0 0.0 0 0.0 0.0 \n", + "146 0 0.0 0.0 0 0.0 0.0 \n", + "147 0 0.0 0.0 0 0.0 0.0 \n", + "148 0 0.0 0.0 0 0.0 0.0 \n", + "\n", + " 模板剂-种类_氯化钾 模板剂-种类_碱式碳酸镁 模板剂-种类_碳酸钙 模板剂-种类_纤维素 \n", + "0 0 0.0 0.0 0.0 \n", + "1 0 0.0 0.0 0.0 \n", + "2 0 0.0 0.0 0.0 \n", + "3 0 0.0 0.0 0.0 \n", + "4 0 1.0 0.0 0.0 \n", + ".. ... ... ... ... \n", + "144 0 0.0 0.0 0.0 \n", + "145 0 0.0 0.0 0.0 \n", + "146 0 0.0 0.0 0.0 \n", + "147 0 0.0 0.0 0.0 \n", + "148 0 0.0 0.0 0.0 \n", + "\n", + "[123 rows x 42 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", + "metadata": {}, + "outputs": [], + "source": [ + "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "use_cols = feature_cols + out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", + "metadata": {}, + "outputs": [], + "source": [ + "use_data = train_data.copy()\n", + "for col in use_cols:\n", + " use_data[col] = use_data[col].astype('float32')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input (InputLayer) [(None, 1, 38)] 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d (Conv1D) (None, 1, 64) 2496 input[0][0] \n", + "__________________________________________________________________________________________________\n", + "bidirectional (Bidirectional) (None, 1, 128) 66048 conv1d[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense (Dense) (None, 1, 128) 16512 bidirectional[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block (TransformerB (None, 1, 128) 69772 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d (Globa (None, 128) 0 transformer_block[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_2 (Dropout) (None, 128) 0 global_average_pooling1d[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_3 (Dense) (None, 64) 8256 dropout_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf.expand_dims (TFOpLambda) (None, 1, 64) 0 dense_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_1 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_2 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_3 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_4 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_1 (Glo (None, 64) 0 transformer_block_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_2 (Glo (None, 64) 0 transformer_block_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_3 (Glo (None, 64) 0 transformer_block_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_4 (Glo (None, 64) 0 transformer_block_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_5 (Dropout) (None, 64) 0 global_average_pooling1d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_8 (Dropout) (None, 64) 0 global_average_pooling1d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_11 (Dropout) (None, 64) 0 global_average_pooling1d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_14 (Dropout) (None, 64) 0 global_average_pooling1d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 32) 2080 dropout_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 32) 2080 dropout_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 32) 2080 dropout_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_15 (Dense) (None, 32) 2080 dropout_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "bet (Dense) (None, 1) 33 dense_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "mesco (Dense) (None, 1) 33 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "micro (Dense) (None, 1) 33 dense_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "avg (Dense) (None, 1) 33 dense_15[0][0] \n", + "==================================================================================================\n", + "Total params: 245,568\n", + "Trainable params: 245,568\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "prediction_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9289f452-a5a4-40c4-b942-f6cb2e348548", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import optimizers\n", + "from tensorflow.python.keras.utils.vis_utils import plot_model" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9a62dea1-4f05-411b-9756-a91623580581", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import ReduceLROnPlateau\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/40\n", + "11/11 [==============================] - 6s 108ms/step - loss: 0.0316 - val_loss: 0.0835\n", + "Epoch 2/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0281 - val_loss: 0.0958\n", + "Epoch 3/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0278 - val_loss: 0.0891\n", + "Epoch 4/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0233 - val_loss: 0.0912\n", + "Epoch 5/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0215 - val_loss: 0.1023\n", + "Epoch 6/40\n", + "11/11 [==============================] - 0s 33ms/step - loss: 0.0348 - val_loss: 0.0864\n", + "Epoch 7/40\n", + "11/11 [==============================] - 0s 16ms/step - loss: 0.0207 - val_loss: 0.0823\n", + "Epoch 8/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0222 - val_loss: 0.0883\n", + "Epoch 9/40\n", + "11/11 [==============================] - 0s 22ms/step - loss: 0.0258 - val_loss: 0.1029\n", + "Epoch 10/40\n", + "11/11 [==============================] - 0s 26ms/step - loss: 0.0288 - val_loss: 0.0857\n", + "Epoch 11/40\n", + "11/11 [==============================] - 0s 22ms/step - loss: 0.0249 - val_loss: 0.0880\n", + "Epoch 12/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0219 - val_loss: 0.0882\n", + "Epoch 13/40\n", + "11/11 [==============================] - 0s 24ms/step - loss: 0.0191 - val_loss: 0.0873\n", + "Epoch 14/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0187 - val_loss: 0.0929\n", + "Epoch 15/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0183 - val_loss: 0.0988\n", + "Epoch 16/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0189 - val_loss: 0.0905\n", + "Epoch 17/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0209 - val_loss: 0.0823\n", + "Epoch 18/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0185 - val_loss: 0.0834\n", + "Epoch 19/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0177 - val_loss: 0.0916\n", + "Epoch 20/40\n", + "11/11 [==============================] - 0s 24ms/step - loss: 0.0163 - val_loss: 0.0919\n", + "Epoch 21/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0141 - val_loss: 0.0898\n", + "Epoch 22/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0144 - val_loss: 0.0923\n", + "Epoch 23/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0138 - val_loss: 0.0906\n", + "Epoch 24/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0140 - val_loss: 0.0897\n", + "Epoch 25/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0126 - val_loss: 0.0892\n", + "Epoch 26/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0918\n", + "Epoch 27/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0123 - val_loss: 0.0935\n", + "Epoch 28/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0131 - val_loss: 0.0933\n", + "Epoch 29/40\n", + "11/11 [==============================] - 0s 17ms/step - loss: 0.0125 - val_loss: 0.0933\n", + "Epoch 30/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0932\n", + "Epoch 31/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0936\n", + "Epoch 32/40\n", + "11/11 [==============================] - 0s 28ms/step - loss: 0.0114 - val_loss: 0.0933\n", + "Epoch 33/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0122 - val_loss: 0.0932\n", + "Epoch 34/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0114 - val_loss: 0.0936\n", + "Epoch 35/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0938\n", + "Epoch 36/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0118 - val_loss: 0.0937\n", + "Epoch 37/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0127 - val_loss: 0.0937\n", + "Epoch 38/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0123 - val_loss: 0.0937\n", + "Epoch 39/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0124 - val_loss: 0.0937\n", + "Epoch 40/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0937\n" + ] + } + ], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1], Y[2], Y[3]], epochs=40, batch_size=8, verbose=1, \n", + " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1], Y_valid[2], Y_valid[3]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[0.8401114 ],\n", + " [0.4296295 ],\n", + " [0.34763122],\n", + " [0.33006623],\n", + " [0.74300694],\n", + " [0.48508543],\n", + " [0.48184243],\n", + " [0.7309267 ],\n", + " [0.5264127 ],\n", + " [0.7570494 ],\n", + " [0.29492375],\n", + " [0.34379733]], dtype=float32),\n", + " array([[0.9495956 ],\n", + " [0.19964108],\n", + " [0.25691378],\n", + " [0.15781167],\n", + " [0.39773428],\n", + " [0.257546 ],\n", + " [0.2265681 ],\n", + " [0.39088207],\n", + " [0.30309337],\n", + " [0.4006669 ],\n", + " [0.16448957],\n", + " [0.20928389]], dtype=float32),\n", + " array([[0.93163174],\n", + " 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] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", + "metadata": {}, + "outputs": [], + "source": [ + "real_rst = test[out_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径'], dtype='object')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_pm25 = pred_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", + "y_pred_pm10 = pred_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", + "y_pred_so2 = pred_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", + "y_pred_no2 = pred_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)\n", + "y_true_pm25 = real_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", + "y_true_pm10 = real_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", + "y_true_so2 = real_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", + "y_true_no2 = real_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "26ea6cfa-efad-443c-9dd9-844f8be42b91", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "28072e7c-c9d5-4ff6-940d-e94ae879afc9", + "metadata": {}, + "outputs": [], + "source": [ + "def print_eva(y_true, y_pred, tp):\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(MSE)\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n", + " print(f'RMSE: {round(RMSE, 4)}', end=',')\n", + " print(f'MAPE: {round(MAPE, 4) * 100} %', end=',')\n", + " print(f'MAE: {round(MAE, 4)}', end=',')\n", + " print(f'R_2: {round(R_2, 4)}')\n", + " return [MSE, RMSE, MAE, MAPE, R_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COL: 比表面积, MSE: 2.36E+05,RMSE: 485.5891,MAPE: 25.86 %,MAE: 340.8309,R_2: -0.1091\n", + "COL: 总孔体积, MSE: 5.15E-02,RMSE: 0.2268,MAPE: 23.810000000000002 %,MAE: 0.1519,R_2: 0.7657\n", + "COL: 微孔体积, MSE: 4.53E-02,RMSE: 0.2128,MAPE: 34.75 %,MAE: 0.1536,R_2: -0.0412\n", + "COL: 平均孔径, MSE: 4.63E-01,RMSE: 0.6802,MAPE: 15.620000000000001 %,MAE: 0.415,R_2: 0.5929\n" + ] + } + ], + "source": [ + "pm25_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", + "pm10_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", + "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", + "nox_eva = print_eva(y_true_no2, y_pred_no2, tp='平均孔径')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac4a4339-ec7d-4266-8197-5276c2395288", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/multi-task-NN-checkpoint.ipynb b/.ipynb_checkpoints/multi-task-NN-checkpoint.ipynb index d034502..ec65f06 100644 --- a/.ipynb_checkpoints/multi-task-NN-checkpoint.ipynb +++ b/.ipynb_checkpoints/multi-task-NN-checkpoint.ipynb @@ -7,14 +7,12 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'" + "import os" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", "metadata": {}, "outputs": [], @@ -33,58 +31,312 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", "metadata": {}, "outputs": [], "source": [ - "data = pd.read_csv('./data/20240102/train_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", - "metadata": {}, - "outputs": [], - "source": [ - "out_cols = [x for x in data.columns if '碳材料' in x]" + "data = pd.read_excel('./data/20240123/煤炭数据.xlsx', header=[1])\n", + "data.drop(columns=data.columns[11:], inplace=True)" ] }, { "cell_type": "code", "execution_count": 5, - "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "out_cols" + "object_cols = ['活化剂种类', '混合方式']\n", + "data = pd.get_dummies(data, columns=object_cols)" ] }, { "cell_type": "code", "execution_count": 6, - "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", "metadata": {}, "outputs": [], "source": [ - "train_data = data.dropna(subset=out_cols).fillna(0)" + "out_cols = ['比表面积', '总孔体积', '微孔体积']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" ] }, { "cell_type": "code", "execution_count": 7, + "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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灰分(d)挥发分(daf）活化剂比例活化温度活化时间升温速率比表面积总孔体积微孔体积活化剂种类_KOH混合方式_浸渍混合方式_研磨
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.......................................
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158 rows × 12 columns

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" + ], + "text/plain": [ + " 灰分(d) 挥发分(daf） 活化剂比例 活化温度 活化时间 升温速率 比表面积 总孔体积 微孔体积 \\\n", + "0 11.25 17.06 3.0 800 1.0 5.0 2784.0 1.0830 0.853 \n", + "1 8.53 13.46 3.0 800 1.0 5.0 2934.0 1.2290 1.074 \n", + "2 18.08 13.85 3.0 800 1.0 5.0 3059.0 1.3044 1.011 \n", + "3 11.42 12.31 3.0 800 1.0 5.0 2365.0 0.8030 0.605 \n", + "4 11.60 8.49 3.0 800 1.0 5.0 2988.0 1.2820 0.944 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "153 4.18 9.77 1.5 800 1.0 5.0 1772.0 0.7383 0.660 \n", + "154 4.18 9.77 2.0 800 1.0 5.0 2382.0 1.0370 0.899 \n", + "155 4.18 9.77 2.5 800 1.0 5.0 2996.0 1.3520 1.162 \n", + "156 4.18 9.77 3.0 800 1.0 5.0 3142.0 1.6080 1.204 \n", + "157 4.18 9.77 3.5 800 1.0 5.0 3389.0 2.0410 1.022 \n", + "\n", + " 活化剂种类_KOH 混合方式_浸渍 混合方式_研磨 \n", + "0 1 0 1 \n", + "1 1 0 1 \n", + "2 1 0 1 \n", + "3 1 0 1 \n", + "4 1 0 1 \n", + ".. ... ... ... \n", + "153 1 0 1 \n", + "154 1 0 1 \n", + "155 1 0 1 \n", + "156 1 0 1 \n", + "157 1 0 1 \n", + "\n", + "[158 rows x 12 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data = data.dropna(subset=out_cols).ffill().reset_index(drop=True)\n", + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7424096a-1283-46aa-a5a8-909ad3d60d9b", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c7b2cb8d-18bb-489a-af0c-29d80c719aa4", + "metadata": {}, + "outputs": [], + "source": [ + "train_data['比表面积'] = np.log1p(train_data['比表面积'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", "metadata": {}, "outputs": [ @@ -92,65 +344,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-04 16:14:39.388684: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-04-08 11:13:19.810980: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], "source": [ "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", - "import tensorflow.keras.backend as K" + "import keras\n", + "from keras import layers\n", + "import keras.backend as K" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c2318ce6-60d2-495c-91cd-67ca53609cf8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /tmp/ipykernel_43672/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.config.list_physical_devices('GPU')` instead.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-04 16:14:40.311876: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-01-04 16:14:40.319726: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", - "2024-01-04 16:14:40.406804: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", - "2024-01-04 16:14:40.406829: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", - "2024-01-04 16:14:40.406833: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", - "2024-01-04 16:14:40.406963: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", - "2024-01-04 16:14:40.406982: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", - "2024-01-04 16:14:40.406985: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n" - ] - }, - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.test.is_gpu_available()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", "metadata": {}, "outputs": [], @@ -178,27 +385,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", "metadata": {}, "outputs": [], "source": [ - "from tensorflow.keras import Model" + "from keras import Model" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", "metadata": {}, "outputs": [], "source": [ - "from tensorflow.keras.initializers import Constant" + "from keras.initializers import Constant" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", "metadata": {}, "outputs": [], @@ -238,77 +445,73 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "a190207e-5a59-4813-9660-758760cf1b73", "metadata": {}, "outputs": [], "source": [ - "num_heads, ff_dim = 1, 12" + "num_heads, ff_dim = 3, 12" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 18, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], "source": [ "def get_prediction_model():\n", " def build_output(out, out_name):\n", - " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", - " out = self_block(out)\n", - " out = layers.GlobalAveragePooling1D()(out)\n", - " out = layers.Dropout(0.1)(out)\n", + " # self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " # out = self_block(out)\n", + " # out = layers.GlobalAveragePooling1D()(out)\n", + " # out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(32, activation=\"relu\")(out)\n", - " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", " return out\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", - " # x = layers.Dropout(rate=0.1)(x)\n", + " x = layers.Dropout(rate=0.1)(x)\n", " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", - " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " out = layers.Dense(128, activation='relu')(lstm_out)\n", " transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", " out = transformer_block(lstm_out)\n", " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(64, activation='relu')(out)\n", - " out = K.expand_dims(out, axis=1)\n", + " # out = K.expand_dims(out, axis=1)\n", "\n", " bet = build_output(out, 'bet')\n", " mesco = build_output(out, 'mesco')\n", " micro = build_output(out, 'micro')\n", - " avg = build_output(out, 'avg')\n", "\n", - " bet = layers.Dense(1, activation='sigmoid', name='bet')(bet)\n", - " mesco = layers.Dense(1, activation='sigmoid', name='mesco')(mesco)\n", - " micro = layers.Dense(1, activation='sigmoid', name='micro')(micro)\n", - " avg = layers.Dense(1, activation='sigmoid', name='avg')(avg)\n", + " bet = layers.Dense(1, activation='sigmoid', name='bet2')(bet)\n", + " mesco = layers.Dense(1, activation='sigmoid', name='mesco2')(mesco)\n", + " micro = layers.Dense(1, activation='sigmoid', name='micro2')(micro)\n", "\n", - " model = Model(inputs=[inputs], outputs=[bet, mesco, micro, avg])\n", + " model = Model(inputs=[inputs], outputs=[bet, mesco, micro])\n", " return model\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 19, "id": "264001b1-5e4a-4786-96fd-2b5c70ab3212", "metadata": {}, "outputs": [], "source": [ "def get_trainable_model(prediction_model):\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", - " bet, mesco, micro, avg = prediction_model(inputs)\n", + " bet, mesco, micro = prediction_model(inputs)\n", " bet_real = layers.Input(shape=(1,), name='bet_real')\n", " mesco_real = layers.Input(shape=(1,), name='mesco_real')\n", " micro_real = layers.Input(shape=(1,), name='micro_real')\n", - " avg_real = layers.Input(shape=(1,), name='avg_real')\n", - " out = CustomMultiLossLayer(nb_outputs=4)([bet_real, mesco_real, micro_real, avg_real, bet, mesco, micro, avg])\n", - " return Model([inputs, bet_real, mesco_real, micro_real, avg_real], out)" + " out = CustomMultiLossLayer(nb_outputs=3)([bet_real, mesco_real, micro_real, bet, mesco, micro])\n", + " return Model([inputs, bet_real, mesco_real, micro_real], out)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 20, "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", "metadata": {}, "outputs": [], @@ -316,6 +519,7 @@ "maxs = train_data.max()\n", "mins = train_data.min()\n", "for col in train_data.columns:\n", + " train_data[col] = train_data[col].astype(float)\n", " if maxs[col] - mins[col] == 0:\n", " continue\n", " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" @@ -323,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 21, "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", "metadata": {}, "outputs": [ @@ -348,149 +552,95 @@ " \n", " \n", " \n", - " 热处理条件-热处理次数\n", - " 热处理条件-是否是中温停留\n", - " 第一次热处理-温度\n", - " 第一次热处理-升温速率\n", - " 第一次热处理-保留时间\n", - " 第二次热处理-温度\n", - " 第二次热处理-升温速率·\n", - " 第二次热处理-保留时间\n", - " 共碳化-是否是共碳化物质\n", - " 共碳化-共碳化物质/沥青\n", - " ...\n", - " 模板剂-种类_二氧化硅\n", - " 模板剂-种类_氢氧化镁\n", - " 模板剂-种类_氧化钙\n", - " 模板剂-种类_氧化锌\n", - " 模板剂-种类_氧化镁\n", - " 模板剂-种类_氯化钠\n", - " 模板剂-种类_氯化钾\n", - " 模板剂-种类_碱式碳酸镁\n", - " 模板剂-种类_碳酸钙\n", - " 模板剂-种类_纤维素\n", + " 灰分(d)\n", + " 挥发分(daf）\n", + " 活化剂比例\n", + " 活化温度\n", + " 活化时间\n", + " 升温速率\n", + " 比表面积\n", + " 总孔体积\n", + " 微孔体积\n", + " 活化剂种类_KOH\n", + " 混合方式_浸渍\n", + " 混合方式_研磨\n", " \n", " \n", " \n", " \n", " 0\n", + " 0.265345\n", + " 0.224627\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", - " 0.0\n", - " 0.166667\n", - " 0.3\n", - " 0.5\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", + " 0.916251\n", + " 0.371910\n", + " 0.417894\n", + " 1.0\n", " 0.0\n", " 1.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " 1\n", + " 0.201133\n", + " 0.160752\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", - " 0.0\n", - " 0.333333\n", - " 0.3\n", - " 0.5\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", + " 0.929645\n", + " 0.426592\n", + " 0.538462\n", + " 1.0\n", " 0.0\n", " 1.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " 2\n", + " 0.426582\n", + " 0.167672\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", - " 0.0\n", - " 0.333333\n", - " 0.3\n", - " 0.5\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", + " 0.940237\n", + " 0.454831\n", + " 0.504092\n", + " 1.0\n", " 0.0\n", " 1.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " 3\n", + " 0.269358\n", + " 0.140348\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", - " 0.0\n", - " 0.333333\n", - " 0.3\n", - " 0.5\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", + " 0.874116\n", + " 0.267041\n", + " 0.282597\n", + " 1.0\n", " 0.0\n", " 1.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " 4\n", + " 0.273607\n", + " 0.072569\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", + " 0.0\n", + " 0.934281\n", + " 0.446442\n", + " 0.467540\n", " 1.0\n", " 0.0\n", - " 0.166667\n", - " 0.3\n", - " 0.5\n", - " 0.666667\n", - " 0.5\n", - " 0.666667\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", " 1.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", " ...\n", @@ -506,198 +656,118 @@ " ...\n", " ...\n", " ...\n", - 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" 146\n", - " 0.0\n", - " 0.0\n", - " 0.666667\n", - " 0.3\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", + " 155\n", + " 0.098442\n", + " 0.095280\n", + " 0.406780\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", + " 0.934960\n", + " 0.472659\n", + " 0.586470\n", + " 1.0\n", " 0.0\n", + " 1.0\n", " \n", " \n", - " 147\n", - " 0.0\n", - " 0.0\n", - " 0.500000\n", - " 0.3\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0.0\n", + " 156\n", + " 0.098442\n", + " 0.095280\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", + " 0.947009\n", + " 0.568539\n", + " 0.609384\n", + " 1.0\n", " 0.0\n", + " 1.0\n", " \n", " \n", - " 148\n", - " 0.0\n", - " 0.0\n", - " 0.500000\n", - " 0.3\n", - " 0.0\n", - " 0.000000\n", - 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123 rows × 42 columns

\n", + "

158 rows × 12 columns

\n", "" ], "text/plain": [ - " 热处理条件-热处理次数 热处理条件-是否是中温停留 第一次热处理-温度 第一次热处理-升温速率 第一次热处理-保留时间 \\\n", - "0 0.0 0.0 0.166667 0.3 0.5 \n", - "1 0.0 0.0 0.333333 0.3 0.5 \n", - "2 0.0 0.0 0.333333 0.3 0.5 \n", - "3 0.0 0.0 0.333333 0.3 0.5 \n", - "4 1.0 0.0 0.166667 0.3 0.5 \n", - ".. ... ... ... ... ... \n", - "144 0.0 0.0 0.333333 0.3 0.0 \n", - "145 0.0 0.0 0.500000 0.3 0.0 \n", - "146 0.0 0.0 0.666667 0.3 0.0 \n", - "147 0.0 0.0 0.500000 0.3 0.0 \n", - "148 0.0 0.0 0.500000 0.3 0.0 \n", + " 灰分(d) 挥发分(daf） 活化剂比例 活化温度 活化时间 升温速率 比表面积 \\\n", + "0 0.265345 0.224627 0.491525 0.62963 0.142857 0.0 0.916251 \n", + "1 0.201133 0.160752 0.491525 0.62963 0.142857 0.0 0.929645 \n", + "2 0.426582 0.167672 0.491525 0.62963 0.142857 0.0 0.940237 \n", + "3 0.269358 0.140348 0.491525 0.62963 0.142857 0.0 0.874116 \n", + "4 0.273607 0.072569 0.491525 0.62963 0.142857 0.0 0.934281 \n", + ".. ... ... ... ... ... ... ... \n", + "153 0.098442 0.095280 0.237288 0.62963 0.142857 0.0 0.797597 \n", + "154 0.098442 0.095280 0.322034 0.62963 0.142857 0.0 0.875983 \n", + "155 0.098442 0.095280 0.406780 0.62963 0.142857 0.0 0.934960 \n", + "156 0.098442 0.095280 0.491525 0.62963 0.142857 0.0 0.947009 \n", + "157 0.098442 0.095280 0.576271 0.62963 0.142857 0.0 0.966042 \n", "\n", - " 第二次热处理-温度 第二次热处理-升温速率· 第二次热处理-保留时间 共碳化-是否是共碳化物质 共碳化-共碳化物质/沥青 ... \\\n", - "0 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "1 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "2 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "3 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "4 0.666667 0.5 0.666667 0.0 0.0 ... \n", - ".. ... ... ... ... ... ... \n", - "144 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "145 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "146 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "147 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "148 0.000000 0.0 0.000000 0.0 0.0 ... \n", + " 总孔体积 微孔体积 活化剂种类_KOH 混合方式_浸渍 混合方式_研磨 \n", + "0 0.371910 0.417894 1.0 0.0 1.0 \n", + "1 0.426592 0.538462 1.0 0.0 1.0 \n", + "2 0.454831 0.504092 1.0 0.0 1.0 \n", + "3 0.267041 0.282597 1.0 0.0 1.0 \n", + "4 0.446442 0.467540 1.0 0.0 1.0 \n", + ".. ... ... ... ... ... \n", + "153 0.242809 0.312602 1.0 0.0 1.0 \n", + "154 0.354682 0.442990 1.0 0.0 1.0 \n", + "155 0.472659 0.586470 1.0 0.0 1.0 \n", + "156 0.568539 0.609384 1.0 0.0 1.0 \n", + "157 0.730712 0.510093 1.0 0.0 1.0 \n", "\n", - " 模板剂-种类_二氧化硅 模板剂-种类_氢氧化镁 模板剂-种类_氧化钙 模板剂-种类_氧化锌 模板剂-种类_氧化镁 模板剂-种类_氯化钠 \\\n", - "0 0 0.0 1.0 0 0.0 0.0 \n", - "1 0 0.0 1.0 0 0.0 0.0 \n", - "2 0 0.0 1.0 0 0.0 0.0 \n", - "3 0 0.0 1.0 0 0.0 0.0 \n", - "4 0 0.0 0.0 0 0.0 0.0 \n", - ".. ... ... ... ... ... ... \n", - "144 0 0.0 0.0 0 0.0 0.0 \n", - "145 0 0.0 0.0 0 0.0 0.0 \n", - "146 0 0.0 0.0 0 0.0 0.0 \n", - "147 0 0.0 0.0 0 0.0 0.0 \n", - "148 0 0.0 0.0 0 0.0 0.0 \n", - "\n", - " 模板剂-种类_氯化钾 模板剂-种类_碱式碳酸镁 模板剂-种类_碳酸钙 模板剂-种类_纤维素 \n", - "0 0 0.0 0.0 0.0 \n", - "1 0 0.0 0.0 0.0 \n", - "2 0 0.0 0.0 0.0 \n", - "3 0 0.0 0.0 0.0 \n", - "4 0 1.0 0.0 0.0 \n", - ".. ... ... ... ... \n", - "144 0 0.0 0.0 0.0 \n", - "145 0 0.0 0.0 0.0 \n", - "146 0 0.0 0.0 0.0 \n", - "147 0 0.0 0.0 0.0 \n", - "148 0 0.0 0.0 0.0 \n", - "\n", - "[123 rows x 42 columns]" + "[158 rows x 12 columns]" ] }, - "execution_count": 41, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -708,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 22, "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", "metadata": {}, "outputs": [], @@ -720,140 +790,41 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 23, "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", "metadata": {}, "outputs": [], "source": [ - "use_data = train_data.copy()\n", + "use_data = train_data[use_cols].copy()\n", "for col in use_cols:\n", " use_data[col] = use_data[col].astype('float32')" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "execution_count": 24, + "id": "eeebafb2-1496-4248-9697-819d065f77b9", "metadata": {}, "outputs": [], "source": [ - "train, valid = train_test_split(use_data[use_cols], test_size=0.2, random_state=42, shuffle=True)\n", - "valid, test = train_test_split(valid, test_size=0.5, random_state=42, shuffle=True)" + "from sklearn.model_selection import KFold\n", + "kf = KFold(n_splits=10, shuffle=True, random_state=42)" ] }, { "cell_type": "code", - "execution_count": 45, - "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "execution_count": 25, + "id": "ae7ddb36-2456-45b7-9580-447e1a13ae7f", "metadata": {}, "outputs": [], "source": [ - "prediction_model = get_prediction_model()\n", - "trainable_model = get_trainable_model(prediction_model)" + "from keras import optimizers" ] }, { "cell_type": "code", - "execution_count": 46, - "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_4\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "input (InputLayer) [(None, 1, 38)] 0 \n", - "__________________________________________________________________________________________________\n", - "conv1d_3 (Conv1D) (None, 1, 64) 2496 input[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_3 (Bidirectional) (None, 1, 128) 66048 conv1d_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_28 (Dense) (None, 1, 128) 16512 bidirectional_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_7 (Transforme (None, 1, 128) 202640 dense_28[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_7 (Glo (None, 128) 0 transformer_block_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_23 (Dropout) (None, 128) 0 global_average_pooling1d_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_31 (Dense) (None, 64) 8256 dropout_23[0][0] \n", - "__________________________________________________________________________________________________\n", - "tf.expand_dims_3 (TFOpLambda) (None, 1, 64) 0 dense_31[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_8 (Transforme (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_9 (Transforme (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_10 (Transform (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_11 (Transform (None, 1, 64) 52176 tf.expand_dims_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_8 (Glo (None, 64) 0 transformer_block_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_9 (Glo (None, 64) 0 transformer_block_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_10 (Gl (None, 64) 0 transformer_block_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_11 (Gl (None, 64) 0 transformer_block_11[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_34 (Dense) (None, 32) 2080 global_average_pooling1d_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_37 (Dense) (None, 32) 2080 global_average_pooling1d_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_40 (Dense) (None, 32) 2080 global_average_pooling1d_10[0][0]\n", - "__________________________________________________________________________________________________\n", - "dense_43 (Dense) (None, 32) 2080 global_average_pooling1d_11[0][0]\n", - "__________________________________________________________________________________________________\n", - "bet (Dense) (None, 1) 33 dense_34[0][0] \n", - "__________________________________________________________________________________________________\n", - "mesco (Dense) (None, 1) 33 dense_37[0][0] \n", - "__________________________________________________________________________________________________\n", - "micro (Dense) (None, 1) 33 dense_40[0][0] \n", - "__________________________________________________________________________________________________\n", - "avg (Dense) (None, 1) 33 dense_43[0][0] \n", - "==================================================================================================\n", - "Total params: 513,108\n", - "Trainable params: 513,108\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "prediction_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "9289f452-a5a4-40c4-b942-f6cb2e348548", - "metadata": {}, - "outputs": [], - "source": [ - "from tensorflow.keras import optimizers\n", - "from tensorflow.python.keras.utils.vis_utils import plot_model" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", - "metadata": {}, - "outputs": [], - "source": [ - "X = np.expand_dims(train[feature_cols].values, axis=1)\n", - "Y = [x for x in train[out_cols].values.T]\n", - "Y_valid = [x for x in valid[out_cols].values.T]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "9a62dea1-4f05-411b-9756-a91623580581", + "execution_count": 26, + "id": "be4ee01b-6e52-47cf-9d43-587334a30dae", "metadata": {}, "outputs": [], "source": [ @@ -861,539 +832,14 @@ "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" ] }, - { - "cell_type": "code", - "execution_count": 50, - "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-04 16:17:21.543163: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", - "2024-01-04 16:17:21.562835: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/160\n", - "13/13 [==============================] - 6s 103ms/step - loss: 5.1128 - val_loss: 4.4845\n", - "Epoch 2/160\n", - "13/13 [==============================] - 0s 30ms/step - loss: 4.4173 - val_loss: 4.3305\n", - "Epoch 3/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 4.0913 - val_loss: 4.4123\n", - "Epoch 4/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 4.0410 - val_loss: 4.3142\n", - "Epoch 5/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 3.9933 - val_loss: 4.5518\n", - "Epoch 6/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 4.1234 - val_loss: 4.3268\n", - "Epoch 7/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 4.0470 - val_loss: 4.2908\n", - "Epoch 8/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 4.1230 - val_loss: 4.4964\n", - "Epoch 9/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 3.8889 - val_loss: 4.0178\n", - "Epoch 10/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 3.6648 - val_loss: 3.9010\n", - "Epoch 11/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.7712 - val_loss: 3.9471\n", - "Epoch 12/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.5449 - val_loss: 3.8723\n", - "Epoch 13/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 3.3373 - val_loss: 3.8543\n", - "Epoch 14/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 3.5200 - val_loss: 3.8259\n", - "Epoch 15/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 3.5623 - val_loss: 3.8838\n", - "Epoch 16/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 3.3898 - val_loss: 3.8122\n", - "Epoch 17/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 3.2718 - val_loss: 3.8799\n", - "Epoch 18/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.3303 - val_loss: 3.7849\n", - "Epoch 19/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 3.2860 - val_loss: 3.7713\n", - "Epoch 20/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 3.2669 - val_loss: 3.5689\n", - "Epoch 21/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 3.2366 - val_loss: 3.5238\n", - "Epoch 22/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 3.0037 - val_loss: 3.6039\n", - "Epoch 23/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.2087 - val_loss: 3.5221\n", - "Epoch 24/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.0619 - val_loss: 3.5939\n", - "Epoch 25/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.0423 - val_loss: 3.2731\n", - "Epoch 26/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 3.0533 - val_loss: 3.2256\n", - "Epoch 27/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 3.0105 - val_loss: 3.2154\n", - "Epoch 28/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.9607 - val_loss: 3.2926\n", - "Epoch 29/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 3.0072 - val_loss: 3.5834\n", - "Epoch 30/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.9276 - val_loss: 3.1635\n", - "Epoch 31/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.8930 - val_loss: 3.1363\n", - "Epoch 32/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.7904 - val_loss: 3.0188\n", - "Epoch 33/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 2.6856 - val_loss: 2.9808\n", - "Epoch 34/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 2.6503 - val_loss: 3.0943\n", - "Epoch 35/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 2.5339 - val_loss: 2.9359\n", - "Epoch 36/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.5369 - val_loss: 2.9704\n", - "Epoch 37/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 2.5478 - val_loss: 2.9344\n", - "Epoch 38/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 2.7129 - val_loss: 2.8326\n", - "Epoch 39/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.3453 - val_loss: 2.8198\n", - "Epoch 40/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.4666 - val_loss: 2.7701\n", - "Epoch 41/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 2.3401 - val_loss: 2.7727\n", - "Epoch 42/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.4369 - val_loss: 2.7568\n", - "Epoch 43/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.2170 - val_loss: 2.6998\n", - "Epoch 44/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.1565 - val_loss: 2.6711\n", - "Epoch 45/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.1251 - val_loss: 2.6134\n", - "Epoch 46/160\n", - "13/13 [==============================] - 0s 30ms/step - loss: 2.1728 - val_loss: 2.6394\n", - "Epoch 47/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.0609 - val_loss: 2.6568\n", - "Epoch 48/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.0893 - val_loss: 2.6603\n", - "Epoch 49/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 2.0615 - val_loss: 2.6517\n", - "Epoch 50/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 2.0500 - val_loss: 2.6041\n", - "Epoch 51/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.9309 - val_loss: 2.6218\n", - "Epoch 52/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.9971 - val_loss: 2.4494\n", - "Epoch 53/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 1.8514 - val_loss: 2.3886\n", - "Epoch 54/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.7823 - val_loss: 2.5517\n", - "Epoch 55/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.7059 - val_loss: 2.3293\n", - "Epoch 56/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.7039 - val_loss: 2.3810\n", - "Epoch 57/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.6961 - val_loss: 2.4552\n", - "Epoch 58/160\n", - "13/13 [==============================] - 0s 31ms/step - loss: 1.7350 - val_loss: 2.3526\n", - "Epoch 59/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.5840 - val_loss: 2.2976\n", - "Epoch 60/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.4915 - val_loss: 2.3516\n", - "Epoch 61/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.5910 - val_loss: 2.2383\n", - "Epoch 62/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.6101 - val_loss: 2.1474\n", - "Epoch 63/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.4698 - val_loss: 2.1210\n", - "Epoch 64/160\n", - "13/13 [==============================] - 0s 31ms/step - loss: 1.4796 - val_loss: 2.0695\n", - "Epoch 65/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.4472 - val_loss: 1.9768\n", - "Epoch 66/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.6250 - val_loss: 2.1760\n", - "Epoch 67/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.4058 - val_loss: 2.0605\n", - "Epoch 68/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.4318 - val_loss: 2.1487\n", - "Epoch 69/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.3285 - val_loss: 1.8259\n", - "Epoch 70/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.4306 - val_loss: 1.7314\n", - "Epoch 71/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.3449 - val_loss: 1.7509\n", - "Epoch 72/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.2737 - val_loss: 1.8892\n", - "Epoch 73/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.3647 - val_loss: 1.8109\n", - "Epoch 74/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.3075 - val_loss: 1.8175\n", - "Epoch 75/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.2765 - val_loss: 1.7334\n", - "Epoch 76/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.2761 - val_loss: 1.7685\n", - "Epoch 77/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.1870 - val_loss: 1.7683\n", - "Epoch 78/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.1441 - val_loss: 1.8794\n", - "Epoch 79/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.1193 - val_loss: 1.9073\n", - "Epoch 80/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 1.1361 - val_loss: 1.8016\n", - "Epoch 81/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 1.0629 - val_loss: 1.8359\n", - "Epoch 82/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.1137 - val_loss: 1.9310\n", - "Epoch 83/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0947 - val_loss: 1.9212\n", - "Epoch 84/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0225 - val_loss: 1.9027\n", - "Epoch 85/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0892 - val_loss: 1.8943\n", - "Epoch 86/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0387 - val_loss: 1.9131\n", - "Epoch 87/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.0590 - val_loss: 1.8768\n", - "Epoch 88/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.0909 - val_loss: 1.8732\n", - "Epoch 89/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.0632 - val_loss: 1.8506\n", - "Epoch 90/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 1.0703 - val_loss: 1.8108\n", - "Epoch 91/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.0262 - val_loss: 1.8143\n", - "Epoch 92/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.0330 - val_loss: 1.8132\n", - "Epoch 93/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0440 - val_loss: 1.8156\n", - "Epoch 94/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0279 - val_loss: 1.8182\n", - "Epoch 95/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0713 - val_loss: 1.8191\n", - "Epoch 96/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0401 - val_loss: 1.8187\n", - "Epoch 97/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0318 - val_loss: 1.8193\n", - "Epoch 98/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0306 - val_loss: 1.8233\n", - "Epoch 99/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0348 - val_loss: 1.8268\n", - "Epoch 100/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 1.0165 - val_loss: 1.8276\n", - "Epoch 101/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0826 - val_loss: 1.8275\n", - "Epoch 102/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0631 - val_loss: 1.8269\n", - "Epoch 103/160\n", - "13/13 [==============================] - 0s 31ms/step - loss: 0.9980 - val_loss: 1.8268\n", - "Epoch 104/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0245 - val_loss: 1.8270\n", - "Epoch 105/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.1240 - val_loss: 1.8272\n", - "Epoch 106/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0099 - val_loss: 1.8275\n", - "Epoch 107/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0435 - val_loss: 1.8272\n", - "Epoch 108/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0166 - val_loss: 1.8257\n", - "Epoch 109/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.0462 - val_loss: 1.8256\n", - "Epoch 110/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.1261 - val_loss: 1.8256\n", - "Epoch 111/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0418 - val_loss: 1.8256\n", - "Epoch 112/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 0.9933 - val_loss: 1.8255\n", - "Epoch 113/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0758 - val_loss: 1.8255\n", - "Epoch 114/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0435 - val_loss: 1.8254\n", - "Epoch 115/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0265 - val_loss: 1.8254\n", - "Epoch 116/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0268 - val_loss: 1.8254\n", - "Epoch 117/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0709 - val_loss: 1.8254\n", - "Epoch 118/160\n", - "13/13 [==============================] - 0s 36ms/step - loss: 1.0304 - val_loss: 1.8253\n", - "Epoch 119/160\n", - "13/13 [==============================] - 0s 25ms/step - loss: 1.0074 - val_loss: 1.8254\n", - "Epoch 120/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0375 - val_loss: 1.8254\n", - "Epoch 121/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0196 - val_loss: 1.8254\n", - "Epoch 122/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0706 - val_loss: 1.8254\n", - "Epoch 123/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0120 - val_loss: 1.8254\n", - "Epoch 124/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0962 - val_loss: 1.8254\n", - "Epoch 125/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 0.9942 - val_loss: 1.8254\n", - "Epoch 126/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.0419 - val_loss: 1.8254\n", - "Epoch 127/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.1072 - val_loss: 1.8254\n", - "Epoch 128/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.0153 - val_loss: 1.8254\n", - "Epoch 129/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0324 - val_loss: 1.8254\n", - "Epoch 130/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0363 - val_loss: 1.8254\n", - "Epoch 131/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 1.0624 - val_loss: 1.8254\n", - "Epoch 132/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.1191 - val_loss: 1.8254\n", - "Epoch 133/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0297 - val_loss: 1.8254\n", - "Epoch 134/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0494 - val_loss: 1.8254\n", - "Epoch 135/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0162 - val_loss: 1.8254\n", - "Epoch 136/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 0.9976 - val_loss: 1.8254\n", - "Epoch 137/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0401 - val_loss: 1.8254\n", - "Epoch 138/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 0.9879 - val_loss: 1.8254\n", - "Epoch 139/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0398 - val_loss: 1.8254\n", - "Epoch 140/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0297 - val_loss: 1.8254\n", - "Epoch 141/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0344 - val_loss: 1.8254\n", - "Epoch 142/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0372 - val_loss: 1.8254\n", - "Epoch 143/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.0513 - val_loss: 1.8254\n", - "Epoch 144/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.0447 - val_loss: 1.8254\n", - "Epoch 145/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 1.0532 - val_loss: 1.8254\n", - "Epoch 146/160\n", - "13/13 [==============================] - 0s 29ms/step - loss: 1.0670 - val_loss: 1.8254\n", - "Epoch 147/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.0499 - val_loss: 1.8254\n", - "Epoch 148/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 1.0295 - val_loss: 1.8254\n", - "Epoch 149/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.1065 - val_loss: 1.8254\n", - "Epoch 150/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.1115 - val_loss: 1.8254\n", - "Epoch 151/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.1047 - val_loss: 1.8254\n", - "Epoch 152/160\n", - "13/13 [==============================] - 0s 26ms/step - loss: 1.0098 - val_loss: 1.8254\n", - "Epoch 153/160\n", - "13/13 [==============================] - 0s 34ms/step - loss: 0.9954 - val_loss: 1.8254\n", - "Epoch 154/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.0815 - val_loss: 1.8254\n", - "Epoch 155/160\n", - "13/13 [==============================] - 0s 28ms/step - loss: 1.1248 - val_loss: 1.8254\n", - "Epoch 156/160\n", - "13/13 [==============================] - 0s 35ms/step - loss: 1.0116 - val_loss: 1.8254\n", - "Epoch 157/160\n", - "13/13 [==============================] - 0s 31ms/step - loss: 1.0502 - val_loss: 1.8254\n", - "Epoch 158/160\n", - "13/13 [==============================] - 0s 33ms/step - loss: 1.0578 - val_loss: 1.8254\n", - "Epoch 159/160\n", - "13/13 [==============================] - 0s 32ms/step - loss: 1.0356 - val_loss: 1.8254\n", - "Epoch 160/160\n", - "13/13 [==============================] - 0s 27ms/step - loss: 1.0162 - val_loss: 1.8254\n" - ] - } - ], - "source": [ - "trainable_model.compile(optimizer='adam', loss=None)\n", - "hist = trainable_model.fit([X, Y[0], Y[1], Y[2], Y[3]], epochs=160, batch_size=8, verbose=1, \n", - " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1], Y_valid[2], Y_valid[3]],\n", - " callbacks=[reduce_lr]\n", - " )" - ] - }, { "cell_type": "code", "execution_count": 27, - "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[0.00237915],\n", - " [0.21877757],\n", - " [0.00211415],\n", - " [0.00235748],\n", - " [0.2187585 ],\n", - " [0.25013232],\n", - " [0.00218698],\n", - " [0.00213578],\n", - " [0.0021604 ],\n", - " [0.00213698],\n", - " [0.00215602],\n", - " [0.00211859],\n", - " [0.00211859]], dtype=float32),\n", - " array([[0.26313323],\n", - " [0.43726084],\n", - " [0.257788 ],\n", - " [0.2622419 ],\n", - " [0.43731606],\n", - " [0.41615662],\n", - " [0.2588436 ],\n", - " [0.2605151 ],\n", - " [0.2610975 ],\n", - " [0.26035452],\n", - " [0.25860977],\n", - " [0.25888485],\n", - " [0.2590733 ]], dtype=float32),\n", - " array([[0.03315076],\n", - " [0.43969226],\n", - " [0.0066632 ],\n", - " [0.0311569 ],\n", - " [0.4396916 ],\n", - " [0.46122804],\n", - " [0.01751196],\n", - " [0.0046435 ],\n", - " [0.00397068],\n", - " [0.00480857],\n", - " [0.01166728],\n", - " [0.00597936],\n", - " [0.00580207]], dtype=float32),\n", - " array([[0.2627051 ],\n", - " [0.25722986],\n", - " [0.30297792],\n", - " [0.26330546],\n", - " [0.25718838],\n", - " [0.30138326],\n", - " [0.27484083],\n", - " [0.3198207 ],\n", - " [0.3352574 ],\n", - " [0.31860778],\n", - " [0.28594404],\n", - " [0.30712652],\n", - " [0.3081199 ]], dtype=float32)]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", - "rst" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.44671712167235617,\n", - " 0.995773503303174,\n", - " 0.8775154468883085,\n", - " 0.9863306026616467]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", - "metadata": {}, - "outputs": [], - "source": [ - "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", - "metadata": {}, - "outputs": [], - "source": [ - "real_rst = test[out_cols].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", - "metadata": {}, - "outputs": [], - "source": [ - "for col in out_cols:\n", - " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", - " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径'], dtype='object')" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "real_rst.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "21739f82-d82a-4bde-8537-9504b68a96d5", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_pm25 = pred_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", - "y_pred_pm10 = pred_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", - "y_pred_so2 = pred_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", - "y_pred_no2 = pred_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)\n", - "y_true_pm25 = real_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", - "y_true_pm10 = real_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", - "y_true_so2 = real_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", - "y_true_no2 = real_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "26ea6cfa-efad-443c-9dd9-844f8be42b91", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "28072e7c-c9d5-4ff6-940d-e94ae879afc9", + "id": "42cb8083-d37b-41e8-b674-fc8f2789a1b9", "metadata": {}, "outputs": [], "source": [ + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error\n", "def print_eva(y_true, y_pred, tp):\n", " MSE = mean_squared_error(y_true, y_pred)\n", " RMSE = np.sqrt(MSE)\n", @@ -1410,26 +856,3207 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "execution_count": null, + "id": "7404a0c6-1325-4348-b0a4-25dae3067d78", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 11:13:33.925432: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-04-08 11:13:33.947575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n", + "pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0\n", + "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s\n", + "2024-04-08 11:13:33.947605: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n", + "2024-04-08 11:13:33.968875: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n", + "2024-04-08 11:13:33.968940: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n", + "2024-04-08 11:13:33.972012: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n", + "2024-04-08 11:13:33.972302: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n", + "2024-04-08 11:13:33.972899: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n", + "2024-04-08 11:13:33.973713: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n", + "2024-04-08 11:13:33.973880: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n", + "2024-04-08 11:13:33.976420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n", + "2024-04-08 11:13:33.976836: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-04-08 11:13:33.986546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n", + "pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0\n", + "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s\n", + "2024-04-08 11:13:33.989040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n", + "2024-04-08 11:13:33.989091: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n", + "2024-04-08 11:13:34.622398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2024-04-08 11:13:34.622417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n", + "2024-04-08 11:13:34.622422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n", + "2024-04-08 11:13:34.626343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 37675 MB memory) -> physical GPU (device: 0, name: NVIDIA A100-PCIE-40GB, pci bus id: 0000:9c:00.0, compute capability: 8.0)\n", + "2024-04-08 11:13:47.978373: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-04-08 11:13:47.994803: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "COL: 比表面积, MSE: 2.59E+06,RMSE: 1609.7549,MAPE: 90.44 %,MAE: 1456.9479,R_2: -3.4577\n", - "COL: 总孔体积, MSE: 2.74E-01,RMSE: 0.5234,MAPE: 36.559999999999995 %,MAE: 0.4001,R_2: 0.1427\n", - "COL: 微孔体积, MSE: 1.45E-01,RMSE: 0.3802,MAPE: 77.27000000000001 %,MAE: 0.324,R_2: -2.0216\n", - "COL: 平均孔径, MSE: 1.44E+00,RMSE: 1.201,MAPE: 42.24 %,MAE: 1.0489,R_2: -0.0048\n" + "Epoch 1/280\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 11:14:01.812069: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n", + "2024-04-08 11:14:02.937519: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8700\n", + "2024-04-08 11:14:03.573600: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n", + "2024-04-08 11:14:03.574110: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n", + "2024-04-08 11:14:03.806121: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15/15 [==============================] - 18s 101ms/step - loss: 6.0853 - val_loss: 6.0270\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 6.0146 - val_loss: 5.9848\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.9665 - val_loss: 5.9331\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.9214 - val_loss: 5.8883\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.8715 - val_loss: 5.8414\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.8291 - val_loss: 5.7992\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.7840 - val_loss: 5.7525\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.7362 - val_loss: 5.7064\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.6912 - val_loss: 5.6601\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 5.6494 - val_loss: 5.6172\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.6031 - val_loss: 5.5690\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.5554 - val_loss: 5.5244\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.5094 - val_loss: 5.4814\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.4629 - val_loss: 5.4334\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.4173 - val_loss: 5.3924\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.3758 - val_loss: 5.3440\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.3243 - val_loss: 5.2960\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.2835 - val_loss: 5.2535\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.2403 - val_loss: 5.2099\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.1915 - val_loss: 5.1600\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.1437 - val_loss: 5.1163\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.1032 - val_loss: 5.0668\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.0552 - val_loss: 5.0426\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.0285 - val_loss: 4.9953\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.9751 - val_loss: 4.9410\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.9293 - val_loss: 4.9016\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.8772 - val_loss: 4.8457\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.8378 - val_loss: 4.8094\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7859 - val_loss: 4.7582\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7452 - val_loss: 4.7266\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.7049 - val_loss: 4.6671\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.6538 - val_loss: 4.6273\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.6117 - val_loss: 4.5850\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.5648 - val_loss: 4.5436\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.5190 - val_loss: 4.4914\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4741 - val_loss: 4.4457\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.4274 - val_loss: 4.4000\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3819 - val_loss: 4.3606\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3497 - val_loss: 4.3305\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3177 - val_loss: 4.3048\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2876 - val_loss: 4.2689\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2592 - val_loss: 4.2384\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.2289 - val_loss: 4.2159\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 4.1975 - val_loss: 4.1815\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1707 - val_loss: 4.1547\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1543 - val_loss: 4.1404\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1142 - val_loss: 4.1112\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0801 - val_loss: 4.0668\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0530 - val_loss: 4.0336\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.0184 - val_loss: 4.0059\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9920 - val_loss: 3.9744\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9605 - val_loss: 3.9486\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.9278 - val_loss: 3.9156\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8994 - val_loss: 3.8840\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.8660 - val_loss: 3.8531\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8381 - val_loss: 3.8223\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8065 - val_loss: 3.7922\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7815 - val_loss: 3.7619\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7489 - val_loss: 3.7333\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7183 - val_loss: 3.7022\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.6889 - val_loss: 3.6773\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.6607 - val_loss: 3.6405\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6283 - val_loss: 3.6107\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5965 - val_loss: 3.5830\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.5705 - val_loss: 3.5539\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5388 - val_loss: 3.5255\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5076 - val_loss: 3.4899\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 3.4752 - val_loss: 3.4631\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4472 - val_loss: 3.4308\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.4196 - val_loss: 3.4010\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3878 - val_loss: 3.3734\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3587 - val_loss: 3.3409\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3293 - val_loss: 3.3142\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2995 - val_loss: 3.2816\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2690 - val_loss: 3.2501\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2388 - val_loss: 3.2263\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2082 - val_loss: 3.1885\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1786 - val_loss: 3.1581\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1471 - val_loss: 3.1334\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1202 - val_loss: 3.1044\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0861 - val_loss: 3.0721\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0574 - val_loss: 3.0442\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0296 - val_loss: 3.0117\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0023 - val_loss: 2.9930\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9710 - val_loss: 2.9580\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9474 - val_loss: 2.9242\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9095 - val_loss: 2.9010\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.8816 - val_loss: 2.8642\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8502 - val_loss: 2.8332\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8203 - val_loss: 2.8042\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7879 - val_loss: 2.7716\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.7570 - val_loss: 2.7430\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.7259 - val_loss: 2.7168\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.7021 - val_loss: 2.6885\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 2.6682 - val_loss: 2.6570\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6432 - val_loss: 2.6263\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6101 - val_loss: 2.5897\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5778 - val_loss: 2.5681\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5558 - val_loss: 2.5327\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 2.5251 - val_loss: 2.5061\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4936 - val_loss: 2.4761\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4649 - val_loss: 2.4512\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4312 - val_loss: 2.4154\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4000 - val_loss: 2.3838\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3727 - val_loss: 2.3540\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.3407 - val_loss: 2.3310\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3078 - val_loss: 2.3006\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2818 - val_loss: 2.2619\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2515 - val_loss: 2.2308\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2204 - val_loss: 2.2044\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1891 - val_loss: 2.1807\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 2.1606 - val_loss: 2.1410\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1317 - val_loss: 2.1113\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0990 - val_loss: 2.0811\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.0764 - val_loss: 2.0522\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0421 - val_loss: 2.0214\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0155 - val_loss: 1.9915\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9799 - val_loss: 1.9629\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9494 - val_loss: 1.9385\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9251 - val_loss: 1.9051\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8949 - val_loss: 1.8759\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8644 - val_loss: 1.8706\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8489 - val_loss: 1.8308\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 1.8087 - val_loss: 1.7977\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7749 - val_loss: 1.7688\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7534 - val_loss: 1.7378\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7127 - val_loss: 1.7045\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6865 - val_loss: 1.6744\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6573 - val_loss: 1.6427\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6235 - val_loss: 1.6109\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5967 - val_loss: 1.5835\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5669 - val_loss: 1.5545\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5339 - val_loss: 1.5174\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5093 - val_loss: 1.4934\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.4793 - val_loss: 1.4596\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4495 - val_loss: 1.4335\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4142 - val_loss: 1.4022\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.3924 - val_loss: 1.3786\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.3609 - val_loss: 1.3442\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3259 - val_loss: 1.3106\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.3040 - val_loss: 1.2859\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2730 - val_loss: 1.2550\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2509 - val_loss: 1.2333\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2160 - val_loss: 1.1922\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.1840 - val_loss: 1.1680\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1514 - val_loss: 1.1352\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1227 - val_loss: 1.1014\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0908 - val_loss: 1.0708\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0584 - val_loss: 1.0415\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0349 - val_loss: 1.0432\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0144 - val_loss: 0.9994\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9710 - val_loss: 0.9684\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9420 - val_loss: 0.9297\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9124 - val_loss: 0.9023\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8858 - val_loss: 0.8702\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8488 - val_loss: 0.8434\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.8263 - val_loss: 0.8078\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7939 - val_loss: 0.7735\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7592 - val_loss: 0.7428\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7293 - val_loss: 0.7199\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7086 - val_loss: 0.6938\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6708 - val_loss: 0.6575\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6533 - val_loss: 0.6233\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6125 - val_loss: 0.5980\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5825 - val_loss: 0.5767\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5598\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5533 - val_loss: 0.5545\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5384 - val_loss: 0.5321\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5180 - val_loss: 0.5248\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5119 - val_loss: 0.5090\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4882 - val_loss: 0.5010\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4835 - val_loss: 0.4774\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4757 - val_loss: 0.4660\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4536 - val_loss: 0.4500\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4341 - val_loss: 0.4243\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4202 - val_loss: 0.4098\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4045 - val_loss: 0.3962\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3846 - val_loss: 0.3807\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.3694 - val_loss: 0.3647\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3567 - val_loss: 0.3471\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3420 - val_loss: 0.3336\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3252 - val_loss: 0.3194\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3148 - val_loss: 0.3046\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2958 - val_loss: 0.2945\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2874 - val_loss: 0.2774\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2675 - val_loss: 0.2683\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2531 - val_loss: 0.2504\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.2370 - val_loss: 0.2337\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2288 - val_loss: 0.2140\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2054 - val_loss: 0.2039\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1923 - val_loss: 0.1865\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1747 - val_loss: 0.1732\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1657 - val_loss: 0.1587\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1380 - val_loss: 0.1423\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1289 - val_loss: 0.1306\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1186 - val_loss: 0.1169\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0992 - val_loss: 0.1038\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0894 - val_loss: 0.0899\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0703 - val_loss: 0.0739\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0535 - val_loss: 0.0572\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0530 - val_loss: 0.0662\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0280 - val_loss: 0.0423\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0344\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0353\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0419\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0343\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0331\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0255 - val_loss: 0.0392\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0245 - val_loss: 0.0384\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0255 - val_loss: 0.0319\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0230 - val_loss: 0.0292\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0255 - val_loss: 0.0305\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0235 - val_loss: 0.0340\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0225 - val_loss: 0.0325\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0210 - val_loss: 0.0295\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0353\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0366\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0321\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0327\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0379\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0216 - val_loss: 0.0369\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0206 - val_loss: 0.0367\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0264 - val_loss: 0.0361\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0352\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0349\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0348\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0348\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0349\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0350\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0343\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0342\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0341\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0339\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0338\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0136 - val_loss: 0.0336\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0336\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0151 - val_loss: 0.0337\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0164 - val_loss: 0.0338\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0339\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0340\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0342\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0342\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0342\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0127 - val_loss: 0.0342\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0156 - val_loss: 0.0342\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0342\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0341\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0172 - val_loss: 0.0341\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0198 - val_loss: 0.0341\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0162 - val_loss: 0.0341\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0342\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0143 - val_loss: 0.0342\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0157 - val_loss: 0.0342\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0341\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0342\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0341\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0341\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0209 - val_loss: 0.0341\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0341\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0341\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0341\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0134 - val_loss: 0.0341\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0341\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0129 - val_loss: 0.0341\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0341\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0341\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0193 - val_loss: 0.0341\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0197 - val_loss: 0.0341\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0152 - val_loss: 0.0341\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0341\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.0105 - val_loss: 0.0341\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0147 - val_loss: 0.0341\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0125 - val_loss: 0.0341\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0176 - val_loss: 0.0341\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0341\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0341\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0341\n", + "COL: 比表面积, MSE: 1.32E-01,RMSE: 0.3629,MAPE: 3.6700000000000004 %,MAE: 0.2619,R_2: 0.2356\n", + "COL: 总孔体积, MSE: 7.52E-02,RMSE: 0.2742,MAPE: 27.810000000000002 %,MAE: 0.1978,R_2: 0.5771\n", + "COL: 微孔体积, MSE: 3.16E-02,RMSE: 0.1779,MAPE: 27.389999999999997 %,MAE: 0.1412,R_2: 0.3639\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 93ms/step - loss: 1.8382 - val_loss: 1.6969\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.6804 - val_loss: 1.6477\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6384 - val_loss: 1.6156\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6002 - val_loss: 1.5794\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5757 - val_loss: 1.5500\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5482 - val_loss: 1.5252\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5112 - val_loss: 1.4768\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4720 - val_loss: 1.4442\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4436 - val_loss: 1.4154\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4106 - val_loss: 1.3848\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3779 - val_loss: 1.3495\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3496 - val_loss: 1.3266\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3157 - val_loss: 1.2910\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2770 - val_loss: 1.2666\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2492 - val_loss: 1.2274\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2195 - val_loss: 1.1976\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1825 - val_loss: 1.1668\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1551 - val_loss: 1.1331\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1302 - val_loss: 1.1090\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1015 - val_loss: 1.0747\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0671 - val_loss: 1.0434\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0422 - val_loss: 1.0214\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0175 - val_loss: 0.9870\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9689 - val_loss: 0.9571\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.9638 - val_loss: 0.9219\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9229 - val_loss: 0.8921\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8851 - val_loss: 0.8686\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8600 - val_loss: 0.8591\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8503 - val_loss: 0.8390\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8302 - val_loss: 0.8242\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8137 - val_loss: 0.8139\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7981 - val_loss: 0.7926\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7801 - val_loss: 0.7753\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7702 - val_loss: 0.7752\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7537 - val_loss: 0.7512\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7407 - val_loss: 0.7492\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7295 - val_loss: 0.7353\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7083 - val_loss: 0.7060\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6915 - val_loss: 0.6916\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6770 - val_loss: 0.6797\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6640 - val_loss: 0.6657\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6501 - val_loss: 0.6506\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6339 - val_loss: 0.6335\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.6152 - val_loss: 0.6280\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6174 - val_loss: 0.6014\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5801 - val_loss: 0.5853\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5731 - val_loss: 0.5724\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5564 - val_loss: 0.5611\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5450 - val_loss: 0.5549\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5287 - val_loss: 0.5268\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5126 - val_loss: 0.5114\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5044 - val_loss: 0.4975\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4773 - val_loss: 0.4839\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4700 - val_loss: 0.4683\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4451 - val_loss: 0.4576\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4326 - val_loss: 0.4369\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4194 - val_loss: 0.4308\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4123 - val_loss: 0.4054\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3949 - val_loss: 0.4015\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3771 - val_loss: 0.3864\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3633 - val_loss: 0.3728\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3432 - val_loss: 0.3576\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3261 - val_loss: 0.3386\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3135 - val_loss: 0.3321\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3005 - val_loss: 0.3138\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2829 - val_loss: 0.2991\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2684 - val_loss: 0.2838\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2604 - val_loss: 0.2645\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2434 - val_loss: 0.2493\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2266 - val_loss: 0.2490\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2219 - val_loss: 0.2254\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1932 - val_loss: 0.2080\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.1971\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1630 - val_loss: 0.1745\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1646 - val_loss: 0.1629\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.1398 - val_loss: 0.1502\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1169 - val_loss: 0.1299\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1055 - val_loss: 0.1099\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0943 - val_loss: 0.0993\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0771 - val_loss: 0.0771\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0663 - val_loss: 0.0752\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0607\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0305 - val_loss: 0.0504\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0391 - val_loss: 0.0555\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0289 - val_loss: 0.0447\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0503\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0326 - val_loss: 0.0529\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0402 - val_loss: 0.0522\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0297 - val_loss: 0.0481\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0336 - val_loss: 0.0483\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0504\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0344 - val_loss: 0.0517\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0526\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0534\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0334 - val_loss: 0.0468\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0389 - val_loss: 0.0455\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0284 - val_loss: 0.0468\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0488\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0493\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0494\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0492\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0484\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0183 - val_loss: 0.0486\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0498\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0500\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0292 - val_loss: 0.0498\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0498\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0500\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0298 - val_loss: 0.0500\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0323 - val_loss: 0.0499\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0500\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0245 - val_loss: 0.0499\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0499\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0270 - val_loss: 0.0499\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0499\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0499\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0327 - val_loss: 0.0499\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0328 - val_loss: 0.0499\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0499\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0301 - val_loss: 0.0499\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0311 - val_loss: 0.0499\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0499\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0499\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0499\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0499\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0312 - val_loss: 0.0499\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0499\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0223 - val_loss: 0.0499\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0221 - val_loss: 0.0499\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0294 - val_loss: 0.0499\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0499\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0241 - val_loss: 0.0499\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0192 - val_loss: 0.0499\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0295 - val_loss: 0.0499\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0213 - val_loss: 0.0499\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0246 - val_loss: 0.0499\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0499\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0242 - val_loss: 0.0499\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0277 - val_loss: 0.0499\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0177 - val_loss: 0.0499\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0499\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0499\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0499\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0191 - val_loss: 0.0499\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0266 - val_loss: 0.0499\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0499\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0256 - val_loss: 0.0499\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0499\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0271 - val_loss: 0.0499\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0233 - val_loss: 0.0499\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0301 - val_loss: 0.0499\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0499\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0499\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0293 - val_loss: 0.0499\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0499\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0299 - val_loss: 0.0499\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0177 - val_loss: 0.0499\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0194 - val_loss: 0.0499\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0252 - val_loss: 0.0499\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0499\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0499\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0499\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0499\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0499\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0499\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0499\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0278 - val_loss: 0.0499\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0499\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0269 - val_loss: 0.0499\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0217 - val_loss: 0.0499\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0499\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0499\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0499\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0499\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0199 - val_loss: 0.0499\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0499\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0178 - val_loss: 0.0499\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0499\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0252 - val_loss: 0.0499\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0499\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0499\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0197 - val_loss: 0.0499\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0269 - val_loss: 0.0499\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0221 - val_loss: 0.0499\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0264 - val_loss: 0.0499\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0218 - val_loss: 0.0499\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0499\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0499\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0499\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0499\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0215 - val_loss: 0.0499\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0499\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0499\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0229 - val_loss: 0.0499\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0499\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0289 - val_loss: 0.0499\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0197 - val_loss: 0.0499\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0233 - val_loss: 0.0499\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0499\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0499\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0271 - val_loss: 0.0499\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0499\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0499\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0287 - val_loss: 0.0499\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0499\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0179 - val_loss: 0.0499\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0298 - val_loss: 0.0499\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0499\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0499\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0300 - val_loss: 0.0499\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0499\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0168 - val_loss: 0.0499\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0499\n", + "COL: 比表面积, MSE: 1.83E-01,RMSE: 0.4274,MAPE: 3.84 %,MAE: 0.2752,R_2: 0.4599\n", + "COL: 总孔体积, MSE: 1.35E-01,RMSE: 0.368,MAPE: 29.43 %,MAE: 0.251,R_2: 0.582\n", + "COL: 微孔体积, MSE: 1.75E-01,RMSE: 0.4187,MAPE: 32.84 %,MAE: 0.2536,R_2: 0.2184\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 86ms/step - loss: 2.5093 - val_loss: 2.2921\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2438 - val_loss: 2.2513\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2354 - val_loss: 2.1929\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.1850 - val_loss: 2.2179\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1398 - val_loss: 2.1617\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1034 - val_loss: 2.1344\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1028 - val_loss: 2.0470\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0489 - val_loss: 2.0199\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0085 - val_loss: 2.0242\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9942 - val_loss: 1.9674\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9434 - val_loss: 1.9257\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9492 - val_loss: 1.8951\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9382 - val_loss: 1.8804\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9204 - val_loss: 1.8696\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.8749 - val_loss: 1.8513\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8301 - val_loss: 1.7955\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.8124 - val_loss: 1.7826\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8018 - val_loss: 1.7692\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7991 - val_loss: 1.7415\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7584 - val_loss: 1.7389\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7315 - val_loss: 1.7086\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7082 - val_loss: 1.6742\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6990 - val_loss: 1.7330\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7054 - val_loss: 1.6404\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6263 - val_loss: 1.6385\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6006 - val_loss: 1.6112\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5858 - val_loss: 1.5762\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.6333\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5803 - val_loss: 1.5680\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5278 - val_loss: 1.5337\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5164 - val_loss: 1.5049\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5023 - val_loss: 1.4911\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4772 - val_loss: 1.4716\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4323 - val_loss: 1.4509\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4409 - val_loss: 1.4576\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4506 - val_loss: 1.4298\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4344 - val_loss: 1.4233\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3917 - val_loss: 1.3796\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3798 - val_loss: 1.3587\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3458 - val_loss: 1.3256\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3357 - val_loss: 1.3232\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2982 - val_loss: 1.2909\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2903 - val_loss: 1.2806\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2797 - val_loss: 1.2819\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2652 - val_loss: 1.2418\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2416 - val_loss: 1.2181\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1992 - val_loss: 1.1981\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1843 - val_loss: 1.1742\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1733 - val_loss: 1.1714\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1635 - val_loss: 1.1432\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1295 - val_loss: 1.1217\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1078 - val_loss: 1.0999\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0884 - val_loss: 1.0842\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0660 - val_loss: 1.0670\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0480 - val_loss: 1.0427\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0226 - val_loss: 1.0279\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0134 - val_loss: 1.0064\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9996 - val_loss: 0.9869\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9771 - val_loss: 0.9727\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9675 - val_loss: 0.9556\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9433 - val_loss: 0.9389\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9366 - val_loss: 0.9140\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9133 - val_loss: 0.9077\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8954 - val_loss: 0.8816\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8708 - val_loss: 0.8689\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8601 - val_loss: 0.8506\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8415 - val_loss: 0.8326\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8209 - val_loss: 0.8159\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8046 - val_loss: 0.8116\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7990 - val_loss: 0.7809\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7824 - val_loss: 0.7674\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7634 - val_loss: 0.7477\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7432 - val_loss: 0.7355\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7313 - val_loss: 0.7165\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7137 - val_loss: 0.6973\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6910 - val_loss: 0.6775\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6671 - val_loss: 0.6567\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6503 - val_loss: 0.6431\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6419 - val_loss: 0.6281\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6325 - val_loss: 0.6109\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6073 - val_loss: 0.5969\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5921 - val_loss: 0.5814\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5766 - val_loss: 0.5767\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5570\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5517 - val_loss: 0.5424\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5356 - val_loss: 0.5201\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5284 - val_loss: 0.5093\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4967 - val_loss: 0.4913\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4909 - val_loss: 0.4805\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4778 - val_loss: 0.4694\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4641 - val_loss: 0.4434\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4429 - val_loss: 0.4431\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4261 - val_loss: 0.4184\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4118 - val_loss: 0.4070\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4017 - val_loss: 0.3975\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3843 - val_loss: 0.3721\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3733 - val_loss: 0.3661\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3501 - val_loss: 0.3501\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3431 - val_loss: 0.3297\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3337 - val_loss: 0.3141\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.3057 - val_loss: 0.3023\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2870 - val_loss: 0.3007\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2794 - val_loss: 0.2787\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2690 - val_loss: 0.2698\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2539 - val_loss: 0.2383\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2290 - val_loss: 0.2458\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2229 - val_loss: 0.2211\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2076 - val_loss: 0.1950\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1842 - val_loss: 0.1891\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1702 - val_loss: 0.1710\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1541 - val_loss: 0.1655\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1383 - val_loss: 0.1385\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1276 - val_loss: 0.1214\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1092 - val_loss: 0.1268\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1004 - val_loss: 0.1230\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0888 - val_loss: 0.0899\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0756 - val_loss: 0.0718\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0530 - val_loss: 0.0633\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0413 - val_loss: 0.0487\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0440\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0337 - val_loss: 0.0697\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0481\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0335 - val_loss: 0.0503\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0281 - val_loss: 0.0584\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0400 - val_loss: 0.0570\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0367 - val_loss: 0.0713\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0343 - val_loss: 0.0378\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0355 - val_loss: 0.0768\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0532\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0310 - val_loss: 0.0867\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0276 - val_loss: 0.0567\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0301 - val_loss: 0.0598\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0711\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0405\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0316 - val_loss: 0.0599\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0296 - val_loss: 0.0631\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0415\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0422\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0531\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0517\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0230 - val_loss: 0.0481\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0484\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0277 - val_loss: 0.0496\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0532\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0250 - val_loss: 0.0496\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0543\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0539\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0538\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0536\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0228 - val_loss: 0.0535\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0533\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0530\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0179 - val_loss: 0.0530\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0271 - val_loss: 0.0529\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0174 - val_loss: 0.0526\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0526\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0526\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0527\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0174 - val_loss: 0.0526\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0527\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0194 - val_loss: 0.0527\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0527\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0290 - val_loss: 0.0527\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0219 - val_loss: 0.0527\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0208 - val_loss: 0.0527\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0527\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0527\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0527\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0231 - val_loss: 0.0527\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0527\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0527\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0527\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0527\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0307 - val_loss: 0.0527\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0527\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0527\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0527\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0215 - val_loss: 0.0527\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0344 - val_loss: 0.0527\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0313 - val_loss: 0.0527\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0527\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0527\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0527\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0180 - val_loss: 0.0527\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0527\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0527\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0527\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0527\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0527\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0527\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0228 - val_loss: 0.0527\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0527\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0527\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0224 - val_loss: 0.0527\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0527\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0527\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0527\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0527\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0527\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0527\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0527\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0527\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0527\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0527\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0527\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0527\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0527\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0527\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0527\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0527\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0183 - val_loss: 0.0527\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0527\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0527\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0166 - val_loss: 0.0527\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0527\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0527\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0316 - val_loss: 0.0527\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0527\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0527\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0263 - val_loss: 0.0527\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0527\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0527\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0527\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0527\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0213 - val_loss: 0.0527\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0527\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0527\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0527\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0527\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0527\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0158 - val_loss: 0.0527\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0527\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0194 - val_loss: 0.0527\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0527\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0527\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0200 - val_loss: 0.0527\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0527\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0190 - val_loss: 0.0527\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0217 - val_loss: 0.0527\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0527\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0263 - val_loss: 0.0527\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0527\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0306 - val_loss: 0.0527\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0527\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0210 - val_loss: 0.0527\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0527\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0527\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0527\n", + "COL: 比表面积, MSE: 1.91E-01,RMSE: 0.4372,MAPE: 4.16 %,MAE: 0.289,R_2: 0.5448\n", + "COL: 总孔体积, MSE: 8.81E-02,RMSE: 0.2969,MAPE: 25.869999999999997 %,MAE: 0.178,R_2: 0.5039\n", + "COL: 微孔体积, MSE: 2.92E-02,RMSE: 0.1709,MAPE: 31.97 %,MAE: 0.1435,R_2: 0.6463\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 87ms/step - loss: 5.1081 - val_loss: 4.9102\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.8975 - val_loss: 4.9026\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.8714 - val_loss: 4.8965\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.8855 - val_loss: 4.8015\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.7490 - val_loss: 4.8035\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7785 - val_loss: 4.7613\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7197 - val_loss: 4.7055\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.7034 - val_loss: 4.6356\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.6134 - val_loss: 4.6040\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.5380 - val_loss: 4.5892\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.5485 - val_loss: 4.5479\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4834 - val_loss: 4.5093\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.4568 - val_loss: 4.5328\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4267 - val_loss: 4.4700\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.4915 - val_loss: 4.3953\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4139 - val_loss: 4.3778\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3356 - val_loss: 4.3524\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2885 - val_loss: 4.3275\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.2311 - val_loss: 4.2670\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.1935 - val_loss: 4.2596\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1738 - val_loss: 4.1836\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1310 - val_loss: 4.1656\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.1215 - val_loss: 4.2036\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0710 - val_loss: 4.1054\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0426 - val_loss: 4.0336\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9955 - val_loss: 4.0605\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9581 - val_loss: 3.9995\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9062 - val_loss: 3.9595\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9252 - val_loss: 3.9659\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.8707 - val_loss: 3.8585\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8102 - val_loss: 3.8593\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7872 - val_loss: 3.7926\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8039 - val_loss: 3.8059\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7222 - val_loss: 3.7581\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7013 - val_loss: 3.7203\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6839 - val_loss: 3.6715\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6679\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5974 - val_loss: 3.6234\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5549 - val_loss: 3.6106\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5219 - val_loss: 3.5373\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4900 - val_loss: 3.5039\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.4689 - val_loss: 3.4978\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4203 - val_loss: 3.4309\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 3.3841 - val_loss: 3.4069\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 3.3521 - val_loss: 3.4026\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3213 - val_loss: 3.3949\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3278 - val_loss: 3.3207\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2762 - val_loss: 3.2875\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.2363 - val_loss: 3.2245\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1902 - val_loss: 3.2058\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1809 - val_loss: 3.1922\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1383 - val_loss: 3.1311\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0813 - val_loss: 3.1212\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0824 - val_loss: 3.0604\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 3.0412 - val_loss: 3.0347\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0030 - val_loss: 3.0090\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9850 - val_loss: 2.9901\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9486 - val_loss: 2.9865\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9330 - val_loss: 2.9117\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8916 - val_loss: 2.8976\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8388 - val_loss: 2.8618\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8202 - val_loss: 2.8307\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7957 - val_loss: 2.8076\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7548 - val_loss: 2.7568\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 2.7154 - val_loss: 2.7365\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.6893 - val_loss: 2.6783\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6608 - val_loss: 2.6590\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.6173 - val_loss: 2.6270\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6031 - val_loss: 2.5972\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5819 - val_loss: 2.5621\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5281 - val_loss: 2.5354\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4967 - val_loss: 2.4950\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4655 - val_loss: 2.4588\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4464 - val_loss: 2.4244\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4075 - val_loss: 2.3982\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3720 - val_loss: 2.3743\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3495 - val_loss: 2.3415\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2947 - val_loss: 2.2965\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2712 - val_loss: 2.2628\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2385 - val_loss: 2.2341\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2093 - val_loss: 2.2007\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1926 - val_loss: 2.1803\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1452 - val_loss: 2.1466\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1080 - val_loss: 2.1139\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0963 - val_loss: 2.0719\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0696 - val_loss: 2.0438\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0350 - val_loss: 2.0082\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9857 - val_loss: 1.9835\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9671 - val_loss: 1.9488\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9304 - val_loss: 1.9033\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9129 - val_loss: 1.8740\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8763 - val_loss: 1.8452\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8298 - val_loss: 1.8235\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.7952 - val_loss: 1.7809\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.7741 - val_loss: 1.7611\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7307 - val_loss: 1.7357\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.7168 - val_loss: 1.7038\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.6875 - val_loss: 1.6567\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.6440 - val_loss: 1.6213\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6178 - val_loss: 1.5933\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5765 - val_loss: 1.5616\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5462 - val_loss: 1.5271\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5152 - val_loss: 1.4967\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.4833 - val_loss: 1.4654\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4444 - val_loss: 1.4358\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4152 - val_loss: 1.4042\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3893 - val_loss: 1.3689\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3534 - val_loss: 1.3381\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3189 - val_loss: 1.3094\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2893 - val_loss: 1.2775\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2663 - val_loss: 1.2617\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2497 - val_loss: 1.2412\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2280 - val_loss: 1.2307\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2131 - val_loss: 1.2178\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1963 - val_loss: 1.2042\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.1870 - val_loss: 1.1858\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1782 - val_loss: 1.1721\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.1521 - val_loss: 1.1573\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1387 - val_loss: 1.1391\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1245 - val_loss: 1.1252\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1089 - val_loss: 1.1109\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0948 - val_loss: 1.0973\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0807 - val_loss: 1.0832\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0651 - val_loss: 1.0653\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.0550 - val_loss: 1.0520\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0281 - val_loss: 1.0342\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.0174 - val_loss: 1.0233\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0004 - val_loss: 1.0092\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.9905 - val_loss: 0.9870\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9755 - val_loss: 0.9740\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9609 - val_loss: 0.9636\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9390 - val_loss: 0.9474\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9285 - val_loss: 0.9306\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9186 - val_loss: 0.9154\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8964 - val_loss: 0.9008\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8825 - val_loss: 0.8857\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8665 - val_loss: 0.8731\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8525 - val_loss: 0.8578\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8374 - val_loss: 0.8368\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8190 - val_loss: 0.8262\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.8082 - val_loss: 0.8144\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7900 - val_loss: 0.7967\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.7780 - val_loss: 0.7780\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7609 - val_loss: 0.7624\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7445 - val_loss: 0.7526\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7292 - val_loss: 0.7464\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7150 - val_loss: 0.7205\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7076 - val_loss: 0.7118\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6928 - val_loss: 0.6972\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.6844\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6611 - val_loss: 0.6654\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6423 - val_loss: 0.6477\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6313 - val_loss: 0.6356\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6113 - val_loss: 0.6156\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5967 - val_loss: 0.6031\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5812 - val_loss: 0.5875\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5702 - val_loss: 0.5728\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5558 - val_loss: 0.5591\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5361 - val_loss: 0.5422\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5235 - val_loss: 0.5270\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5084 - val_loss: 0.5119\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4930 - val_loss: 0.4952\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4763 - val_loss: 0.4804\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4594 - val_loss: 0.4669\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4478 - val_loss: 0.4494\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4318 - val_loss: 0.4370\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4136 - val_loss: 0.4334\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4057 - val_loss: 0.4085\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3844 - val_loss: 0.3921\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3693 - val_loss: 0.3802\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3657 - val_loss: 0.3635\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3474 - val_loss: 0.3528\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3396 - val_loss: 0.3365\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3189 - val_loss: 0.3179\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3041 - val_loss: 0.3039\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2797 - val_loss: 0.2888\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2678 - val_loss: 0.2714\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2664 - val_loss: 0.2648\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2430 - val_loss: 0.2442\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2275 - val_loss: 0.2299\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2174 - val_loss: 0.2133\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1952 - val_loss: 0.2043\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1787 - val_loss: 0.1842\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1641 - val_loss: 0.1706\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1502 - val_loss: 0.1541\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1363 - val_loss: 0.1366\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1143 - val_loss: 0.1261\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1053 - val_loss: 0.1179\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0897 - val_loss: 0.0969\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0726 - val_loss: 0.0757\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0586 - val_loss: 0.0688\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0413 - val_loss: 0.0489\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0385\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0374\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0381\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0407\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0405\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0350\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0399\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0259 - val_loss: 0.0431\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0397\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0377\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0354\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0355\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0168 - val_loss: 0.0316\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0378\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0337\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0377\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0426\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0451\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0412\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0425\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0359\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0429\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0378\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0386\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0387\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0384\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0378\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0224 - val_loss: 0.0372\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0166 - val_loss: 0.0369\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0373\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0160 - val_loss: 0.0372\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0374\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0148 - val_loss: 0.0366\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0366\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0367\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0368\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0368\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0141 - val_loss: 0.0368\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0366\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0364\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0112 - val_loss: 0.0363\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0163 - val_loss: 0.0363\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0252 - val_loss: 0.0364\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0364\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0364\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0364\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0364\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0364\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0147 - val_loss: 0.0365\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0365\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0170 - val_loss: 0.0365\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0189 - val_loss: 0.0365\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0365\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0365\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0365\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0365\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0187 - val_loss: 0.0365\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0365\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0365\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0113 - val_loss: 0.0365\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0109 - val_loss: 0.0365\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0124 - val_loss: 0.0365\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0168 - val_loss: 0.0365\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0123 - val_loss: 0.0365\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0365\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0365\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0136 - val_loss: 0.0365\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0206 - val_loss: 0.0365\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0170 - val_loss: 0.0365\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0136 - val_loss: 0.0365\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0135 - val_loss: 0.0365\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0186 - val_loss: 0.0365\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0365\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0365\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0365\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0149 - val_loss: 0.0365\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0142 - val_loss: 0.0365\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0365\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0365\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0150 - val_loss: 0.0365\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0365\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0365\n", + "COL: 比表面积, MSE: 2.48E-01,RMSE: 0.4978,MAPE: 4.0 %,MAE: 0.302,R_2: 0.2379\n", + "COL: 总孔体积, MSE: 3.02E-01,RMSE: 0.5491,MAPE: 28.02 %,MAE: 0.3058,R_2: 0.1327\n", + "COL: 微孔体积, MSE: 2.86E-02,RMSE: 0.169,MAPE: 28.199999999999996 %,MAE: 0.119,R_2: 0.7352\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 92ms/step - loss: 3.8534 - val_loss: 3.7567\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6472 - val_loss: 3.6221\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4827 - val_loss: 3.5718\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4439 - val_loss: 3.7812\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4902 - val_loss: 3.4990\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4199 - val_loss: 3.5033\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3520 - val_loss: 3.4131\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2274 - val_loss: 3.4744\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.3141 - val_loss: 3.3652\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2422 - val_loss: 3.3192\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1901 - val_loss: 3.2861\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1546 - val_loss: 3.2805\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 8ms/step - loss: 3.1568 - val_loss: 3.1997\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0975 - val_loss: 3.1395\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0909 - val_loss: 3.1347\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0395 - val_loss: 3.0548\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0171 - val_loss: 3.0526\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8964 - val_loss: 3.0192\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9154 - val_loss: 3.0198\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8078 - val_loss: 2.9941\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7513 - val_loss: 2.9358\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8136 - val_loss: 2.8785\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8047 - val_loss: 2.8693\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.7416 - val_loss: 2.8484\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.6788 - val_loss: 2.8676\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6403 - val_loss: 2.7848\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 2.5648 - val_loss: 2.6941\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5826 - val_loss: 2.6609\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6405 - val_loss: 2.6603\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4518 - val_loss: 2.5825\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4556 - val_loss: 2.5640\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3909 - val_loss: 2.5145\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4314 - val_loss: 2.5188\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3933 - val_loss: 2.4724\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.3968 - val_loss: 2.4456\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.2659 - val_loss: 2.3750\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3021 - val_loss: 2.3642\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.2431 - val_loss: 2.3579\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2053 - val_loss: 2.2751\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2433 - val_loss: 2.2323\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.0799 - val_loss: 2.2380\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.1074 - val_loss: 2.2114\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0792 - val_loss: 2.1425\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0379 - val_loss: 2.0959\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0600 - val_loss: 2.0612\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9412 - val_loss: 2.0333\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9411 - val_loss: 2.0240\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.9220 - val_loss: 1.9449\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8270 - val_loss: 1.9371\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8160 - val_loss: 1.9008\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7859 - val_loss: 1.9029\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8180 - val_loss: 1.8543\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6860 - val_loss: 1.8061\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6871 - val_loss: 1.7867\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6814 - val_loss: 1.7409\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6678 - val_loss: 1.7037\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5697 - val_loss: 1.6629\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5739 - val_loss: 1.6251\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.5888\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5003 - val_loss: 1.5611\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4374 - val_loss: 1.5485\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4609 - val_loss: 1.5217\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3757 - val_loss: 1.4869\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3607 - val_loss: 1.4766\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3568 - val_loss: 1.4298\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3402 - val_loss: 1.4201\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2598 - val_loss: 1.3883\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2835 - val_loss: 1.4037\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3104 - val_loss: 1.3474\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.2975 - val_loss: 1.3322\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2389 - val_loss: 1.3066\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2247 - val_loss: 1.2726\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2057 - val_loss: 1.2699\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.1751 - val_loss: 1.2541\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1926 - val_loss: 1.2535\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1240 - val_loss: 1.2125\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1475 - val_loss: 1.1964\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1142 - val_loss: 1.1723\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0919 - val_loss: 1.1463\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0487 - val_loss: 1.1511\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0812 - val_loss: 1.1103\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0616 - val_loss: 1.1049\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0060 - val_loss: 1.0763\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9815 - val_loss: 1.0593\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9701 - val_loss: 1.0384\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0118 - val_loss: 1.0158\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9694 - val_loss: 0.9976\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8923 - val_loss: 0.9689\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8620 - val_loss: 0.9573\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8262 - val_loss: 0.9274\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8643 - val_loss: 0.9043\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7931 - val_loss: 0.8905\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7984 - val_loss: 0.8760\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7855 - val_loss: 0.8485\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7722 - val_loss: 0.8250\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7325 - val_loss: 0.8051\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7136 - val_loss: 0.7794\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6683 - val_loss: 0.7647\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7094 - val_loss: 0.7334\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6586 - val_loss: 0.7116\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6167 - val_loss: 0.6856\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6134 - val_loss: 0.6801\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6155 - val_loss: 0.6491\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5715 - val_loss: 0.6357\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5260 - val_loss: 0.6052\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.5269 - val_loss: 0.5999\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5412 - val_loss: 0.5573\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5057 - val_loss: 0.5422\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4933 - val_loss: 0.5037\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4462 - val_loss: 0.4962\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4479 - val_loss: 0.4797\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4036 - val_loss: 0.4708\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4004 - val_loss: 0.4384\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3423 - val_loss: 0.4223\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3605 - val_loss: 0.3956\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3069 - val_loss: 0.3667\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3148 - val_loss: 0.3675\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3581 - val_loss: 0.3360\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2954 - val_loss: 0.2939\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2562 - val_loss: 0.2863\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2189 - val_loss: 0.2752\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1909 - val_loss: 0.2655\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2091 - val_loss: 0.2517\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1874 - val_loss: 0.2449\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2034 - val_loss: 0.2362\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1790 - val_loss: 0.2391\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.2272\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1812 - val_loss: 0.2234\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1731 - val_loss: 0.2219\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1789 - val_loss: 0.2142\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1547 - val_loss: 0.2143\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1717 - val_loss: 0.1890\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1656 - val_loss: 0.1934\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1378 - val_loss: 0.1874\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1595 - val_loss: 0.1803\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1520 - val_loss: 0.1690\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1372 - val_loss: 0.1707\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1398 - val_loss: 0.1716\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1318 - val_loss: 0.1650\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1382 - val_loss: 0.1633\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1195 - val_loss: 0.1586\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1226 - val_loss: 0.1533\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1163 - val_loss: 0.1529\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1119 - val_loss: 0.1457\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1016 - val_loss: 0.1407\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1107 - val_loss: 0.1242\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1032 - val_loss: 0.1262\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0993 - val_loss: 0.1167\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0960 - val_loss: 0.1096\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0914 - val_loss: 0.1052\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1001 - val_loss: 0.1168\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1094 - val_loss: 0.1104\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1035 - val_loss: 0.0997\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1001 - val_loss: 0.0935\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0838 - val_loss: 0.0931\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0794 - val_loss: 0.0897\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0782 - val_loss: 0.0855\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0685 - val_loss: 0.0867\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0778 - val_loss: 0.0852\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0702 - val_loss: 0.0724\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0626 - val_loss: 0.0764\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0671 - val_loss: 0.0755\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0522 - val_loss: 0.0720\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0624 - val_loss: 0.0724\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0587 - val_loss: 0.0723\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0588 - val_loss: 0.0745\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0559 - val_loss: 0.0627\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0501 - val_loss: 0.0605\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0459 - val_loss: 0.0581\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0480 - val_loss: 0.0530\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0467 - val_loss: 0.0543\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0437 - val_loss: 0.0489\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0441 - val_loss: 0.0496\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0452 - val_loss: 0.0614\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0520\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0361 - val_loss: 0.0529\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0539\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0513\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0346 - val_loss: 0.0491\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0309 - val_loss: 0.0530\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0473\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0479\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0412 - val_loss: 0.0524\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0367 - val_loss: 0.0464\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0316 - val_loss: 0.0448\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0311 - val_loss: 0.0472\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0464\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0293 - val_loss: 0.0466\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0493\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0475\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0446\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0341 - val_loss: 0.0459\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0325 - val_loss: 0.0448\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0351 - val_loss: 0.0464\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0269 - val_loss: 0.0424\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0315 - val_loss: 0.0465\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0448\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0428\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0313 - val_loss: 0.0495\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0429\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0332 - val_loss: 0.0407\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0270 - val_loss: 0.0431\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0314 - val_loss: 0.0446\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0421\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0318 - val_loss: 0.0435\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0471\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0309 - val_loss: 0.0431\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0367\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0426\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0265 - val_loss: 0.0549\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0372 - val_loss: 0.0536\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0376 - val_loss: 0.0454\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0472\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0424\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0386\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0409\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0335 - val_loss: 0.0425\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0253 - val_loss: 0.0384\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0238 - val_loss: 0.0379\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0278 - val_loss: 0.0385\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0389\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0386\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0260 - val_loss: 0.0388\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0219 - val_loss: 0.0376\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0382\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0313 - val_loss: 0.0377\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0376\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0369\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0368\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0368\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0368\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0368\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0191 - val_loss: 0.0368\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0227 - val_loss: 0.0368\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0369\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0369\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0369\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0240 - val_loss: 0.0369\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0369\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0369\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0206 - val_loss: 0.0369\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0274 - val_loss: 0.0368\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0368\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0368\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0368\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0368\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0368\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0368\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0368\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0368\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0368\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0203 - val_loss: 0.0368\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0368\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0368\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0368\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0368\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0229 - val_loss: 0.0368\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0368\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0368\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0368\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0258 - val_loss: 0.0368\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0253 - val_loss: 0.0368\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0189 - val_loss: 0.0368\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0368\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0368\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0368\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0368\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0368\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0368\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0368\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0368\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0171 - val_loss: 0.0368\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0368\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0368\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0368\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0368\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0231 - val_loss: 0.0368\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0368\n", + "WARNING:tensorflow:5 out of the last 5 calls to .predict_function at 0x7f925acbf710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 比表面积, MSE: 4.64E-02,RMSE: 0.2154,MAPE: 1.9900000000000002 %,MAE: 0.1412,R_2: 0.8076\n", + "COL: 总孔体积, MSE: 7.08E-02,RMSE: 0.2661,MAPE: 26.39 %,MAE: 0.2135,R_2: 0.7685\n", + "COL: 微孔体积, MSE: 6.68E-02,RMSE: 0.2585,MAPE: 32.42 %,MAE: 0.1907,R_2: 0.5484\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 129ms/step - loss: 4.2350 - val_loss: 4.1776\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1771 - val_loss: 4.1449\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1223 - val_loss: 4.1071\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.0915 - val_loss: 4.0765\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0640 - val_loss: 4.0501\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0344 - val_loss: 4.0214\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9994 - val_loss: 3.9829\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.9724 - val_loss: 3.9461\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9340 - val_loss: 3.9392\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9182 - val_loss: 3.8950\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8824 - val_loss: 3.8600\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.8552 - val_loss: 3.8238\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8206 - val_loss: 3.8020\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7942 - val_loss: 3.7723\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7578 - val_loss: 3.7352\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7251 - val_loss: 3.7036\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6941 - val_loss: 3.6750\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6685 - val_loss: 3.6430\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6137\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6061 - val_loss: 3.5869\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.5691 - val_loss: 3.5518\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5421 - val_loss: 3.5220\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5133 - val_loss: 3.4925\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4835 - val_loss: 3.4649\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4501 - val_loss: 3.4398\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4284 - val_loss: 3.4076\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 3.3958 - val_loss: 3.3724\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3619 - val_loss: 3.3404\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.3317 - val_loss: 3.3143\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3014 - val_loss: 3.2824\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2694 - val_loss: 3.2526\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2441 - val_loss: 3.2212\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2127 - val_loss: 3.1935\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.1830 - val_loss: 3.1647\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1553 - val_loss: 3.1322\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.1277 - val_loss: 3.1093\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.1034 - val_loss: 3.0807\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0715 - val_loss: 3.0497\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0388 - val_loss: 3.0153\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0073 - val_loss: 2.9840\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9731 - val_loss: 2.9659\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9443 - val_loss: 2.9278\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9167 - val_loss: 2.8959\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8854 - val_loss: 2.8698\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8597 - val_loss: 2.8404\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8206 - val_loss: 2.8021\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7924 - val_loss: 2.7780\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7626 - val_loss: 2.7478\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7273 - val_loss: 2.7211\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7038 - val_loss: 2.6861\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6716 - val_loss: 2.6589\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6406 - val_loss: 2.6293\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6130 - val_loss: 2.5971\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.5839 - val_loss: 2.5632\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5519 - val_loss: 2.5352\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5227 - val_loss: 2.5058\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4997 - val_loss: 2.4834\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.4671 - val_loss: 2.4525\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4349 - val_loss: 2.4193\n" ] } ], + "source": [ + "total_bet = list()\n", + "total_micro = list()\n", + "total_mesco = list()\n", + "for train_index, test_index in kf.split(use_data):\n", + " test = use_data.iloc[test_index].copy()\n", + " train = use_data.iloc[train_index].copy()\n", + " train, valid = train_test_split(train, test_size=0.2, random_state=42, shuffle=True)\n", + " prediction_model = get_prediction_model()\n", + " trainable_model = get_trainable_model(prediction_model)\n", + " X = np.expand_dims(train[feature_cols].values, axis=1)\n", + " Y = [x for x in train[out_cols].values.T]\n", + " Y_valid = [x for x in valid[out_cols].values.T]\n", + " X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n", + " trainable_model.compile(optimizer='adam', loss=None)\n", + " hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, \n", + " validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]],\n", + " callbacks=[reduce_lr]\n", + " )\n", + " rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", + " pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)\n", + " real_rst = test[out_cols].copy()\n", + " for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " pred_rst['比表面积'] = np.expm1(pred_rst['比表面积'])\n", + " real_rst['比表面积'] = np.expm1(real_rst['比表面积'])\n", + " y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,)\n", + " y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,)\n", + " y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,)\n", + " y_true_pm25 = real_rst['比表面积'].values.reshape(-1,)\n", + " y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,)\n", + " y_true_so2 = real_rst['微孔体积'].values.reshape(-1,)\n", + " bet_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", + " mesco_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", + " micro_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", + " total_bet.append(bet_eva)\n", + " total_mesco.append(mesco_eva)\n", + " total_micro.append(micro_eva)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32cd89b1-3379-4c40-92f9-e5426c8b229d", + "metadata": {}, + "outputs": [], + "source": [ + "X_valid = np.expand_dims(valid[feature_cols].values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, \n", + " validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [], + "source": [ + "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", + "metadata": {}, + "outputs": [], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", + "metadata": {}, + "outputs": [], + "source": [ + "real_rst = test[out_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82cca0af-4aef-47d5-830a-04a51837c005", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst['比表面积'] = np.expm1(pred_rst['比表面积'])\n", + "real_rst['比表面积'] = np.expm1(real_rst['比表面积'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,)\n", + "y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,)\n", + "y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,)\n", + "y_true_pm25 = real_rst['比表面积'].values.reshape(-1,)\n", + "y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,)\n", + "y_true_so2 = real_rst['微孔体积'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "metadata": {}, + "outputs": [], "source": [ "pm25_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", "pm10_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", - "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", - "nox_eva = print_eva(y_true_no2, y_pred_no2, tp='平均孔径')" + "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')" ] }, { @@ -1451,9 +4078,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python37", "language": "python", - "name": "python3" + "name": "python37" }, "language_info": { "codemirror_mode": { @@ -1465,7 +4092,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.7.16" } }, "nbformat": 4, diff --git a/.ipynb_checkpoints/multi-task0102-checkpoint.ipynb b/.ipynb_checkpoints/multi-task0102-checkpoint.ipynb index e04979d..6fb9ab8 100644 --- a/.ipynb_checkpoints/multi-task0102-checkpoint.ipynb +++ b/.ipynb_checkpoints/multi-task0102-checkpoint.ipynb @@ -549,7 +549,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 16:46:07.061819: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-01-08 18:28:46.594783: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], @@ -666,6 +666,14 @@ "outputs": [], "source": [ "def get_prediction_model():\n", + " def build_output(out, out_name):\n", + " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " out = self_block(out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(32, activation=\"relu\")(out)\n", + " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", + " return out\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", " # x = layers.Dropout(rate=0.1)(x)\n", @@ -676,10 +684,12 @@ " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(64, activation='relu')(out)\n", - " # out = K.expand_dims(out, axis=1)\n", + " out = K.expand_dims(out, axis=1)\n", "\n", - " bet = layers.Dense(32, activation=\"relu\")(out)\n", - " mesco = layers.Dense(32, activation=\"relu\")(out)\n", + " # bet = layers.Dense(32, activation=\"relu\")(out)\n", + " # mesco = layers.Dense(32, activation=\"relu\")(out)\n", + " bet = build_output(out, 'bet')\n", + " mesco = build_output(out, 'mesco')\n", "\n", " bet = layers.Dense(1, activation='sigmoid', name='vad')(bet)\n", " mesco = layers.Dense(1, activation='sigmoid', name='fcad')(mesco)\n", @@ -940,7 +950,7 @@ "outputs": [], "source": [ "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", - "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols and '编号' not in x]\n", "use_cols = feature_cols + out_cols" ] }, @@ -1021,33 +1031,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 16:46:22.503307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", - "2024-01-05 16:46:22.560854: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", - "2024-01-05 16:46:22.560909: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", - "2024-01-05 16:46:22.560920: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", - "2024-01-05 16:46:22.561113: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", - "2024-01-05 16:46:22.561132: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", - "2024-01-05 16:46:22.561135: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n", - "2024-01-05 16:46:22.561424: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "2024-01-08 18:28:48.712096: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-08 18:28:48.770197: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-08 18:28:48.770270: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-08 18:28:48.770284: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-08 18:28:48.770578: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-08 18:28:48.770639: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-08 18:28:48.770650: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n", + "2024-01-08 18:28:48.771267: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] - }, - { - "ename": "ValueError", - "evalue": "in user code:\n\n /tmp/ipykernel_16320/2404117700.py:31 call *\n return K.concatenate(inputs, -1)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper **\n return target(*args, **kwargs)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:3098 concatenate\n return array_ops.concat([to_dense(x) for x in tensors], axis)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper\n return target(*args, **kwargs)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:1768 concat\n return gen_array_ops.concat_v2(values=values, axis=axis, name=name)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py:1227 concat_v2\n _, _, _op, _outputs = _op_def_library._apply_op_helper(\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:748 _apply_op_helper\n op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:599 _create_op_internal\n return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3557 _create_op_internal\n ret = Operation(\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:2041 __init__\n self._c_op = _create_c_op(self._graph, node_def, inputs,\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1883 _create_c_op\n raise ValueError(str(e))\n\n ValueError: Shape must be rank 2 but is rank 3 for '{{node custom_multi_loss_layer/concat}} = ConcatV2[N=4, T=DT_FLOAT, Tidx=DT_INT32](Placeholder, Placeholder_1, Placeholder_2, Placeholder_3, custom_multi_loss_layer/concat/axis)' with input shapes: [?,1], [?,1], [?,1,1], [?,1,1], [].\n", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[28], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m prediction_model \u001b[38;5;241m=\u001b[39m get_prediction_model()\n\u001b[0;32m----> 2\u001b[0m trainable_model \u001b[38;5;241m=\u001b[39m \u001b[43mget_trainable_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprediction_model\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m trainable_model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "Cell \u001b[0;32mIn[18], line 6\u001b[0m, in \u001b[0;36mget_trainable_model\u001b[0;34m(prediction_model)\u001b[0m\n\u001b[1;32m 4\u001b[0m bet_real \u001b[38;5;241m=\u001b[39m layers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m,), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvad_real\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m mesco_real \u001b[38;5;241m=\u001b[39m layers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m,), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfcad_real\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mCustomMultiLossLayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnb_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mbet_real\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmesco_real\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmesco\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Model([inputs, bet_real, mesco_real], out)\n", - "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:969\u001b[0m, in \u001b[0;36mLayer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 963\u001b[0m \u001b[38;5;66;03m# Functional Model construction mode is invoked when `Layer`s are called on\u001b[39;00m\n\u001b[1;32m 964\u001b[0m \u001b[38;5;66;03m# symbolic `KerasTensor`s, i.e.:\u001b[39;00m\n\u001b[1;32m 965\u001b[0m \u001b[38;5;66;03m# >> inputs = tf.keras.Input(10)\u001b[39;00m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;66;03m# >> outputs = MyLayer()(inputs) # Functional construction mode.\u001b[39;00m\n\u001b[1;32m 967\u001b[0m \u001b[38;5;66;03m# >> model = tf.keras.Model(inputs, outputs)\u001b[39;00m\n\u001b[1;32m 968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _in_functional_construction_mode(\u001b[38;5;28mself\u001b[39m, inputs, args, kwargs, input_list):\n\u001b[0;32m--> 969\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_functional_construction_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 970\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_list\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 972\u001b[0m \u001b[38;5;66;03m# Maintains info about the `Layer.call` stack.\u001b[39;00m\n\u001b[1;32m 973\u001b[0m call_context \u001b[38;5;241m=\u001b[39m base_layer_utils\u001b[38;5;241m.\u001b[39mcall_context()\n", - "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1107\u001b[0m, in \u001b[0;36mLayer._functional_construction_call\u001b[0;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[1;32m 1102\u001b[0m training_arg_passed_by_framework \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1104\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m call_context\u001b[38;5;241m.\u001b[39menter(\n\u001b[1;32m 1105\u001b[0m layer\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, inputs\u001b[38;5;241m=\u001b[39minputs, build_graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, training\u001b[38;5;241m=\u001b[39mtraining_value):\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;66;03m# Check input assumptions set after layer building, e.g. input shape.\u001b[39;00m\n\u001b[0;32m-> 1107\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_keras_tensor_symbolic_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1108\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_masks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m outputs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA layer\u001b[39m\u001b[38;5;130;01m\\'\u001b[39;00m\u001b[38;5;124ms `call` method should return a \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTensor or a list of Tensors, not None \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(layer: \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:840\u001b[0m, in \u001b[0;36mLayer._keras_tensor_symbolic_call\u001b[0;34m(self, inputs, input_masks, args, kwargs)\u001b[0m\n\u001b[1;32m 838\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mmap_structure(keras_tensor\u001b[38;5;241m.\u001b[39mKerasTensor, output_signature)\n\u001b[1;32m 839\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 840\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_infer_output_signature\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_masks\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:880\u001b[0m, in \u001b[0;36mLayer._infer_output_signature\u001b[0;34m(self, inputs, args, kwargs, input_masks)\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_build(inputs)\n\u001b[1;32m 879\u001b[0m inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_inputs(inputs)\n\u001b[0;32m--> 880\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mcall_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 882\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle_activity_regularization(inputs, outputs)\n\u001b[1;32m 883\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_mask_metadata(inputs, outputs, input_masks,\n\u001b[1;32m 884\u001b[0m build_graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py:695\u001b[0m, in \u001b[0;36mconvert..decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint:disable=broad-except\u001b[39;00m\n\u001b[1;32m 694\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(e, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mag_error_metadata\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 695\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mag_error_metadata\u001b[38;5;241m.\u001b[39mto_exception(e)\n\u001b[1;32m 696\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 697\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", - "\u001b[0;31mValueError\u001b[0m: in user code:\n\n /tmp/ipykernel_16320/2404117700.py:31 call *\n return K.concatenate(inputs, -1)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper **\n return target(*args, **kwargs)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:3098 concatenate\n return array_ops.concat([to_dense(x) for x in tensors], axis)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:206 wrapper\n return target(*args, **kwargs)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py:1768 concat\n return gen_array_ops.concat_v2(values=values, axis=axis, name=name)\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py:1227 concat_v2\n _, _, _op, _outputs = _op_def_library._apply_op_helper(\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py:748 _apply_op_helper\n op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:599 _create_op_internal\n return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3557 _create_op_internal\n ret = Operation(\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:2041 __init__\n self._c_op = _create_c_op(self._graph, node_def, inputs,\n /root/miniconda3/envs/python38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1883 _create_c_op\n raise ValueError(str(e))\n\n ValueError: Shape must be rank 2 but is rank 3 for '{{node custom_multi_loss_layer/concat}} = ConcatV2[N=4, T=DT_FLOAT, Tidx=DT_INT32](Placeholder, Placeholder_1, Placeholder_2, Placeholder_3, custom_multi_loss_layer/concat/axis)' with input shapes: [?,1], [?,1], [?,1,1], [?,1,1], [].\n" - ] } ], "source": [ @@ -1058,17 +1051,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "4a1be90d-b8f1-4fe1-9952-1cdcc489fab5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "plot_model(prediction_model)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0", "metadata": {}, "outputs": [ @@ -1076,28 +1081,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 13:55:16.952556: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", - "2024-01-05 13:55:16.970806: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "COL: 挥发分Vad, MSE: 5.39E-01,RMSE: 0.734,MAPE: 1.948 %,MAE: 0.582,R_2: 0.883\n", - "COL: 固定炭Fcad, MSE: 7.77E-01,RMSE: 0.881,MAPE: 1.246 %,MAE: 0.654,R_2: 0.969\n", - "COL: 挥发分Vad, MSE: 8.80E-01,RMSE: 0.938,MAPE: 2.679 %,MAE: 0.783,R_2: 0.893\n", - "COL: 固定炭Fcad, MSE: 1.32E+00,RMSE: 1.149,MAPE: 1.814 %,MAE: 0.907,R_2: 0.974\n", - "COL: 挥发分Vad, MSE: 6.68E-01,RMSE: 0.817,MAPE: 2.064 %,MAE: 0.606,R_2: 0.829\n", - "COL: 固定炭Fcad, MSE: 9.89E-01,RMSE: 0.995,MAPE: 1.427 %,MAE: 0.798,R_2: 0.929\n", - "COL: 挥发分Vad, MSE: 6.34E-01,RMSE: 0.796,MAPE: 2.099 %,MAE: 0.62,R_2: 0.889\n", - "COL: 固定炭Fcad, MSE: 4.93E-01,RMSE: 0.702,MAPE: 1.058 %,MAE: 0.542,R_2: 0.985\n", - "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7f0801c91c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 2.34E+00,RMSE: 1.53,MAPE: 4.385 %,MAE: 1.317,R_2: 0.467\n", - "COL: 固定炭Fcad, MSE: 2.21E+02,RMSE: 14.87,MAPE: 27.662 %,MAE: 14.835,R_2: -9.385\n", - "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7f0801cf34c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 6.16E-01,RMSE: 0.785,MAPE: 2.29 %,MAE: 0.674,R_2: 0.873\n", - "COL: 固定炭Fcad, MSE: 1.04E+00,RMSE: 1.02,MAPE: 1.603 %,MAE: 0.811,R_2: 0.956\n" + "2024-01-08 18:28:51.289876: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-01-08 18:28:51.306804: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" ] } ], @@ -1139,26 +1124,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MSE 0.667414\n", - "RMSE 0.814141\n", - "MAE 0.652951\n", - "MAPE 0.022159\n", - "R_2 0.873633\n", - "dtype: float64" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", "vad_df.sort_values(by='R_2')[1:].mean()" @@ -1166,22 +1135,22 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MSE 0.923848\n", - "RMSE 0.949375\n", - "MAE 0.742411\n", - "MAPE 0.014295\n", - "R_2 0.962834\n", + "MSE 0.820345\n", + "RMSE 0.899216\n", + "MAE 0.723321\n", + "MAPE 0.013728\n", + "R_2 0.967491\n", "dtype: float64" ] }, - "execution_count": 34, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } diff --git a/.ipynb_checkpoints/multioutput_regression-checkpoint.ipynb b/.ipynb_checkpoints/multioutput_regression-checkpoint.ipynb index e4ba576..eca96dc 100644 --- a/.ipynb_checkpoints/multioutput_regression-checkpoint.ipynb +++ b/.ipynb_checkpoints/multioutput_regression-checkpoint.ipynb @@ -1,43 +1,193 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# A demo for multi-output regression\n\nThe demo is adopted from scikit-learn:\n\nhttps://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py\n\nSee :doc:`/tutorials/multioutput` for more information.\n\n

Note

The feature is experimental. For the `multi_output_tree` strategy, many features are\n missing.

\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import argparse\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimport xgboost as xgb\n\n\ndef plot_predt(y: np.ndarray, y_predt: np.ndarray, name: str) -> None:\n s = 25\n plt.scatter(y[:, 0], y[:, 1], c=\"navy\", s=s, edgecolor=\"black\", label=\"data\")\n plt.scatter(\n y_predt[:, 0], y_predt[:, 1], c=\"cornflowerblue\", s=s, edgecolor=\"black\"\n )\n plt.xlim([-1, 2])\n plt.ylim([-1, 2])\n plt.show()\n\n\ndef gen_circle() -> Tuple[np.ndarray, np.ndarray]:\n \"Generate a sample dataset that y is a 2 dim circle.\"\n rng = np.random.RandomState(1994)\n X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)\n y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\n y[::5, :] += 0.5 - rng.rand(20, 2)\n y = y - y.min()\n y = y / y.max()\n return X, y\n\n\ndef rmse_model(plot_result: bool, strategy: str) -> None:\n \"\"\"Draw a circle with 2-dim coordinate as target variables.\"\"\"\n X, y = gen_circle()\n # Train a regressor on it\n reg = xgb.XGBRegressor(\n tree_method=\"hist\",\n n_estimators=128,\n n_jobs=16,\n max_depth=8,\n multi_strategy=strategy,\n subsample=0.6,\n )\n reg.fit(X, y, eval_set=[(X, y)])\n\n y_predt = reg.predict(X)\n if plot_result:\n plot_predt(y, y_predt, \"multi\")\n\n\ndef custom_rmse_model(plot_result: bool, strategy: str) -> None:\n \"\"\"Train using Python implementation of Squared Error.\"\"\"\n\n # As the experimental support status, custom objective doesn't support matrix as\n # gradient and hessian, which will be changed in future release.\n def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n \"\"\"Compute the gradient squared error.\"\"\"\n y = dtrain.get_label().reshape(predt.shape)\n return (predt - y).reshape(y.size)\n\n def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n \"\"\"Compute the hessian for squared error.\"\"\"\n return np.ones(predt.shape).reshape(predt.size)\n\n def squared_log(\n predt: np.ndarray, dtrain: xgb.DMatrix\n ) -> Tuple[np.ndarray, np.ndarray]:\n grad = gradient(predt, dtrain)\n hess = hessian(predt, dtrain)\n return grad, hess\n\n def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:\n y = dtrain.get_label().reshape(predt.shape)\n v = np.sqrt(np.sum(np.power(y - predt, 2)))\n return \"PyRMSE\", v\n\n X, y = gen_circle()\n Xy = xgb.DMatrix(X, y)\n results: Dict[str, Dict[str, List[float]]] = {}\n # Make sure the `num_target` is passed to XGBoost when custom objective is used.\n # When builtin objective is used, XGBoost can figure out the number of targets\n # automatically.\n booster = xgb.train(\n {\n \"tree_method\": \"hist\",\n \"num_target\": y.shape[1],\n \"multi_strategy\": strategy,\n },\n dtrain=Xy,\n num_boost_round=128,\n obj=squared_log,\n evals=[(Xy, \"Train\")],\n evals_result=results,\n custom_metric=rmse,\n )\n\n y_predt = booster.inplace_predict(X)\n if plot_result:\n plot_predt(y, y_predt, \"multi\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--plot\", choices=[0, 1], type=int, default=1)\n args = parser.parse_args()\n\n # Train with builtin RMSE objective\n # - One model per output.\n rmse_model(args.plot == 1, \"one_output_per_tree\")\n # - One model for all outputs, this is still working in progress, many features are\n # missing.\n rmse_model(args.plot == 1, \"multi_output_tree\")\n\n # Train with custom objective.\n # - One model per output.\n custom_rmse_model(args.plot == 1, \"one_output_per_tree\")\n # - One model for all outputs, this is still working in progress, many features are\n # missing.\n custom_rmse_model(args.plot == 1, \"multi_output_tree\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.18" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# A demo for multi-output regression\n", + "\n", + "The demo is adopted from scikit-learn:\n", + "\n", + "https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py\n", + "\n", + "See :doc:`/tutorials/multioutput` for more information.\n", + "\n", + "

Note

The feature is experimental. For the `multi_output_tree` strategy, many features are\n", + " missing.

\n" + ] }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'xgboost'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxgboost\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxgb\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_predt\u001b[39m(y: np\u001b[38;5;241m.\u001b[39mndarray, y_predt: np\u001b[38;5;241m.\u001b[39mndarray, name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 11\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m25\u001b[39m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'xgboost'" + ] + } + ], + "source": [ + "import argparse\n", + "from typing import Dict, List, Tuple\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "import xgboost as xgb\n", + "\n", + "\n", + "def plot_predt(y: np.ndarray, y_predt: np.ndarray, name: str) -> None:\n", + " s = 25\n", + " plt.scatter(y[:, 0], y[:, 1], c=\"navy\", s=s, edgecolor=\"black\", label=\"data\")\n", + " plt.scatter(\n", + " y_predt[:, 0], y_predt[:, 1], c=\"cornflowerblue\", s=s, edgecolor=\"black\"\n", + " )\n", + " plt.xlim([-1, 2])\n", + " plt.ylim([-1, 2])\n", + " plt.show()\n", + "\n", + "\n", + "def gen_circle() -> Tuple[np.ndarray, np.ndarray]:\n", + " \"Generate a sample dataset that y is a 2 dim circle.\"\n", + " rng = np.random.RandomState(1994)\n", + " X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)\n", + " y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T\n", + " y[::5, :] += 0.5 - rng.rand(20, 2)\n", + " y = y - y.min()\n", + " y = y / y.max()\n", + " return X, y\n", + "\n", + "\n", + "def rmse_model(plot_result: bool, strategy: str) -> None:\n", + " \"\"\"Draw a circle with 2-dim coordinate as target variables.\"\"\"\n", + " X, y = gen_circle()\n", + " # Train a regressor on it\n", + " reg = xgb.XGBRegressor(\n", + " tree_method=\"hist\",\n", + " n_estimators=128,\n", + " n_jobs=16,\n", + " max_depth=8,\n", + " multi_strategy=strategy,\n", + " subsample=0.6,\n", + " )\n", + " reg.fit(X, y, eval_set=[(X, y)])\n", + "\n", + " y_predt = reg.predict(X)\n", + " if plot_result:\n", + " plot_predt(y, y_predt, \"multi\")\n", + "\n", + "\n", + "def custom_rmse_model(plot_result: bool, strategy: str) -> None:\n", + " \"\"\"Train using Python implementation of Squared Error.\"\"\"\n", + "\n", + " # As the experimental support status, custom objective doesn't support matrix as\n", + " # gradient and hessian, which will be changed in future release.\n", + " def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n", + " \"\"\"Compute the gradient squared error.\"\"\"\n", + " y = dtrain.get_label().reshape(predt.shape)\n", + " return (predt - y).reshape(y.size)\n", + "\n", + " def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray:\n", + " \"\"\"Compute the hessian for squared error.\"\"\"\n", + " return np.ones(predt.shape).reshape(predt.size)\n", + "\n", + " def squared_log(\n", + " predt: np.ndarray, dtrain: xgb.DMatrix\n", + " ) -> Tuple[np.ndarray, np.ndarray]:\n", + " grad = gradient(predt, dtrain)\n", + " hess = hessian(predt, dtrain)\n", + " return grad, hess\n", + "\n", + " def rmse(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]:\n", + " y = dtrain.get_label().reshape(predt.shape)\n", + " v = np.sqrt(np.sum(np.power(y - predt, 2)))\n", + " return \"PyRMSE\", v\n", + "\n", + " X, y = gen_circle()\n", + " Xy = xgb.DMatrix(X, y)\n", + " results: Dict[str, Dict[str, List[float]]] = {}\n", + " # Make sure the `num_target` is passed to XGBoost when custom objective is used.\n", + " # When builtin objective is used, XGBoost can figure out the number of targets\n", + " # automatically.\n", + " booster = xgb.train(\n", + " {\n", + " \"tree_method\": \"hist\",\n", + " \"num_target\": y.shape[1],\n", + " \"multi_strategy\": strategy,\n", + " },\n", + " dtrain=Xy,\n", + " num_boost_round=128,\n", + " obj=squared_log,\n", + " evals=[(Xy, \"Train\")],\n", + " evals_result=results,\n", + " custom_metric=rmse,\n", + " )\n", + "\n", + " y_predt = booster.inplace_predict(X)\n", + " if plot_result:\n", + " plot_predt(y, y_predt, \"multi\")\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument(\"--plot\", choices=[0, 1], type=int, default=1)\n", + " args = parser.parse_args()\n", + "\n", + " # Train with builtin RMSE objective\n", + " # - One model per output.\n", + " rmse_model(args.plot == 1, \"one_output_per_tree\")\n", + " # - One model for all outputs, this is still working in progress, many features are\n", + " # missing.\n", + " rmse_model(args.plot == 1, \"multi_output_tree\")\n", + "\n", + " # Train with custom objective.\n", + " # - One model per output.\n", + " custom_rmse_model(args.plot == 1, \"one_output_per_tree\")\n", + " # - One model for all outputs, this is still working in progress, many features are\n", + " # missing.\n", + " custom_rmse_model(args.plot == 1, \"multi_output_tree\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/plot71-checkpoint.png b/.ipynb_checkpoints/plot71-checkpoint.png new file mode 100644 index 0000000000000000000000000000000000000000..a9543f66f1a87b26517e8588e44653a20ebd682e GIT binary patch literal 1005645 zcma&P1yt4Bx;D<<);7RnfP!M9k}4q$Dk@4UDXoIEbcf(}i(5oN6p$9_R=QCULFwMK zps?uf{GRE#_dmv8_BY1I7-yV&m&97XIp_O6_0IG7nv6L07KSZUR8-VguUwR)qT1g< 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编号煤种分析水Mad灰分挥发分碳氢氮硫氧碳化温度（℃）升温速率（℃/min)保温时间(h)KOHK2CO3BET比表面积（m2/g）孔体积（cm3/g)微孔体积（cm3/g)介孔体积（cm3/g)
01中级烟煤2.128.4937.1486.205.421.600.006.781100.02.02.000296.00.270NaNNaN
12萃取中级烟煤NaNNaNNaN75.114.731.380.0018.781100.02.02.000316.00.481NaNNaN
23褐煤14.914.3548.4267.764.571.293.5622.82650.010.00.510665.00.3560.2890.067
34褐煤14.914.3548.4267.764.571.293.5622.82650.010.00.5101221.00.6080.4820.126
45褐煤14.914.3548.4267.764.571.293.5622.82650.010.00.5102609.01.4380.6700.768
............................................................
6667无烟煤0.814.159.7791.593.961.760.212.48800.05.01.0103142.01.6081.2040.404
6768无烟煤0.814.159.7791.593.961.760.212.48800.05.01.0103389.02.0411.0221.019
6869无烟煤0.888.428.8391.692.312.040.003.96700.05.01.0102542.01.1350.9160.219
6970无烟煤0.888.428.8391.692.312.040.003.96800.05.01.0102665.01.2190.9470.272
7071无烟煤0.888.428.8391.692.312.040.003.96900.05.01.0102947.01.4730.7180.755
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"file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/.ipynb_checkpoints/未命名-checkpoint.ipynb b/.ipynb_checkpoints/未命名-checkpoint.ipynb new file mode 100644 index 0000000..8b8f3f2 --- /dev/null +++ b/.ipynb_checkpoints/未命名-checkpoint.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "798076ab-a8f3-47d4-a221-00b465568f41", + "metadata": {}, + "outputs": [], + "source": [ + "from statistics import mean\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import explained_variance_score,r2_score,median_absolute_error,mean_squared_error,mean_absolute_error\n", + "from scipy import stats\n", + "import numpy as np\n", + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"font.size\"] = 16\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "697bdeac-4ca7-4cf9-ad01-2ee00b4c7c72", + "metadata": {}, + "outputs": [], + "source": [ + "def scatter_out_1(x,y): ## x,y为两个需要做对比分析的两个量。\n", + " # ==========计算评价指标==========\n", + " BIAS = mean(x - y)\n", + " MSE = mean_squared_error(x, y)\n", + " RMSE = np.power(MSE, 0.5)\n", + " R2 = r2_score(x, y)\n", + " MAE = mean_absolute_error(x, y)\n", + " EV = explained_variance_score(x, y)\n", + " print('==========算法评价指标==========')\n", + " print('BIAS:', '%.3f' % (BIAS))\n", + " print('Explained Variance(EV):', '%.3f' % (EV))\n", + " print('Mean Absolute Error(MAE):', '%.3f' % (MAE))\n", + " print('Mean squared error(MSE):', '%.3f' % (MSE))\n", + " print('Root Mean Squard Error(RMSE):', '%.3f' % (RMSE))\n", + " print('R_squared:', '%.3f' % (R2))\n", + " # ===========Calculate the point density==========\n", + " xy = np.vstack([x, y])\n", + " z = stats.gaussian_kde(xy)(xy)\n", + " # ===========Sort the points by density, so that the densest points are plotted last===========\n", + " idx = z.argsort()\n", + " x, y, z = x[idx], y[idx], z[idx]\n", + " def best_fit_slope_and_intercept(xs, ys):\n", + " m = (((mean(xs) * mean(ys)) - mean(xs * ys)) / ((mean(xs) * mean(xs)) - mean(xs * xs)))\n", + " b = mean(ys) - m * mean(xs)\n", + " return m, b\n", + " m, b = best_fit_slope_and_intercept(x, y)\n", + " regression_line = []\n", + " for a in x:\n", + " regression_line.append((m * a) + b)\n", + " fig,ax=plt.subplots(figsize=(12,9),dpi=600)\n", + " scatter=ax.scatter(x,y,marker='o',c=z, edgecolors='b',s=15,label='LST',cmap='Spectral_r')\n", + " cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency', )\n", + " plt.plot([0,35],[0,35],'black',lw=1.5) # 画的1:1线，线的颜色为black，线宽为0.8\n", + " plt.plot(x,regression_line,'red',lw=1.5) # 预测与实测数据之间的回归线\n", + " plt.axis([0,35,0,35]) # 设置线的范围\n", + " plt.title(\"总孔体积拟合结果 $10^2 cm^3/g$\", fontdict={\"fontsize\":16})\n", + " plt.xlabel('预测值', fontdict={\"fontsize\":16})\n", + " plt.ylabel('真实值', fontdict={\"fontsize\":16})\n", + " plt.text(0.5,34, '$N=%.f$' % len(y), fontdict={\"fontsize\":16}) # text的位置需要根据x,y的大小范围进行调整。\n", + " plt.text(0.5,33, '$R^2=%.3f$' % R2, fontdict={\"fontsize\":16})\n", + " plt.text(0.5,32, '$BIAS=%.4f$' % BIAS, fontdict={\"fontsize\":16})\n", + " plt.text(0.5,31, '$RMSE=%.3f$' % RMSE, fontdict={\"fontsize\":16})\n", + " plt.xlim(0,35) # 设置x坐标轴的显示范围\n", + " plt.ylim(0,35) # 设置y坐标轴的显示范围\n", + " plt.savefig('./总孔体积.png',dpi=300, bbox_inches='tight',pad_inches=0)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e1279b5-4b18-4f57-bdc0-197ba84cda33", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2d5bcdf5-cc45-40b7-8000-a0e41b18ef93", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./rst/总孔体积_比表.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8908a8b3-6bd5-4d71-b012-cbdd690b6a31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 真实值 预测值\n", + "count 184.000000 184.000000\n", + "mean 267.871020 272.776239\n", + "std 696.475264 693.395059\n", + "min 0.060000 0.069085\n", + "25% 0.539250 0.570501\n", + "50% 0.877000 0.889113\n", + "75% 1.673250 1.551479\n", + "max 3322.000000 3225.575700" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d6bfbaae-a201-4963-be4e-4731a3fe8a37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==========算法评价指标==========\n", + "BIAS: 0.049\n", + "Explained Variance(EV): 0.921\n", + "Mean Absolute Error(MAE): 0.622\n", + "Mean squared error(MSE): 3.797\n", + "Root Mean Squard Error(RMSE): 1.949\n", + "R_squared: 0.921\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scatter_out_1(df['预测值'].values/100, df['真实值'].values/100)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6e95d4f4-eb89-4263-8f81-f1694ad7e97f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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碳源共碳化物质共碳化物/煤沥青加热次数是否有碳化过程模板剂种类模板剂比例KOH与煤沥青比例活化温度升温速率活化时间混合方式比表面积总孔体积微孔体积平均孔径
0煤沥青无0.01否自制氧化钙1.01.050052.0溶剂908.070.400.341.75
1煤沥青无0.01否自制氧化钙1.00.560052.0溶剂953.950.660.352.76
2煤沥青无0.01否自制氧化钙1.01.060052.0溶剂1388.620.610.531.77
3煤沥青无0.01否自制氧化钙1.02.060052.0溶剂1444.630.590.551.62
4煤沥青无0.02是自制碱式碳酸镁1.01.060052.0溶剂1020.990.450.351.77
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" + ], + "text/plain": [ + " 碳源 共碳化物质 共碳化物/煤沥青 加热次数 是否有碳化过程 模板剂种类 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 \\\n", + "0 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 500 5 \n", + "1 煤沥青 无 0.0 1 否 自制氧化钙 1.0 0.5 600 5 \n", + "2 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 600 5 \n", + "3 煤沥青 无 0.0 1 否 自制氧化钙 1.0 2.0 600 5 \n", + "4 煤沥青 无 0.0 2 是 自制碱式碳酸镁 1.0 1.0 600 5 \n", + "\n", + " 活化时间 混合方式 比表面积 总孔体积 微孔体积 平均孔径 \n", + "0 2.0 溶剂 908.07 0.40 0.34 1.75 \n", + "1 2.0 溶剂 953.95 0.66 0.35 2.76 \n", + "2 2.0 溶剂 1388.62 0.61 0.53 1.77 \n", + "3 2.0 溶剂 1444.63 0.59 0.55 1.62 \n", + "4 2.0 溶剂 1020.99 0.45 0.35 1.77 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤沥青数据.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b016e6bd-b4de-4a6e-a3d5-2fc544042eb8", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=['碳源'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d042e811-9548-480f-8a0b-27b72454fe43", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['共碳化物质', '是否有碳化过程', '模板剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4ccf1708-e9cd-49d5-bdf0-27f6fdb60e1c", + "metadata": {}, + "outputs": [], + "source": [ + "data_0102 = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['共碳化物/煤沥青',\n", + " '加热次数',\n", + " '模板剂比例',\n", + " 'KOH与煤沥青比例',\n", + " '活化温度',\n", + " '升温速率',\n", + " '活化时间',\n", + " '共碳化物质_2-甲基咪唑',\n", + " '共碳化物质_三聚氰胺',\n", + " '共碳化物质_尿素',\n", + " '共碳化物质_无',\n", + " '共碳化物质_硫酸铵',\n", + " '共碳化物质_聚磷酸铵',\n", + " '是否有碳化过程_否',\n", + " '是否有碳化过程_是',\n", + " '模板剂种类_Al2O3',\n", + " '模板剂种类_TiO2',\n", + " '模板剂种类_α-Fe2O3',\n", + " '模板剂种类_γ-Fe2O3',\n", + " '模板剂种类_二氧化硅',\n", + " '模板剂种类_无',\n", + " '模板剂种类_氯化钾',\n", + " '模板剂种类_纤维素',\n", + " '模板剂种类_自制氢氧化镁',\n", + " '模板剂种类_自制氧化钙',\n", + " '模板剂种类_自制氧化锌',\n", + " '模板剂种类_自制氧化镁',\n", + " '模板剂种类_自制碱式碳酸镁',\n", + " '模板剂种类_购买氢氧化镁',\n", + " '模板剂种类_购买氧化钙',\n", + " '模板剂种类_购买氧化锌',\n", + " '模板剂种类_购买氧化镁',\n", + " '模板剂种类_购买氯化钠',\n", + " '模板剂种类_购买碳酸钙',\n", + " '混合方式_溶剂',\n", + " '混合方式_研磨']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积', '平均孔径']\n", + "feature_cols = [x for x in data_0102.columns if x not in out_cols]\n", + "feature_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['比表面积', '总孔体积', '微孔体积', '平均孔径']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-23 12:21:49.081644: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-01-23 12:21:49.083823: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-01-23 12:21:49.125771: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2024-01-23 12:21:49.126872: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-01-23 12:21:50.338510: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow.keras.backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.initializers import Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", + "metadata": {}, + "outputs": [], + "source": [ + "def get_prediction_model():\n", + " inputs = layers.Input(shape=(1, len(feature_cols)), name='input')\n", + " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", + " # x = layers.Dropout(rate=0.1)(x)\n", + " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", + " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " # transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", + " # out = transformer_block(lstm_out)\n", + " # out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(lstm_out)\n", + " out = layers.Dense(64, activation='relu')(out)\n", + " bet = layers.Dense(1, activation='sigmoid', name='vad')(out)\n", + " model = Model(inputs=[inputs], outputs=[bet])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7a9915ee-0016-44e5-a6fb-5ee90532dc14", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-23 12:22:03.707721: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input (InputLayer) [(None, 1, 36)] 0 \n", + " \n", + " conv1d (Conv1D) (None, 1, 64) 2368 \n", + " \n", + " bidirectional (Bidirection (None, 1, 128) 66048 \n", + " al) \n", + " \n", + " dense (Dense) (None, 1, 128) 16512 \n", + " \n", + " dropout (Dropout) (None, 1, 128) 0 \n", + " \n", + " dense_1 (Dense) (None, 1, 64) 8256 \n", + " \n", + " vad (Dense) (None, 1, 1) 65 \n", + " \n", + "=================================================================\n", + "Total params: 93249 (364.25 KB)\n", + "Trainable params: 93249 (364.25 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = get_prediction_model()\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "372011ea-9876-41eb-a4e6-83ccd6c71559", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.python.keras.utils.vis_utils import plot_model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "965b1d6e-8b9f-4536-8205-06b314aeab51", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data_0102.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", + "metadata": {}, + "outputs": [], + "source": [ + "maxs = train_data.max()\n", + "mins = train_data.min()\n", + "for col in train_data.columns:\n", + " if maxs[col] - mins[col] == 0:\n", + " continue\n", + " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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共碳化物/煤沥青加热次数模板剂比例KOH与煤沥青比例活化温度升温速率活化时间比表面积总孔体积微孔体积...模板剂种类_自制氧化镁模板剂种类_自制碱式碳酸镁模板剂种类_购买氢氧化镁模板剂种类_购买氧化钙模板剂种类_购买氧化锌模板剂种类_购买氧化镁模板剂种类_购买氯化钠模板剂种类_购买碳酸钙混合方式_溶剂混合方式_研磨
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40.01.00.10.0666670.1666670.30.3333330.3076660.1552270.278754...0.01.00.00.00.00.00.00.01.00.0
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149 rows × 40 columns

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" + ], + "text/plain": [ + " 共碳化物/煤沥青 加热次数 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 活化时间 比表面积 \\\n", + "0 0.0 0.0 0.1 0.066667 0.000000 0.3 0.333333 0.273437 \n", + "1 0.0 0.0 0.1 0.033333 0.166667 0.3 0.333333 0.287345 \n", + "2 0.0 0.0 0.1 0.066667 0.166667 0.3 0.333333 0.419103 \n", + "3 0.0 0.0 0.1 0.133333 0.166667 0.3 0.333333 0.436081 \n", + "4 0.0 1.0 0.1 0.066667 0.166667 0.3 0.333333 0.307666 \n", + ".. ... ... ... ... ... ... ... ... \n", + "144 0.0 0.0 0.0 0.266667 0.166667 0.3 0.000000 0.592301 \n", + "145 0.0 0.0 0.0 0.266667 0.333333 0.3 0.000000 0.843589 \n", + "146 0.0 0.0 0.0 0.266667 0.500000 0.3 0.000000 0.682631 \n", + "147 0.0 0.0 0.0 0.200000 0.333333 0.3 0.000000 0.569567 \n", + "148 0.0 0.0 0.0 0.333333 0.333333 0.3 0.000000 0.776902 \n", + "\n", + " 总孔体积 微孔体积 ... 模板剂种类_自制氧化镁 模板剂种类_自制碱式碳酸镁 模板剂种类_购买氢氧化镁 \\\n", + "0 0.137628 0.270767 ... 0.0 0.0 0.0 \n", + "1 0.229145 0.278754 ... 0.0 0.0 0.0 \n", + "2 0.211545 0.422524 ... 0.0 0.0 0.0 \n", + "3 0.204505 0.438498 ... 0.0 0.0 0.0 \n", + "4 0.155227 0.278754 ... 0.0 1.0 0.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0.331221 0.638179 ... 0.0 0.0 0.0 \n", + "145 0.472017 0.941693 ... 0.0 0.0 0.0 \n", + "146 0.376980 0.781949 ... 0.0 0.0 0.0 \n", + "147 0.292503 0.614217 ... 0.0 0.0 0.0 \n", + "148 0.394579 0.885783 ... 0.0 0.0 0.0 \n", + "\n", + " 模板剂种类_购买氧化钙 模板剂种类_购买氧化锌 模板剂种类_购买氧化镁 模板剂种类_购买氯化钠 模板剂种类_购买碳酸钙 混合方式_溶剂 \\\n", + "0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 1.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "145 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "146 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "147 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "148 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " 混合方式_研磨 \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + ".. ... \n", + "144 1.0 \n", + "145 1.0 \n", + "146 1.0 \n", + "147 1.0 \n", + "148 1.0 \n", + "\n", + "[149 rows x 40 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "70414dca-d785-4f70-9521-6e58221486be", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import optimizers\n", + "from tensorflow.python.keras.utils.vis_utils import plot_model\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7f985922-75d4-45f2-9603-4a38ca84f696", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import ReduceLROnPlateau\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "266cd0ae-5681-402b-a4f7-ef705e3ac0cb", + "metadata": {}, + "outputs": [], + "source": [ + "def print_eva(y_true, y_pred, tp):\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(MSE)\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n", + " print(f'RMSE: {round(RMSE, 3)}', end=',')\n", + " print(f'MAPE: {round(MAPE * 100, 3)} %', end=',')\n", + " print(f'MAE: {round(MAE, 3)}', end=',')\n", + " print(f'R_2: {round(R_2, 3)}')\n", + " return [MSE, RMSE, MAE, MAPE, R_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a1e4e900-b97d-4c52-88ad-439b80866f6b", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.losses import mean_squared_error" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c47baf5e-8557-46d7-b67d-85530baf1af0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "比表面积\n", + "1/1 [==============================] - 1s 588ms/step\n", + "COL: 比表面积, MSE: 6.12E+05,RMSE: 782.4299926757812,MAPE: 409.191 %,MAE: 622.412,R_2: 0.034\n", + "1/1 [==============================] - 1s 546ms/step\n", + "COL: 比表面积, MSE: 4.94E+05,RMSE: 703.1320190429688,MAPE: 400.892 %,MAE: 560.341,R_2: 0.019\n", + "1/1 [==============================] - 1s 576ms/step\n", + "COL: 比表面积, MSE: 7.66E+05,RMSE: 875.4010009765625,MAPE: 807.376 %,MAE: 735.814,R_2: 0.043\n", + "1/1 [==============================] - 1s 1s/step\n", + "COL: 比表面积, MSE: 6.41E+05,RMSE: 800.6019897460938,MAPE: 1664.653 %,MAE: 621.767,R_2: -0.055\n", + "WARNING:tensorflow:5 out of the last 5 calls to .predict_function at 0x7fdc106a8b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "1/1 [==============================] - 1s 571ms/step\n", + "COL: 比表面积, MSE: 6.27E+05,RMSE: 791.9520263671875,MAPE: 756.572 %,MAE: 627.752,R_2: -0.022\n", + "总孔体积\n", + "WARNING:tensorflow:6 out of the last 6 calls to .predict_function at 0x7fdb30784b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "1/1 [==============================] - 1s 540ms/step\n", + "COL: 总孔体积, MSE: 2.80E-01,RMSE: 0.5289999842643738,MAPE: 313.778 %,MAE: 0.428,R_2: 0.153\n", + "1/1 [==============================] - 1s 574ms/step\n", + "COL: 总孔体积, MSE: 1.50E-01,RMSE: 0.3880000114440918,MAPE: 381.536 %,MAE: 0.32,R_2: 0.016\n", + "1/1 [==============================] - 1s 556ms/step\n", + "COL: 总孔体积, MSE: 2.93E-01,RMSE: 0.5419999957084656,MAPE: 166.29 %,MAE: 0.384,R_2: 0.104\n", + "1/1 [==============================] - 1s 540ms/step\n", + "COL: 总孔体积, MSE: 3.27E-01,RMSE: 0.5720000267028809,MAPE: 428.563 %,MAE: 0.426,R_2: 0.124\n", + "1/1 [==============================] - 1s 557ms/step\n", + "COL: 总孔体积, MSE: 3.52E-01,RMSE: 0.5929999947547913,MAPE: 333.533 %,MAE: 0.405,R_2: 0.12\n", + "微孔体积\n", + "1/1 [==============================] - 1s 541ms/step\n", + "COL: 微孔体积, MSE: 7.01E-02,RMSE: 0.26499998569488525,MAPE: 597.598 %,MAE: 0.212,R_2: 0.031\n", + "1/1 [==============================] - 1s 544ms/step\n", + "COL: 微孔体积, MSE: 1.05E-01,RMSE: 0.3240000009536743,MAPE: 1788.931 %,MAE: 0.273,R_2: 0.088\n", + "1/1 [==============================] - 1s 553ms/step\n", + "COL: 微孔体积, MSE: 9.10E-02,RMSE: 0.3019999861717224,MAPE: 3142.796 %,MAE: 0.238,R_2: 0.033\n", + "1/1 [==============================] - 1s 559ms/step\n", + "COL: 微孔体积, MSE: 1.26E-01,RMSE: 0.35600000619888306,MAPE: 1105.4 %,MAE: 0.298,R_2: 0.065\n", + "1/1 [==============================] - 1s 681ms/step\n", + "COL: 微孔体积, MSE: 9.03E-02,RMSE: 0.3009999990463257,MAPE: 466.473 %,MAE: 0.226,R_2: 0.026\n", + "平均孔径\n", + "1/1 [==============================] - 1s 612ms/step\n", + "COL: 平均孔径, MSE: 1.35E+00,RMSE: 1.159999966621399,MAPE: 30.229 %,MAE: 0.837,R_2: 0.108\n", + "1/1 [==============================] - 1s 560ms/step\n", + "COL: 平均孔径, MSE: 2.38E+00,RMSE: 1.5420000553131104,MAPE: 37.616 %,MAE: 1.076,R_2: 0.095\n", + "1/1 [==============================] - 1s 571ms/step\n", + "COL: 平均孔径, MSE: 2.05E+00,RMSE: 1.4329999685287476,MAPE: 32.263 %,MAE: 0.996,R_2: 0.03\n", + "1/1 [==============================] - 1s 530ms/step\n", + "COL: 平均孔径, MSE: 1.42E+00,RMSE: 1.1920000314712524,MAPE: 33.395 %,MAE: 0.899,R_2: 0.221\n", + "1/1 [==============================] - 1s 532ms/step\n", + "COL: 平均孔径, MSE: 1.58E+00,RMSE: 1.2589999437332153,MAPE: 33.747 %,MAE: 0.931,R_2: 0.009\n" + ] + } + ], + "source": [ + "for pred_col in out_cols:\n", + " print(pred_col)\n", + " use_cols = feature_cols + [pred_col]\n", + " use_data = train_data[use_cols].dropna().reset_index(drop=True)\n", + " kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + " vad_eva_list = list()\n", + " for (train_index, test_index) in kf.split(use_data):\n", + " train = use_data.loc[train_index]\n", + " valid = use_data.loc[test_index]\n", + " X = np.expand_dims(train[feature_cols].values, axis=1)\n", + " Y = train[pred_col].values.T\n", + " X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n", + " Y_valid = valid[pred_col].values.T\n", + " prediction_model = get_prediction_model()\n", + " prediction_model.compile(optimizer='adam', loss=mean_squared_error)\n", + " hist = prediction_model.fit(X, Y, epochs=120, batch_size=8, verbose=0, \n", + " validation_data=(X_valid, Y_valid),\n", + " callbacks=[reduce_lr]\n", + " )\n", + " rst = prediction_model.predict(X_valid)\n", + " pred_rst = rst * (maxs[pred_col] - mins[pred_col]) + mins[pred_col]\n", + " real_rst = valid[pred_col] * (maxs[pred_col] - mins[pred_col]) + mins[pred_col]\n", + " y_pred_vad = pred_rst.reshape(-1,)\n", + " y_true_vad = real_rst.values.reshape(-1,)\n", + " vad_eva = print_eva(y_true_vad, y_pred_vad, tp=pred_col)\n", + " vad_eva_list.append(vad_eva)\n", + " del prediction_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", + "metadata": {}, + "outputs": [], + "source": [ + "vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", + "vad_df.sort_values(by='R_2').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MSE 0.317628\n", + "RMSE 0.557178\n", + "MAE 0.412263\n", + "MAPE 0.007993\n", + "R_2 0.986373\n", + "dtype: float64" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", + "fcad_df.sort_values(by='R_2').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=1, \n", + " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[0.73740077],\n", + " [0.89292204],\n", + " [0.7599046 ],\n", + " [0.67802393],\n", + " [0.6815233 ],\n", + " [0.88627005],\n", + " [0.6121343 ],\n", + " [0.7072234 ],\n", + " [0.8561135 ],\n", + " [0.52762157],\n", + " [0.8325021 ],\n", + " [0.50241977],\n", + " [0.8242289 ],\n", + " [0.68957335],\n", + " [0.6980361 ],\n", + " [0.82116604],\n", + " [0.8566438 ],\n", + " [0.53687835],\n", + " [0.56832707],\n", + " [0.78476715],\n", + " [0.85638577]], dtype=float32),\n", + " array([[0.68600863],\n", + " [0.78454906],\n", + " [0.8179163 ],\n", + " [0.94351083],\n", + " [0.86383885],\n", + " [0.69705516],\n", + " [0.6913491 ],\n", + " [0.80277354],\n", + " [0.93557894],\n", + " [0.82278305],\n", + " [0.82674253],\n", + " [0.93518937],\n", + " [0.8094449 ],\n", + " [0.9206344 ],\n", + " [0.7747319 ],\n", + " [0.9137207 ],\n", + " [0.9491073 ],\n", + " [0.93225 ],\n", + " [0.6185102 ],\n", + " [0.8867341 ],\n", + " [0.82890105]], dtype=float32)]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", + "rst" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.9991559102070927, 0.9998196796918477]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['挥发分Vad(%)', '固定炭Fcad(%)'], dtype='object')" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "294813b8-90be-4007-9fd6-c26ee7bb9652", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)\n", + "y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)\n", + "y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)\n", + "y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COL: 挥发分Vad, MSE: 3.35E-01,RMSE: 0.579,MAPE: 1.639 %,MAE: 0.504,R_2: 0.87\n", + "COL: 固定炭Fcad, MSE: 1.11E+00,RMSE: 1.055,MAPE: 1.497 %,MAE: 0.814,R_2: 0.876\n" + ] + } + ], + "source": [ + "pm25_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')\n", + "pm10_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac4a4339-ec7d-4266-8197-5276c2395288", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/20240123_煤沥青.ipynb b/20240123_煤沥青.ipynb new file mode 100644 index 0000000..8837322 --- /dev/null +++ b/20240123_煤沥青.ipynb @@ -0,0 +1,622 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4713d33-c5a2-4f49-8aed-873069543bec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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碳源共碳化物质共碳化物/煤沥青加热次数是否有碳化过程模板剂种类模板剂比例KOH与煤沥青比例活化温度升温速率活化时间混合方式比表面积总孔体积微孔体积平均孔径
0煤沥青无0.01否自制氧化钙1.01.050052.0溶剂908.070.400.341.75
1煤沥青无0.01否自制氧化钙1.00.560052.0溶剂953.950.660.352.76
2煤沥青无0.01否自制氧化钙1.01.060052.0溶剂1388.620.610.531.77
3煤沥青无0.01否自制氧化钙1.02.060052.0溶剂1444.630.590.551.62
4煤沥青无0.02是自制碱式碳酸镁1.01.060052.0溶剂1020.990.450.351.77
\n", + "
" + ], + "text/plain": [ + " 碳源 共碳化物质 共碳化物/煤沥青 加热次数 是否有碳化过程 模板剂种类 模板剂比例 KOH与煤沥青比例 活化温度 升温速率 \\\n", + "0 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 500 5 \n", + "1 煤沥青 无 0.0 1 否 自制氧化钙 1.0 0.5 600 5 \n", + "2 煤沥青 无 0.0 1 否 自制氧化钙 1.0 1.0 600 5 \n", + "3 煤沥青 无 0.0 1 否 自制氧化钙 1.0 2.0 600 5 \n", + "4 煤沥青 无 0.0 2 是 自制碱式碳酸镁 1.0 1.0 600 5 \n", + "\n", + " 活化时间 混合方式 比表面积 总孔体积 微孔体积 平均孔径 \n", + "0 2.0 溶剂 908.07 0.40 0.34 1.75 \n", + "1 2.0 溶剂 953.95 0.66 0.35 2.76 \n", + "2 2.0 溶剂 1388.62 0.61 0.53 1.77 \n", + "3 2.0 溶剂 1444.63 0.59 0.55 1.62 \n", + "4 2.0 溶剂 1020.99 0.45 0.35 1.77 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤沥青数据.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1a0903a-596f-4d6f-98b1-a668a26f4175", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 16)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "359c9cc6-2694-46a6-9f18-6361e220542a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['碳源', '共碳化物质', '共碳化物/煤沥青', '加热次数', '是否有碳化过程', '模板剂种类', '模板剂比例',\n", + " 'KOH与煤沥青比例', '活化温度', '升温速率', '活化时间', '混合方式', '比表面积', '总孔体积', '微孔体积',\n", + " '平均孔径'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7ca6d610-060a-4540-bf1d-4f51cc2c55d1", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=['碳源'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['共碳化物质', '是否有碳化过程', '模板剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3368163e-85a1-4487-8078-be51cb5fb560", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积', '平均孔径']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e545ccba-07b2-4c49-bd48-f49b3892fafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(149, 40)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=666)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "42597842-1acb-4263-bdad-bfca7b11bcb5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "params_xgb = {\"objective\": 'reg:squarederror',\n", + " \"subsample\": 0.9,\n", + " \"max_depth\": 20,\n", + " \"eta\": 0.01,\n", + " \"colsample_bytree\": 0.9,}\n", + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 267691.4403, RMSE: 517.3891, MAE: 395.2788, MAPE: 94.67 %, R_2: 0.4467\n", + "MSE: 242169.4062, RMSE: 492.1071, MAE: 353.5184, MAPE: 153.84 %, R_2: 0.7103\n", + "MSE: 337963.1058, RMSE: 581.3459, MAE: 453.5923, MAPE: 368.53 %, R_2: 0.5508\n", + "MSE: 241296.272, RMSE: 491.2192, MAE: 378.0324, MAPE: 36.02 %, R_2: 0.5678\n", + "MSE: 393198.8331, RMSE: 627.0557, MAE: 494.652, MAPE: 424.8 %, R_2: 0.309\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.1984, RMSE: 0.4454, MAE: 0.3543, MAPE: 72.07 %, R_2: 0.616\n", + "MSE: 0.1439, RMSE: 0.3794, MAE: 0.3062, MAPE: 224.83 %, R_2: 0.4173\n", + "MSE: 0.1073, RMSE: 0.3275, MAE: 0.2583, MAPE: 30.27 %, R_2: 0.6678\n", + "MSE: 0.1076, RMSE: 0.3281, MAE: 0.2422, MAPE: 39.55 %, R_2: 0.5426\n", + "MSE: 0.187, RMSE: 0.4324, MAE: 0.3131, MAPE: 389.39 %, R_2: 0.0647\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0303, RMSE: 0.1739, MAE: 0.1339, MAPE: 144.75 %, R_2: 0.6541\n", + "MSE: 0.0652, RMSE: 0.2554, MAE: 0.1954, MAPE: 55.86 %, R_2: 0.1165\n", + "MSE: 0.0546, RMSE: 0.2337, MAE: 0.1888, MAPE: 1337.75 %, R_2: 0.6439\n", + "MSE: 0.0312, RMSE: 0.1765, MAE: 0.1505, MAPE: 43.36 %, R_2: 0.5198\n", + "MSE: 0.0565, RMSE: 0.2377, MAE: 0.1762, MAPE: 496.94 %, R_2: 0.5316\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eva_total = list()\n", + "index_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for col in out_cols:\n", + " eva_list = list()\n", + " train_data = train_data[~train_data[col].isna()].reset_index(drop=True)\n", + " cur_test = list()\n", + " cur_real = list()\n", + " for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[col]\n", + " X_valid, Y_valid = valid[feature_cols], valid[col]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " cur_test.extend(y_pred[:7])\n", + " cur_real.extend(y_true[:7])\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n", + " plt.figure(figsize=(12, 8))\n", + " plt.plot(range(len(cur_test)), cur_real, 'o-', label='real')\n", + " plt.plot(range(len(cur_test)), cur_test, '*-', label='pred')\n", + " plt.legend(loc='best')\n", + " plt.title(f'{col}')\n", + " plt.show()\n", + " eva_total.append(np.mean(eva_list, axis=0))\n", + " index_list.append(f\"{col}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSERMSEMAEMAPER2
比表面积296463.811484541.823393415.0147832.1557170.516912
总孔体积0.1488390.3825570.2948421.5122000.461672
微孔体积0.0475540.2154570.1689624.1573140.493163
平均孔径0.7736110.8447100.5456860.1937520.410705
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" + ], + "text/plain": [ + " MSE RMSE MAE MAPE R2\n", + "比表面积 296463.811484 541.823393 415.014783 2.155717 0.516912\n", + "总孔体积 0.148839 0.382557 0.294842 1.512200 0.461672\n", + "微孔体积 0.047554 0.215457 0.168962 4.157314 0.493163\n", + "平均孔径 0.773611 0.844710 0.545686 0.193752 0.410705" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame.from_records(eva_total, index=index_list, columns=eva_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python38", + "language": "python", + "name": "python38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/20240123_煤炭.ipynb b/20240123_煤炭.ipynb new file mode 100644 index 0000000..370ebde --- /dev/null +++ b/20240123_煤炭.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4713d33-c5a2-4f49-8aed-873069543bec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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灰分(d)挥发分(daf）活化剂种类活化剂比例混合方式活化温度活化时间升温速率比表面积总孔体积微孔体积Unnamed: 11
011.2517.06KOH3.0研磨8001.05.02784.01.08300.853刘宇昊\\n煤基活性炭的制备及其电化学性能研究 学位论文
18.5313.46KOH3.0研磨8001.05.02934.01.22901.074NaN
218.0813.85KOH3.0研磨8001.05.03059.01.30441.011NaN
311.4212.31KOH3.0研磨8001.05.02365.00.80300.605NaN
411.608.49KOH3.0研磨8001.05.02988.01.28200.944NaN
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" + ], + "text/plain": [ + " 灰分(d) 挥发分(daf） 活化剂种类 活化剂比例 混合方式 活化温度 活化时间 升温速率 比表面积 总孔体积 微孔体积 \\\n", + "0 11.25 17.06 KOH 3.0 研磨 800 1.0 5.0 2784.0 1.0830 0.853 \n", + "1 8.53 13.46 KOH 3.0 研磨 800 1.0 5.0 2934.0 1.2290 1.074 \n", + "2 18.08 13.85 KOH 3.0 研磨 800 1.0 5.0 3059.0 1.3044 1.011 \n", + "3 11.42 12.31 KOH 3.0 研磨 800 1.0 5.0 2365.0 0.8030 0.605 \n", + "4 11.60 8.49 KOH 3.0 研磨 800 1.0 5.0 2988.0 1.2820 0.944 \n", + "\n", + " Unnamed: 11 \n", + "0 刘宇昊\\n煤基活性炭的制备及其电化学性能研究 学位论文 \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240123/煤炭数据.xlsx', header=[1])\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1a0903a-596f-4d6f-98b1-a668a26f4175", + "metadata": {}, + "outputs": [], + "source": [ + "data.drop(columns=data.columns[-1], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "359c9cc6-2694-46a6-9f18-6361e220542a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['灰分(d)', '挥发分(daf）', '活化剂种类', '活化剂比例', '混合方式', '活化温度', '活化时间', '升温速率',\n", + " '比表面积', '总孔体积', '微孔体积'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "object_cols = ['活化剂种类', '混合方式']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3368163e-85a1-4487-8078-be51cb5fb560", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.get_dummies(data, columns=object_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比表面积', '总孔体积', '微孔体积']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e545ccba-07b2-4c49-bd48-f49b3892fafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(174, 12)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=6, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "42597842-1acb-4263-bdad-bfca7b11bcb5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "params_xgb = {\"objective\": 'reg:squarederror',\n", + " \"subsample\": 0.8,\n", + " \"max_depth\": 20,\n", + " \"eta\": 0.01,\n", + " \"colsample_bytree\": 0.9,}\n", + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 83933.6379, RMSE: 289.713, MAE: 205.8723, MAPE: 16.71 %, R_2: 0.8599\n", + "MSE: 151368.0568, RMSE: 389.0605, MAE: 331.2811, MAPE: 25.0 %, R_2: 0.8364\n", + "MSE: 179281.5189, RMSE: 423.4165, MAE: 293.9454, MAPE: 16.84 %, R_2: 0.7792\n", + "MSE: 230625.1215, RMSE: 480.2344, MAE: 288.9958, MAPE: 56.39 %, R_2: 0.5948\n", + "MSE: 212246.0972, RMSE: 460.7017, MAE: 312.8322, MAPE: 39.54 %, R_2: 0.6924\n", + "MSE: 231044.2089, RMSE: 480.6706, MAE: 359.0907, MAPE: 18.98 %, R_2: 0.6907\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0309, RMSE: 0.1758, MAE: 0.1127, MAPE: 14.96 %, R_2: 0.8353\n", + "MSE: 0.0477, RMSE: 0.2184, MAE: 0.1858, MAPE: 23.49 %, R_2: 0.8287\n", + "MSE: 0.0656, RMSE: 0.2561, MAE: 0.1692, MAPE: 17.14 %, R_2: 0.8098\n", + "MSE: 0.0338, RMSE: 0.184, MAE: 0.122, MAPE: 18.98 %, R_2: 0.7735\n", + "MSE: 0.0511, RMSE: 0.2261, MAE: 0.1652, MAPE: 36.1 %, R_2: 0.8148\n", + "MSE: 0.0684, RMSE: 0.2615, MAE: 0.192, MAPE: 18.13 %, R_2: 0.7924\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 0.0185, RMSE: 0.1362, MAE: 0.0952, MAPE: 18.49 %, R_2: 0.7329\n", + "MSE: 0.0503, RMSE: 0.2242, MAE: 0.1338, MAPE: 21.21 %, R_2: 0.6868\n", + "MSE: 0.0768, RMSE: 0.2771, MAE: 0.1638, MAPE: 26.24 %, R_2: 0.4523\n", + "MSE: 0.0222, RMSE: 0.1489, MAE: 0.0975, MAPE: 21.11 %, R_2: 0.5256\n", + "MSE: 0.0395, RMSE: 0.1987, MAE: 0.1253, MAPE: 42.88 %, R_2: 0.5666\n", + "MSE: 0.0525, RMSE: 0.229, MAE: 0.1566, MAPE: 23.66 %, R_2: 0.2799\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "eva_total = list()\n", + "index_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for col in out_cols:\n", + " eva_list = list()\n", + " train_data = train_data[~train_data[col].isna()].reset_index(drop=True)\n", + " cur_test = list()\n", + " cur_real = list()\n", + " for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[col]\n", + " X_valid, Y_valid = valid[feature_cols], valid[col]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " cur_test.extend(y_pred[:7])\n", + " cur_real.extend(y_true[:7])\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n", + " plt.figure(figsize=(12, 8))\n", + " plt.plot(range(len(cur_test)), cur_real, 'o-', label='real')\n", + " plt.plot(range(len(cur_test)), cur_test, '*-', label='pred')\n", + " plt.legend(loc='best')\n", + " plt.title(f'{col}')\n", + " plt.show()\n", + " eva_total.append(np.mean(eva_list, axis=0))\n", + " index_list.append(f\"{col}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSERMSEMAEMAPER2
比表面积181416.440212420.632794298.6695740.2891030.742225
总孔体积0.0495920.2203150.1577990.2146820.809092
微孔体积0.0432860.2023550.1287140.2559820.540697
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" + ], + "text/plain": [ + " MSE RMSE MAE MAPE R2\n", + "比表面积 181416.440212 420.632794 298.669574 0.289103 0.742225\n", + "总孔体积 0.049592 0.220315 0.157799 0.214682 0.809092\n", + "微孔体积 0.043286 0.202355 0.128714 0.255982 0.540697" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame.from_records(eva_total, index=index_list, columns=eva_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py38", + "language": "python", + "name": "py38" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/20240617_电容器.html b/20240617_电容器.html new file mode 100644 index 0000000..dca84a8 --- /dev/null +++ b/20240617_电容器.html @@ -0,0 +1,15222 @@ + + + + + +20240617_电容器 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/20240617_电容器.ipynb b/20240617_电容器.ipynb new file mode 100644 index 0000000..4a0c9ce --- /dev/null +++ b/20240617_电容器.ipynb @@ -0,0 +1,464 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "077f5f8a-ffe5-4405-8806-1b5559140a5d", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install pandas hyperopt xgboost scikit-learn matplotlib numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a4713d33-c5a2-4f49-8aed-873069543bec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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比表面积总孔体积微孔体积平均孔径氮掺杂量at氧掺杂量ID/IG电流密度比电容
01141.80.460.421.611.743.841.10.5206.5
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" + ], + "text/plain": [ + " 比表面积 总孔体积 微孔体积 平均孔径 氮掺杂量at 氧掺杂量 ID/IG 电流密度 比电容\n", + "0 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 0.5 206.5\n", + "1 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 1.0 179.1\n", + "2 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 2.0 163.3\n", + "3 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 5.0 146.0\n", + "4 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 10.0 137.8" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_excel('./data/20240617/电容性能新.xlsx')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "24f58281-9f13-49ef-b44d-81d0644d6976", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = ['比电容']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0", + "metadata": {}, + "outputs": [], + "source": [ + "feature_cols = [x for x in data.columns if x not in out_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e4946bd7-ae94-4981-82ed-66e2b496e035", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4109685a-4d5b-4c63-b4e2-eb9db3989d02", + "metadata": {}, + "outputs": [], + "source": [ + "import xgboost as xgb\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a140942f-4206-49e5-a51b-932c55170436", + "metadata": {}, + "outputs": [], + "source": [ + "# 定义超参数的搜索空间\n", + "space = {\n", + " 'eta': hp.loguniform('eta', -5, 0), # 学习率，搜索范围是 [1e-5, 1]\n", + " 'max_depth': hp.choice('max_depth', range(5, 30)), # 树的最大深度，搜索范围是 [1, 10]\n", + " 'min_child_weight': hp.uniform('min_child_weight', 0, 10), # 子节点最小的权重和\n", + " 'gamma': hp.loguniform('gamma', -5, 0), # 叶子节点分裂所需的最小损失减少\n", + " 'subsample': hp.uniform('subsample', 0.5, 1), # 训练集的采样率\n", + " 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1), # 特征的采样率\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7dd2dc64-0e80-4504-b84a-1d549f2cf90d", + "metadata": {}, + "outputs": [], + "source": [ + "# 划分训练集和测试集\n", + "X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], \n", + " train_data[out_cols], \n", + " test_size=0.3, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "68669fde-52ab-4e62-8efc-b60dcf13734b", + "metadata": {}, + "outputs": [], + "source": [ + "# 定义目标函数，用于评估模型的性能\n", + "def objective(params):\n", + " # 创建决策树分类器实例\n", + " gbr = xgb.XGBRegressor(**params)\n", + " # 训练模型\n", + " gbr.fit(X_train, y_train)\n", + " # 使用模型进行预测\n", + " y_pred = gbr.predict(X_test)\n", + " mae = mean_absolute_error(y_test, y_pred)\n", + " return {'loss': mae, 'status': STATUS_OK}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e89097ea-fee2-4298-81a2-ff528688857e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:08<00:00, 11.55trial/s, best loss: 12.132344347686164]\n" + ] + } + ], + "source": [ + "# 创建 Trials 对象来记录搜索历史\n", + "trials = Trials()\n", + "\n", + "# 使用 fmin 函数进行超参数优化\n", + "best_params = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0ccfb873-6f5a-4606-9b17-a63cdbcf8acc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'colsample_bytree': 0.8857035476046763, 'eta': 0.11588664776521924, 'gamma': 0.007847746718601799, 'max_depth': 10, 'min_child_weight': 6.396614191886977, 'subsample': 0.7070880429614513}\n" + ] + } + ], + "source": [ + "print(best_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import KFold, train_test_split\n", + "kf = KFold(n_splits=10, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4", + "metadata": {}, + "outputs": [], + "source": [ + "num_boost_round = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017", + "metadata": {}, + "outputs": [], + "source": [ + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE: 164.2816, RMSE: 12.8172, MAE: 9.1819, MAPE: 4.33 %, R_2: 0.9534\n", + "MSE: 172.8146, RMSE: 13.1459, MAE: 8.4597, MAPE: 4.24 %, R_2: 0.9475\n", + "MSE: 105.637, RMSE: 10.278, MAE: 7.1138, MAPE: 3.19 %, R_2: 0.9736\n", + "MSE: 306.2548, RMSE: 17.5001, MAE: 10.3353, MAPE: 4.27 %, R_2: 0.9348\n", + "MSE: 212.1827, RMSE: 14.5665, MAE: 10.452, MAPE: 4.64 %, R_2: 0.9467\n", + "MSE: 311.2193, RMSE: 17.6414, MAE: 10.62, MAPE: 3.97 %, R_2: 0.929\n", + "MSE: 479.0079, RMSE: 21.8862, MAE: 11.6752, MAPE: 5.11 %, R_2: 0.8952\n", + "MSE: 153.6563, RMSE: 12.3958, MAE: 8.8708, MAPE: 4.44 %, R_2: 0.9502\n", + "MSE: 285.905, RMSE: 16.9087, MAE: 10.4152, MAPE: 5.35 %, R_2: 0.9522\n", + "MSE: 570.9538, RMSE: 23.8946, MAE: 12.4216, MAPE: 5.98 %, R_2: 0.8954\n" + ] + } + ], + "source": [ + "eva_list = list()\n", + "eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\n", + "for (train_index, test_index) in kf.split(train_data):\n", + " train = train_data.loc[train_index]\n", + " valid = train_data.loc[test_index]\n", + " X_train, Y_train = train[feature_cols], train[out_cols]\n", + " X_valid, Y_valid = valid[feature_cols], valid[out_cols]\n", + " dtrain = xgb.DMatrix(X_train, Y_train)\n", + " dvalid = xgb.DMatrix(X_valid, Y_valid)\n", + " watchlist = [(dvalid, 'eval')]\n", + " gb_model = xgb.train(best_params, dtrain, num_boost_round, evals=watchlist,\n", + " early_stopping_rounds=100, verbose_eval=False)\n", + " y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n", + " y_true = Y_valid.values\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print('MSE:', round(MSE, 4), end=', ')\n", + " print('RMSE:', round(RMSE, 4), end=', ')\n", + " print('MAE:', round(MAE, 4), end=', ')\n", + " print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n", + " print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n", + " eva_list.append([MSE, RMSE, MAE, MAPE, R_2])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "844d8b9f-a820-4d59-85f5-df434ca3da8d", + "metadata": {}, + "outputs": [], + "source": [ + "eva_df = pd.DataFrame.from_records(eva_list, columns=eva_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6c7c4910-81a2-4703-948a-152ccc7b859d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MSE 276.191297\n", + "RMSE 16.103459\n", + "MAE 9.954548\n", + "MAPE 0.045525\n", + "R2 0.937810\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eva_df.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12, 8))\n", + "plt.plot(range(len(y_true)), y_true, 'o-', label='real')\n", + "plt.plot(range(len(y_pred)), y_pred, '*-', label='pred')\n", + "plt.legend(loc='best')\n", + "plt.title(f'{out_cols}')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b69f612e-3548-41d6-9512-7591c47ca50e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/CBA_4feature.ipynb b/CBA_4feature.ipynb index 43d01bb..e078751 100644 --- a/CBA_4feature.ipynb +++ b/CBA_4feature.ipynb @@ -1432,7 +1432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/CBA_vad_fcad.ipynb b/CBA_vad_fcad.ipynb index 3c4d394..adec589 100644 --- a/CBA_vad_fcad.ipynb +++ b/CBA_vad_fcad.ipynb @@ -495,8 +495,10 @@ "metadata": {}, "outputs": [], "source": [ - "out_cols = ['挥发分Vad(%)']\n", - "# out_cols = ['固定炭Fcad(%)']" + "# out_cols = ['挥发分Vad(%)']\n", + "# drop_cols = ['化验编号', '固定炭Fcad(%)']\n", + "out_cols = ['固定炭Fcad(%)']\n", + "drop_cols = ['挥发分Vad(%)', '化验编号']" ] }, { @@ -508,7 +510,7 @@ { "data": { "text/plain": [ - "['挥发分Vad(%)']" + "['固定炭Fcad(%)']" ] }, "execution_count": 8, @@ -550,7 +552,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 17:02:16.953831: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-01-08 18:09:21.597754: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], @@ -661,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 17, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], @@ -684,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "372011ea-9876-41eb-a4e6-83ccd6c71559", "metadata": {}, "outputs": [], @@ -694,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", "metadata": {}, "outputs": [], @@ -709,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", "metadata": {}, "outputs": [ @@ -781,7 +783,7 @@ "1 0.674897 0.794606 " ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -792,19 +794,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", "metadata": {}, "outputs": [], "source": [ "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", - "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols and x not in drop_cols]\n", "use_cols = feature_cols + out_cols" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", "metadata": {}, "outputs": [], @@ -816,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "50daf170-efec-49e5-8f8e-9a45938cacfc", "metadata": {}, "outputs": [], @@ -827,7 +829,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "0f863423-be12-478b-a08d-e3c6f5dfb8ee", "metadata": {}, "outputs": [], @@ -839,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "2c89b32a-017c-4d05-ab78-8b9b8eb0dcbb", "metadata": {}, "outputs": [], @@ -850,34 +852,49 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 26, "id": "ae24eea7-7dc1-4e33-9d41-3baff07ebb88", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:09:35.590279: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-08 18:09:35.656503: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-08 18:09:35.656548: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-08 18:09:35.656557: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-08 18:09:35.656758: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-08 18:09:35.656795: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-08 18:09:35.656802: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n", + "2024-01-08 18:09:35.657280: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"model_2\"\n", + "Model: \"model\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "input (InputLayer) [(None, 1, 7)] 0 \n", + "input (InputLayer) [(None, 1, 5)] 0 \n", "_________________________________________________________________\n", - "conv1d_3 (Conv1D) (None, 1, 64) 512 \n", + "conv1d (Conv1D) (None, 1, 64) 384 \n", "_________________________________________________________________\n", - "bidirectional_3 (Bidirection (None, 1, 128) 66048 \n", + "bidirectional (Bidirectional (None, 1, 128) 66048 \n", "_________________________________________________________________\n", - "dense_5 (Dense) (None, 1, 128) 16512 \n", + "dense (Dense) (None, 1, 128) 16512 \n", "_________________________________________________________________\n", - "dropout_3 (Dropout) (None, 1, 128) 0 \n", + "dropout (Dropout) (None, 1, 128) 0 \n", "_________________________________________________________________\n", - "dense_6 (Dense) (None, 1, 64) 8256 \n", + "dense_1 (Dense) (None, 1, 64) 8256 \n", "_________________________________________________________________\n", "vad (Dense) (None, 1, 1) 65 \n", "=================================================================\n", - "Total params: 91,393\n", - "Trainable params: 91,393\n", + "Total params: 91,265\n", + "Trainable params: 91,265\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -890,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "ca6ce434-80b6-4609-9596-9a5120680462", "metadata": {}, "outputs": [], @@ -911,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "503bbec7-2020-44c8-b622-05bb41082e43", "metadata": {}, "outputs": [], @@ -921,22 +938,30 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 30, "id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:03:50.956250: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-01-08 18:03:50.974801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "COL: 挥发分Vad, MSE: 2.49E-01,RMSE: 0.499,MAPE: 1.336 %,MAE: 0.398,R_2: 0.946\n", - "COL: 挥发分Vad, MSE: 3.81E-01,RMSE: 0.617,MAPE: 1.597 %,MAE: 0.455,R_2: 0.954\n", - "COL: 挥发分Vad, MSE: 5.71E-01,RMSE: 0.756,MAPE: 2.077 %,MAE: 0.621,R_2: 0.854\n", - "WARNING:tensorflow:5 out of the last 45 calls to .predict_function at 0x7f00004145e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 3.24E-01,RMSE: 0.569,MAPE: 1.575 %,MAE: 0.46,R_2: 0.943\n", - "WARNING:tensorflow:6 out of the last 47 calls to .predict_function at 0x7f0165b81e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 3.13E-01,RMSE: 0.56,MAPE: 1.548 %,MAE: 0.466,R_2: 0.929\n", - "COL: 挥发分Vad, MSE: 4.94E-01,RMSE: 0.703,MAPE: 1.852 %,MAE: 0.539,R_2: 0.898\n" + "COL: 挥发分Vad, MSE: 5.84E-01,RMSE: 0.764,MAPE: 2.111 %,MAE: 0.633,R_2: 0.874\n", + "COL: 挥发分Vad, MSE: 1.06E+00,RMSE: 1.028,MAPE: 2.941 %,MAE: 0.869,R_2: 0.872\n", + "COL: 挥发分Vad, MSE: 6.70E-01,RMSE: 0.819,MAPE: 2.217 %,MAE: 0.658,R_2: 0.829\n", + "COL: 挥发分Vad, MSE: 5.96E-01,RMSE: 0.772,MAPE: 2.07 %,MAE: 0.607,R_2: 0.896\n", + "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7f6e8d6f8940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 挥发分Vad, MSE: 8.56E-01,RMSE: 0.925,MAPE: 2.335 %,MAE: 0.717,R_2: 0.805\n", + "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7f6e8f6e4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 挥发分Vad, MSE: 7.24E-01,RMSE: 0.851,MAPE: 2.435 %,MAE: 0.713,R_2: 0.851\n" ] } ], @@ -976,20 +1001,26 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "f7132465-89e9-4193-829b-c6e7606cd266", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:09:42.506363: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-01-08 18:09:42.522809: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "COL: 固定炭Fcad, MSE: 1.75E-01,RMSE: 0.419,MAPE: 0.639 %,MAE: 0.339,R_2: 0.993\n", - "COL: 固定炭Fcad, MSE: 2.85E-01,RMSE: 0.534,MAPE: 0.822 %,MAE: 0.386,R_2: 0.994\n", - "COL: 固定炭Fcad, MSE: 2.23E-01,RMSE: 0.472,MAPE: 0.609 %,MAE: 0.344,R_2: 0.984\n", - "COL: 固定炭Fcad, MSE: 1.89E-01,RMSE: 0.435,MAPE: 0.662 %,MAE: 0.318,R_2: 0.994\n", - "COL: 固定炭Fcad, MSE: 2.94E-01,RMSE: 0.542,MAPE: 0.842 %,MAE: 0.446,R_2: 0.986\n", - "COL: 固定炭Fcad, MSE: 2.30E-01,RMSE: 0.48,MAPE: 0.741 %,MAE: 0.386,R_2: 0.99\n" + "COL: 固定炭Fcad, MSE: 7.16E-01,RMSE: 0.846,MAPE: 1.337 %,MAE: 0.715,R_2: 0.972\n", + "COL: 固定炭Fcad, MSE: 1.04E+00,RMSE: 1.018,MAPE: 1.65 %,MAE: 0.847,R_2: 0.98\n", + "COL: 固定炭Fcad, MSE: 8.89E-01,RMSE: 0.943,MAPE: 1.294 %,MAE: 0.724,R_2: 0.936\n", + "COL: 固定炭Fcad, MSE: 5.17E-01,RMSE: 0.719,MAPE: 1.066 %,MAE: 0.545,R_2: 0.985\n" ] } ], @@ -1025,22 +1056,22 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 32, "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MSE 0.388723\n", - "RMSE 0.617294\n", - "MAE 0.489930\n", - "MAPE 0.016641\n", - "R_2 0.920706\n", + "MSE 0.747816\n", + "RMSE 0.859839\n", + "MAE 0.699474\n", + "MAPE 0.023513\n", + "R_2 0.854338\n", "dtype: float64" ] }, - "execution_count": 66, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1052,26 +1083,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MSE 0.232791\n", - "RMSE 0.480288\n", - "MAE 0.369610\n", - "MAPE 0.007189\n", - "R_2 0.990404\n", - "dtype: float64" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", "fcad_df.sort_values(by='R_2').mean()" @@ -1308,7 +1323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/TEST.csv b/TEST.csv new file mode 100644 index 0000000..c3bffe2 --- /dev/null +++ b/TEST.csv @@ -0,0 +1,150 @@ +,共碳化物/煤沥青,加热次数,模板剂比例,KOH与煤沥青比例,活化温度,升温速率,活化时间,共碳化物质_2-甲基咪唑,共碳化物质_三聚氰胺,共碳化物质_尿素,共碳化物质_无,共碳化物质_硫酸铵,共碳化物质_聚磷酸铵,是否有碳化过程_否,是否有碳化过程_是,模板剂种类_Al2O3,模板剂种类_TiO2,模板剂种类_α-Fe2O3,模板剂种类_γ-Fe2O3,模板剂种类_二氧化硅,模板剂种类_无,模板剂种类_氯化钾,模板剂种类_纤维素,模板剂种类_自制氢氧化镁,模板剂种类_自制氧化钙,模板剂种类_自制氧化锌,模板剂种类_自制氧化镁,模板剂种类_自制碱式碳酸镁,模板剂种类_购买氢氧化镁,模板剂种类_购买氧化钙,模板剂种类_购买氧化锌,模板剂种类_购买氧化镁,模板剂种类_购买氯化钠,模板剂种类_购买碳酸钙,混合方式_溶剂,混合方式_研磨,比表面积 +0,0.0,0.0,0.1,0.06666667,0.0,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.2734374 +1,0.0,0.0,0.1,0.033333335,0.16666667,0.3,0.33333334,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.28734466 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matplotlib.pyplot as plt\n", + "#新增加的两行\n", + "from pylab import mpl\n", + "# 设置显示中文字体\n", + "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n", + "\n", + "mpl.rcParams[\"axes.unicode_minus\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('./data/20240102/train_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", + "metadata": {}, + "outputs": [], + "source": [ + "out_cols = [x for x in data.columns if '碳材料' in x]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "metadata": {}, + "outputs": [], + "source": [ + "train_data = data.dropna(subset=out_cols).fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-04 16:22:35.199530: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import tensorflow.keras.backend as K" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c2318ce6-60d2-495c-91cd-67ca53609cf8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /tmp/ipykernel_44444/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.config.list_physical_devices('GPU')` instead.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-04 16:22:36.097926: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-01-04 16:22:36.142225: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-04 16:22:36.232036: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-04 16:22:36.232061: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-04 16:22:36.232065: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-04 16:22:36.232185: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-04 16:22:36.232204: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-04 16:22:36.232207: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.test.is_gpu_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", + "metadata": {}, + "outputs": [], + "source": [ + "class TransformerBlock(layers.Layer):\n", + " def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1):\n", + " super().__init__()\n", + " self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name)\n", + " self.ffn = keras.Sequential(\n", + " [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n", + " )\n", + " self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n", + " self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n", + " self.dropout1 = layers.Dropout(rate)\n", + " self.dropout2 = layers.Dropout(rate)\n", + "\n", + " def call(self, inputs, training):\n", + " attn_output = self.att(inputs, inputs)\n", + " attn_output = self.dropout1(attn_output, training=training)\n", + " out1 = self.layernorm1(inputs + attn_output)\n", + " ffn_output = self.ffn(out1)\n", + " ffn_output = self.dropout2(ffn_output, training=training)\n", + " return self.layernorm2(out1 + ffn_output)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.initializers import Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", + "metadata": {}, + "outputs": [], + "source": [ + "# Custom loss layer\n", + "class CustomMultiLossLayer(layers.Layer):\n", + " def __init__(self, nb_outputs=2, **kwargs):\n", + " self.nb_outputs = nb_outputs\n", + " self.is_placeholder = True\n", + " super(CustomMultiLossLayer, self).__init__(**kwargs)\n", + " \n", + " def build(self, input_shape=None):\n", + " # initialise log_vars\n", + " self.log_vars = []\n", + " for i in range(self.nb_outputs):\n", + " self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,),\n", + " initializer=tf.initializers.he_normal(), trainable=True)]\n", + " super(CustomMultiLossLayer, self).build(input_shape)\n", + "\n", + " def multi_loss(self, ys_true, ys_pred):\n", + " assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs\n", + " loss = 0\n", + " for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars):\n", + " mse = (y_true - y_pred) ** 2.\n", + " pre = K.exp(-log_var[0])\n", + " loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1))\n", + " return K.mean(loss)\n", + "\n", + " def call(self, inputs):\n", + " ys_true = inputs[:self.nb_outputs]\n", + " ys_pred = inputs[self.nb_outputs:]\n", + " loss = self.multi_loss(ys_true, ys_pred)\n", + " self.add_loss(loss, inputs=inputs)\n", + " # We won't actually use the output.\n", + " return K.concatenate(inputs, -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a190207e-5a59-4813-9660-758760cf1b73", + "metadata": {}, + "outputs": [], + "source": [ + "num_heads, ff_dim = 1, 12" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", + "metadata": {}, + "outputs": [], + "source": [ + "def get_prediction_model():\n", + " def build_output(out, out_name):\n", + " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " out = self_block(out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(32, activation=\"relu\")(out)\n", + " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", + " return out\n", + " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", + " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", + " # x = layers.Dropout(rate=0.1)(x)\n", + " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", + " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", + " out = transformer_block(lstm_out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(64, activation='relu')(out)\n", + " out = K.expand_dims(out, axis=1)\n", + "\n", + " bet = build_output(out, 'bet')\n", + " mesco = build_output(out, 'mesco')\n", + " micro = build_output(out, 'micro')\n", + " avg = build_output(out, 'avg')\n", + "\n", + " bet = layers.Dense(1, activation='sigmoid', name='bet')(bet)\n", + " mesco = layers.Dense(1, activation='sigmoid', name='mesco')(mesco)\n", + " micro = layers.Dense(1, activation='sigmoid', name='micro')(micro)\n", + " avg = layers.Dense(1, activation='sigmoid', name='avg')(avg)\n", + "\n", + " model = Model(inputs=[inputs], outputs=[bet, mesco, micro, avg])\n", + " return model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "264001b1-5e4a-4786-96fd-2b5c70ab3212", + "metadata": {}, + "outputs": [], + "source": [ + "def get_trainable_model(prediction_model):\n", + " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", + " bet, mesco, micro, avg = prediction_model(inputs)\n", + " bet_real = layers.Input(shape=(1,), name='bet_real')\n", + " mesco_real = layers.Input(shape=(1,), name='mesco_real')\n", + " micro_real = layers.Input(shape=(1,), name='micro_real')\n", + " avg_real = layers.Input(shape=(1,), name='avg_real')\n", + " out = CustomMultiLossLayer(nb_outputs=4)([bet_real, mesco_real, micro_real, avg_real, bet, mesco, micro, avg])\n", + " return Model([inputs, bet_real, mesco_real, micro_real, avg_real], out)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", + "metadata": {}, + "outputs": [], + "source": [ + "maxs = train_data.max()\n", + "mins = train_data.min()\n", + "for col in train_data.columns:\n", + " if maxs[col] - mins[col] == 0:\n", + " continue\n", + " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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热处理条件-热处理次数热处理条件-是否是中温停留第一次热处理-温度第一次热处理-升温速率第一次热处理-保留时间第二次热处理-温度第二次热处理-升温速率·第二次热处理-保留时间共碳化-是否是共碳化物质共碳化-共碳化物质/沥青...模板剂-种类_二氧化硅模板剂-种类_氢氧化镁模板剂-种类_氧化钙模板剂-种类_氧化锌模板剂-种类_氧化镁模板剂-种类_氯化钠模板剂-种类_氯化钾模板剂-种类_碱式碳酸镁模板剂-种类_碳酸钙模板剂-种类_纤维素
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..................................................................
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" + ], + "text/plain": [ + " 热处理条件-热处理次数 热处理条件-是否是中温停留 第一次热处理-温度 第一次热处理-升温速率 第一次热处理-保留时间 \\\n", + "0 0.0 0.0 0.166667 0.3 0.5 \n", + "1 0.0 0.0 0.333333 0.3 0.5 \n", + "2 0.0 0.0 0.333333 0.3 0.5 \n", + "3 0.0 0.0 0.333333 0.3 0.5 \n", + "4 1.0 0.0 0.166667 0.3 0.5 \n", + ".. ... ... ... ... ... \n", + "144 0.0 0.0 0.333333 0.3 0.0 \n", + "145 0.0 0.0 0.500000 0.3 0.0 \n", + "146 0.0 0.0 0.666667 0.3 0.0 \n", + "147 0.0 0.0 0.500000 0.3 0.0 \n", + "148 0.0 0.0 0.500000 0.3 0.0 \n", + "\n", + " 第二次热处理-温度 第二次热处理-升温速率· 第二次热处理-保留时间 共碳化-是否是共碳化物质 共碳化-共碳化物质/沥青 ... \\\n", + "0 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "1 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "2 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "3 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "4 0.666667 0.5 0.666667 0.0 0.0 ... \n", + ".. ... ... ... ... ... ... \n", + "144 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "145 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "146 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "147 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "148 0.000000 0.0 0.000000 0.0 0.0 ... \n", + "\n", + " 模板剂-种类_二氧化硅 模板剂-种类_氢氧化镁 模板剂-种类_氧化钙 模板剂-种类_氧化锌 模板剂-种类_氧化镁 模板剂-种类_氯化钠 \\\n", + "0 0 0.0 1.0 0 0.0 0.0 \n", + "1 0 0.0 1.0 0 0.0 0.0 \n", + "2 0 0.0 1.0 0 0.0 0.0 \n", + "3 0 0.0 1.0 0 0.0 0.0 \n", + "4 0 0.0 0.0 0 0.0 0.0 \n", + ".. ... ... ... ... ... ... \n", + "144 0 0.0 0.0 0 0.0 0.0 \n", + "145 0 0.0 0.0 0 0.0 0.0 \n", + "146 0 0.0 0.0 0 0.0 0.0 \n", + "147 0 0.0 0.0 0 0.0 0.0 \n", + "148 0 0.0 0.0 0 0.0 0.0 \n", + "\n", + " 模板剂-种类_氯化钾 模板剂-种类_碱式碳酸镁 模板剂-种类_碳酸钙 模板剂-种类_纤维素 \n", + "0 0 0.0 0.0 0.0 \n", + "1 0 0.0 0.0 0.0 \n", + "2 0 0.0 0.0 0.0 \n", + "3 0 0.0 0.0 0.0 \n", + "4 0 1.0 0.0 0.0 \n", + ".. ... ... ... ... \n", + "144 0 0.0 0.0 0.0 \n", + "145 0 0.0 0.0 0.0 \n", + "146 0 0.0 0.0 0.0 \n", + "147 0 0.0 0.0 0.0 \n", + "148 0 0.0 0.0 0.0 \n", + "\n", + "[123 rows x 42 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", + "metadata": {}, + "outputs": [], + "source": [ + "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "use_cols = feature_cols + out_cols" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", + "metadata": {}, + "outputs": [], + "source": [ + "use_data = train_data.copy()\n", + "for col in use_cols:\n", + " use_data[col] = use_data[col].astype('float32')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input (InputLayer) [(None, 1, 38)] 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d (Conv1D) (None, 1, 64) 2496 input[0][0] \n", + "__________________________________________________________________________________________________\n", + "bidirectional (Bidirectional) (None, 1, 128) 66048 conv1d[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense (Dense) (None, 1, 128) 16512 bidirectional[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block (TransformerB (None, 1, 128) 69772 dense[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d (Globa (None, 128) 0 transformer_block[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_2 (Dropout) (None, 128) 0 global_average_pooling1d[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_3 (Dense) (None, 64) 8256 dropout_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "tf.expand_dims (TFOpLambda) (None, 1, 64) 0 dense_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_1 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_2 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_3 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_4 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_1 (Glo (None, 64) 0 transformer_block_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_2 (Glo (None, 64) 0 transformer_block_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_3 (Glo (None, 64) 0 transformer_block_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_4 (Glo (None, 64) 0 transformer_block_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_5 (Dropout) (None, 64) 0 global_average_pooling1d_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_8 (Dropout) (None, 64) 0 global_average_pooling1d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_11 (Dropout) (None, 64) 0 global_average_pooling1d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_14 (Dropout) (None, 64) 0 global_average_pooling1d_4[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 32) 2080 dropout_5[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 32) 2080 dropout_8[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 32) 2080 dropout_11[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_15 (Dense) (None, 32) 2080 dropout_14[0][0] \n", + "__________________________________________________________________________________________________\n", + "bet (Dense) (None, 1) 33 dense_6[0][0] \n", + "__________________________________________________________________________________________________\n", + "mesco (Dense) (None, 1) 33 dense_9[0][0] \n", + "__________________________________________________________________________________________________\n", + "micro (Dense) (None, 1) 33 dense_12[0][0] \n", + "__________________________________________________________________________________________________\n", + "avg (Dense) (None, 1) 33 dense_15[0][0] \n", + "==================================================================================================\n", + "Total params: 245,568\n", + "Trainable params: 245,568\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "prediction_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9289f452-a5a4-40c4-b942-f6cb2e348548", + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import optimizers\n", + "from tensorflow.python.keras.utils.vis_utils import plot_model" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9a62dea1-4f05-411b-9756-a91623580581", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import ReduceLROnPlateau\n", + "reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/40\n", + "11/11 [==============================] - 6s 108ms/step - loss: 0.0316 - val_loss: 0.0835\n", + "Epoch 2/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0281 - val_loss: 0.0958\n", + "Epoch 3/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0278 - val_loss: 0.0891\n", + "Epoch 4/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0233 - val_loss: 0.0912\n", + "Epoch 5/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0215 - val_loss: 0.1023\n", + "Epoch 6/40\n", + "11/11 [==============================] - 0s 33ms/step - loss: 0.0348 - val_loss: 0.0864\n", + "Epoch 7/40\n", + "11/11 [==============================] - 0s 16ms/step - loss: 0.0207 - val_loss: 0.0823\n", + "Epoch 8/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0222 - val_loss: 0.0883\n", + "Epoch 9/40\n", + "11/11 [==============================] - 0s 22ms/step - loss: 0.0258 - val_loss: 0.1029\n", + "Epoch 10/40\n", + "11/11 [==============================] - 0s 26ms/step - loss: 0.0288 - val_loss: 0.0857\n", + "Epoch 11/40\n", + "11/11 [==============================] - 0s 22ms/step - loss: 0.0249 - val_loss: 0.0880\n", + "Epoch 12/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0219 - val_loss: 0.0882\n", + "Epoch 13/40\n", + "11/11 [==============================] - 0s 24ms/step - loss: 0.0191 - val_loss: 0.0873\n", + "Epoch 14/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0187 - val_loss: 0.0929\n", + "Epoch 15/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0183 - val_loss: 0.0988\n", + "Epoch 16/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0189 - val_loss: 0.0905\n", + "Epoch 17/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0209 - val_loss: 0.0823\n", + "Epoch 18/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0185 - val_loss: 0.0834\n", + "Epoch 19/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0177 - val_loss: 0.0916\n", + "Epoch 20/40\n", + "11/11 [==============================] - 0s 24ms/step - loss: 0.0163 - val_loss: 0.0919\n", + "Epoch 21/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0141 - val_loss: 0.0898\n", + "Epoch 22/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0144 - val_loss: 0.0923\n", + "Epoch 23/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0138 - val_loss: 0.0906\n", + "Epoch 24/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0140 - val_loss: 0.0897\n", + "Epoch 25/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0126 - val_loss: 0.0892\n", + "Epoch 26/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0918\n", + "Epoch 27/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0123 - val_loss: 0.0935\n", + "Epoch 28/40\n", + "11/11 [==============================] - 0s 25ms/step - loss: 0.0131 - val_loss: 0.0933\n", + "Epoch 29/40\n", + "11/11 [==============================] - 0s 17ms/step - loss: 0.0125 - val_loss: 0.0933\n", + "Epoch 30/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0932\n", + "Epoch 31/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0936\n", + "Epoch 32/40\n", + "11/11 [==============================] - 0s 28ms/step - loss: 0.0114 - val_loss: 0.0933\n", + "Epoch 33/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0122 - val_loss: 0.0932\n", + "Epoch 34/40\n", + "11/11 [==============================] - 0s 21ms/step - loss: 0.0114 - val_loss: 0.0936\n", + "Epoch 35/40\n", + "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0938\n", + "Epoch 36/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0118 - val_loss: 0.0937\n", + "Epoch 37/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0127 - val_loss: 0.0937\n", + "Epoch 38/40\n", + "11/11 [==============================] - 0s 27ms/step - loss: 0.0123 - val_loss: 0.0937\n", + "Epoch 39/40\n", + "11/11 [==============================] - 0s 19ms/step - loss: 0.0124 - val_loss: 0.0937\n", + "Epoch 40/40\n", + "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0937\n" + ] + } + ], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1], Y[2], Y[3]], epochs=40, batch_size=8, verbose=1, \n", + " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1], Y_valid[2], Y_valid[3]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[0.8401114 ],\n", + " [0.4296295 ],\n", + " [0.34763122],\n", + " [0.33006623],\n", + " [0.74300694],\n", + " [0.48508543],\n", + " [0.48184243],\n", + " [0.7309267 ],\n", + " [0.5264127 ],\n", + " [0.7570494 ],\n", + " [0.29492375],\n", + " [0.34379733]], dtype=float32),\n", + " array([[0.9495956 ],\n", + " [0.19964108],\n", + " [0.25691378],\n", + " [0.15781167],\n", + " [0.39773428],\n", + " [0.257546 ],\n", + " [0.2265681 ],\n", + " [0.39088207],\n", + " [0.30309337],\n", + " [0.4006669 ],\n", + " [0.16448957],\n", + " [0.20928389]], dtype=float32),\n", + " array([[0.93163174],\n", + " 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] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", + "metadata": {}, + "outputs": [], + "source": [ + "real_rst = test[out_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径'], dtype='object')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_pm25 = pred_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", + "y_pred_pm10 = pred_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", + "y_pred_so2 = pred_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", + "y_pred_no2 = pred_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)\n", + "y_true_pm25 = real_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", + "y_true_pm10 = real_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", + "y_true_so2 = real_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", + "y_true_no2 = real_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "26ea6cfa-efad-443c-9dd9-844f8be42b91", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "28072e7c-c9d5-4ff6-940d-e94ae879afc9", + "metadata": {}, + "outputs": [], + "source": [ + "def print_eva(y_true, y_pred, tp):\n", + " MSE = mean_squared_error(y_true, y_pred)\n", + " RMSE = np.sqrt(MSE)\n", + " MAE = mean_absolute_error(y_true, y_pred)\n", + " MAPE = mean_absolute_percentage_error(y_true, y_pred)\n", + " R_2 = r2_score(y_true, y_pred)\n", + " print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n", + " print(f'RMSE: {round(RMSE, 4)}', end=',')\n", + " print(f'MAPE: {round(MAPE, 4) * 100} %', end=',')\n", + " print(f'MAE: {round(MAE, 4)}', end=',')\n", + " print(f'R_2: {round(R_2, 4)}')\n", + " return [MSE, RMSE, MAE, MAPE, R_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COL: 比表面积, MSE: 2.36E+05,RMSE: 485.5891,MAPE: 25.86 %,MAE: 340.8309,R_2: -0.1091\n", + "COL: 总孔体积, MSE: 5.15E-02,RMSE: 0.2268,MAPE: 23.810000000000002 %,MAE: 0.1519,R_2: 0.7657\n", + "COL: 微孔体积, MSE: 4.53E-02,RMSE: 0.2128,MAPE: 34.75 %,MAE: 0.1536,R_2: -0.0412\n", + "COL: 平均孔径, MSE: 4.63E-01,RMSE: 0.6802,MAPE: 15.620000000000001 %,MAE: 0.415,R_2: 0.5929\n" + ] + } + ], + "source": [ + "pm25_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", + "pm10_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", + "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", + "nox_eva = print_eva(y_true_no2, y_pred_no2, tp='平均孔径')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac4a4339-ec7d-4266-8197-5276c2395288", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/multi-task-NN.ipynb b/multi-task-NN.ipynb index 3799b18..7f2fd05 100644 --- a/multi-task-NN.ipynb +++ b/multi-task-NN.ipynb @@ -7,14 +7,12 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'\n", - "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'" + "import os" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d", "metadata": {}, "outputs": [], @@ -33,58 +31,312 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17", "metadata": {}, "outputs": [], "source": [ - "data = pd.read_csv('./data/20240102/train_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", - "metadata": {}, - "outputs": [], - "source": [ - "out_cols = [x for x in data.columns if '碳材料' in x]" + "data = pd.read_excel('./data/20240123/煤炭数据.xlsx', header=[1])\n", + "data.drop(columns=data.columns[11:], inplace=True)" ] }, { "cell_type": "code", "execution_count": 5, - "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", + "id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "out_cols" + "object_cols = ['活化剂种类', '混合方式']\n", + "data = pd.get_dummies(data, columns=object_cols)" ] }, { "cell_type": "code", "execution_count": 6, - "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d", "metadata": {}, "outputs": [], "source": [ - "train_data = data.dropna(subset=out_cols).fillna(0)" + "out_cols = ['比表面积', '总孔体积', '微孔体积']\n", + "feature_cols = [x for x in data.columns if x not in out_cols]" ] }, { "cell_type": "code", "execution_count": 7, + "id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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灰分(d)挥发分(daf）活化剂比例活化温度活化时间升温速率比表面积总孔体积微孔体积活化剂种类_KOH混合方式_浸渍混合方式_研磨
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" + ], + "text/plain": [ + " 灰分(d) 挥发分(daf） 活化剂比例 活化温度 活化时间 升温速率 比表面积 总孔体积 微孔体积 \\\n", + "0 11.25 17.06 3.0 800 1.0 5.0 2784.0 1.0830 0.853 \n", + "1 8.53 13.46 3.0 800 1.0 5.0 2934.0 1.2290 1.074 \n", + "2 18.08 13.85 3.0 800 1.0 5.0 3059.0 1.3044 1.011 \n", + "3 11.42 12.31 3.0 800 1.0 5.0 2365.0 0.8030 0.605 \n", + "4 11.60 8.49 3.0 800 1.0 5.0 2988.0 1.2820 0.944 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "153 4.18 9.77 1.5 800 1.0 5.0 1772.0 0.7383 0.660 \n", + "154 4.18 9.77 2.0 800 1.0 5.0 2382.0 1.0370 0.899 \n", + "155 4.18 9.77 2.5 800 1.0 5.0 2996.0 1.3520 1.162 \n", + "156 4.18 9.77 3.0 800 1.0 5.0 3142.0 1.6080 1.204 \n", + "157 4.18 9.77 3.5 800 1.0 5.0 3389.0 2.0410 1.022 \n", + "\n", + " 活化剂种类_KOH 混合方式_浸渍 混合方式_研磨 \n", + "0 1 0 1 \n", + "1 1 0 1 \n", + "2 1 0 1 \n", + "3 1 0 1 \n", + "4 1 0 1 \n", + ".. ... ... ... \n", + "153 1 0 1 \n", + "154 1 0 1 \n", + "155 1 0 1 \n", + "156 1 0 1 \n", + "157 1 0 1 \n", + "\n", + "[158 rows x 12 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_data = data.dropna(subset=out_cols).ffill().reset_index(drop=True)\n", + "train_data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7424096a-1283-46aa-a5a8-909ad3d60d9b", + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c7b2cb8d-18bb-489a-af0c-29d80c719aa4", + "metadata": {}, + "outputs": [], + "source": [ + "train_data['比表面积'] = np.log1p(train_data['比表面积'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451", "metadata": {}, "outputs": [ @@ -92,65 +344,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-04 16:22:35.199530: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-04-08 11:13:19.810980: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], "source": [ "import tensorflow as tf\n", - "from tensorflow import keras\n", - "from tensorflow.keras import layers\n", - "import tensorflow.keras.backend as K" + "import keras\n", + "from keras import layers\n", + "import keras.backend as K" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "c2318ce6-60d2-495c-91cd-67ca53609cf8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /tmp/ipykernel_44444/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use `tf.config.list_physical_devices('GPU')` instead.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-04 16:22:36.097926: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2024-01-04 16:22:36.142225: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", - "2024-01-04 16:22:36.232036: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", - "2024-01-04 16:22:36.232061: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", - "2024-01-04 16:22:36.232065: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", - "2024-01-04 16:22:36.232185: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", - "2024-01-04 16:22:36.232204: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", - "2024-01-04 16:22:36.232207: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n" - ] - }, - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.test.is_gpu_available()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "1c85d462-f248-4ffb-908f-eb4b20eab179", "metadata": {}, "outputs": [], @@ -178,27 +385,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9", "metadata": {}, "outputs": [], "source": [ - "from tensorflow.keras import Model" + "from keras import Model" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2", "metadata": {}, "outputs": [], "source": [ - "from tensorflow.keras.initializers import Constant" + "from keras.initializers import Constant" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "9bc02f29-0fb7-420d-99a8-435eadc06e29", "metadata": {}, "outputs": [], @@ -238,77 +445,73 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "id": "a190207e-5a59-4813-9660-758760cf1b73", "metadata": {}, "outputs": [], "source": [ - "num_heads, ff_dim = 1, 12" + "num_heads, ff_dim = 3, 12" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], "source": [ "def get_prediction_model():\n", " def build_output(out, out_name):\n", - " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", - " out = self_block(out)\n", - " out = layers.GlobalAveragePooling1D()(out)\n", - " out = layers.Dropout(0.1)(out)\n", + " # self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " # out = self_block(out)\n", + " # out = layers.GlobalAveragePooling1D()(out)\n", + " # out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(32, activation=\"relu\")(out)\n", - " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", " return out\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", - " # x = layers.Dropout(rate=0.1)(x)\n", + " x = layers.Dropout(rate=0.1)(x)\n", " lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n", - " lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n", + " out = layers.Dense(128, activation='relu')(lstm_out)\n", " transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n", " out = transformer_block(lstm_out)\n", " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(64, activation='relu')(out)\n", - " out = K.expand_dims(out, axis=1)\n", + " # out = K.expand_dims(out, axis=1)\n", "\n", " bet = build_output(out, 'bet')\n", " mesco = build_output(out, 'mesco')\n", " micro = build_output(out, 'micro')\n", - " avg = build_output(out, 'avg')\n", "\n", - " bet = layers.Dense(1, activation='sigmoid', name='bet')(bet)\n", - " mesco = layers.Dense(1, activation='sigmoid', name='mesco')(mesco)\n", - " micro = layers.Dense(1, activation='sigmoid', name='micro')(micro)\n", - " avg = layers.Dense(1, activation='sigmoid', name='avg')(avg)\n", + " bet = layers.Dense(1, activation='sigmoid', name='bet2')(bet)\n", + " mesco = layers.Dense(1, activation='sigmoid', name='mesco2')(mesco)\n", + " micro = layers.Dense(1, activation='sigmoid', name='micro2')(micro)\n", "\n", - " model = Model(inputs=[inputs], outputs=[bet, mesco, micro, avg])\n", + " model = Model(inputs=[inputs], outputs=[bet, mesco, micro])\n", " return model\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "id": "264001b1-5e4a-4786-96fd-2b5c70ab3212", "metadata": {}, "outputs": [], "source": [ "def get_trainable_model(prediction_model):\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", - " bet, mesco, micro, avg = prediction_model(inputs)\n", + " bet, mesco, micro = prediction_model(inputs)\n", " bet_real = layers.Input(shape=(1,), name='bet_real')\n", " mesco_real = layers.Input(shape=(1,), name='mesco_real')\n", " micro_real = layers.Input(shape=(1,), name='micro_real')\n", - " avg_real = layers.Input(shape=(1,), name='avg_real')\n", - " out = CustomMultiLossLayer(nb_outputs=4)([bet_real, mesco_real, micro_real, avg_real, bet, mesco, micro, avg])\n", - " return Model([inputs, bet_real, mesco_real, micro_real, avg_real], out)" + " out = CustomMultiLossLayer(nb_outputs=3)([bet_real, mesco_real, micro_real, bet, mesco, micro])\n", + " return Model([inputs, bet_real, mesco_real, micro_real], out)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b", "metadata": {}, "outputs": [], @@ -316,6 +519,7 @@ "maxs = train_data.max()\n", "mins = train_data.min()\n", "for col in train_data.columns:\n", + " train_data[col] = train_data[col].astype(float)\n", " if maxs[col] - mins[col] == 0:\n", " continue\n", " train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])" @@ -323,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13", "metadata": {}, "outputs": [ @@ -348,149 +552,95 @@ " \n", " \n", " \n", - " 热处理条件-热处理次数\n", - " 热处理条件-是否是中温停留\n", - " 第一次热处理-温度\n", - " 第一次热处理-升温速率\n", - " 第一次热处理-保留时间\n", - " 第二次热处理-温度\n", - " 第二次热处理-升温速率·\n", - " 第二次热处理-保留时间\n", - " 共碳化-是否是共碳化物质\n", - " 共碳化-共碳化物质/沥青\n", - " ...\n", - " 模板剂-种类_二氧化硅\n", - " 模板剂-种类_氢氧化镁\n", - " 模板剂-种类_氧化钙\n", - " 模板剂-种类_氧化锌\n", - " 模板剂-种类_氧化镁\n", - " 模板剂-种类_氯化钠\n", - " 模板剂-种类_氯化钾\n", - " 模板剂-种类_碱式碳酸镁\n", - " 模板剂-种类_碳酸钙\n", - " 模板剂-种类_纤维素\n", + " 灰分(d)\n", + " 挥发分(daf）\n", + " 活化剂比例\n", + " 活化温度\n", + " 活化时间\n", + " 升温速率\n", + " 比表面积\n", + " 总孔体积\n", + " 微孔体积\n", + " 活化剂种类_KOH\n", + " 混合方式_浸渍\n", + " 混合方式_研磨\n", " \n", " \n", " \n", " \n", " 0\n", + " 0.265345\n", + " 0.224627\n", + " 0.491525\n", + " 0.62963\n", + " 0.142857\n", " 0.0\n", - 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123 rows × 42 columns

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158 rows × 12 columns

\n", "" ], "text/plain": [ - " 热处理条件-热处理次数 热处理条件-是否是中温停留 第一次热处理-温度 第一次热处理-升温速率 第一次热处理-保留时间 \\\n", - "0 0.0 0.0 0.166667 0.3 0.5 \n", - "1 0.0 0.0 0.333333 0.3 0.5 \n", - "2 0.0 0.0 0.333333 0.3 0.5 \n", - "3 0.0 0.0 0.333333 0.3 0.5 \n", - "4 1.0 0.0 0.166667 0.3 0.5 \n", - ".. ... ... ... ... ... \n", - "144 0.0 0.0 0.333333 0.3 0.0 \n", - "145 0.0 0.0 0.500000 0.3 0.0 \n", - "146 0.0 0.0 0.666667 0.3 0.0 \n", - "147 0.0 0.0 0.500000 0.3 0.0 \n", - "148 0.0 0.0 0.500000 0.3 0.0 \n", + " 灰分(d) 挥发分(daf） 活化剂比例 活化温度 活化时间 升温速率 比表面积 \\\n", + "0 0.265345 0.224627 0.491525 0.62963 0.142857 0.0 0.916251 \n", + "1 0.201133 0.160752 0.491525 0.62963 0.142857 0.0 0.929645 \n", + "2 0.426582 0.167672 0.491525 0.62963 0.142857 0.0 0.940237 \n", + "3 0.269358 0.140348 0.491525 0.62963 0.142857 0.0 0.874116 \n", + "4 0.273607 0.072569 0.491525 0.62963 0.142857 0.0 0.934281 \n", + ".. ... ... ... ... ... ... ... \n", + "153 0.098442 0.095280 0.237288 0.62963 0.142857 0.0 0.797597 \n", + "154 0.098442 0.095280 0.322034 0.62963 0.142857 0.0 0.875983 \n", + "155 0.098442 0.095280 0.406780 0.62963 0.142857 0.0 0.934960 \n", + "156 0.098442 0.095280 0.491525 0.62963 0.142857 0.0 0.947009 \n", + "157 0.098442 0.095280 0.576271 0.62963 0.142857 0.0 0.966042 \n", "\n", - " 第二次热处理-温度 第二次热处理-升温速率· 第二次热处理-保留时间 共碳化-是否是共碳化物质 共碳化-共碳化物质/沥青 ... \\\n", - "0 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "1 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "2 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "3 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "4 0.666667 0.5 0.666667 0.0 0.0 ... \n", - ".. ... ... ... ... ... ... \n", - "144 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "145 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "146 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "147 0.000000 0.0 0.000000 0.0 0.0 ... \n", - "148 0.000000 0.0 0.000000 0.0 0.0 ... \n", + " 总孔体积 微孔体积 活化剂种类_KOH 混合方式_浸渍 混合方式_研磨 \n", + "0 0.371910 0.417894 1.0 0.0 1.0 \n", + "1 0.426592 0.538462 1.0 0.0 1.0 \n", + "2 0.454831 0.504092 1.0 0.0 1.0 \n", + "3 0.267041 0.282597 1.0 0.0 1.0 \n", + "4 0.446442 0.467540 1.0 0.0 1.0 \n", + ".. ... ... ... ... ... \n", + "153 0.242809 0.312602 1.0 0.0 1.0 \n", + "154 0.354682 0.442990 1.0 0.0 1.0 \n", + "155 0.472659 0.586470 1.0 0.0 1.0 \n", + "156 0.568539 0.609384 1.0 0.0 1.0 \n", + "157 0.730712 0.510093 1.0 0.0 1.0 \n", "\n", - " 模板剂-种类_二氧化硅 模板剂-种类_氢氧化镁 模板剂-种类_氧化钙 模板剂-种类_氧化锌 模板剂-种类_氧化镁 模板剂-种类_氯化钠 \\\n", - "0 0 0.0 1.0 0 0.0 0.0 \n", - "1 0 0.0 1.0 0 0.0 0.0 \n", - "2 0 0.0 1.0 0 0.0 0.0 \n", - "3 0 0.0 1.0 0 0.0 0.0 \n", - "4 0 0.0 0.0 0 0.0 0.0 \n", - ".. ... ... ... ... ... ... \n", - "144 0 0.0 0.0 0 0.0 0.0 \n", - "145 0 0.0 0.0 0 0.0 0.0 \n", - "146 0 0.0 0.0 0 0.0 0.0 \n", - "147 0 0.0 0.0 0 0.0 0.0 \n", - "148 0 0.0 0.0 0 0.0 0.0 \n", - "\n", - " 模板剂-种类_氯化钾 模板剂-种类_碱式碳酸镁 模板剂-种类_碳酸钙 模板剂-种类_纤维素 \n", - "0 0 0.0 0.0 0.0 \n", - "1 0 0.0 0.0 0.0 \n", - "2 0 0.0 0.0 0.0 \n", - "3 0 0.0 0.0 0.0 \n", - "4 0 1.0 0.0 0.0 \n", - ".. ... ... ... ... \n", - "144 0 0.0 0.0 0.0 \n", - "145 0 0.0 0.0 0.0 \n", - "146 0 0.0 0.0 0.0 \n", - "147 0 0.0 0.0 0.0 \n", - "148 0 0.0 0.0 0.0 \n", - "\n", - "[123 rows x 42 columns]" + "[158 rows x 12 columns]" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -708,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb", "metadata": {}, "outputs": [], @@ -720,148 +790,41 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686", "metadata": {}, "outputs": [], "source": [ - "use_data = train_data.copy()\n", + "use_data = train_data[use_cols].copy()\n", "for col in use_cols:\n", " use_data[col] = use_data[col].astype('float32')" ] }, - { - "cell_type": "code", - "execution_count": 20, - "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", - "metadata": {}, - "outputs": [], - "source": [ - "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", - "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", - "metadata": {}, - "outputs": [], - "source": [ - "prediction_model = get_prediction_model()\n", - "trainable_model = get_trainable_model(prediction_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "input (InputLayer) [(None, 1, 38)] 0 \n", - "__________________________________________________________________________________________________\n", - "conv1d (Conv1D) (None, 1, 64) 2496 input[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional (Bidirectional) (None, 1, 128) 66048 conv1d[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense (Dense) (None, 1, 128) 16512 bidirectional[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block (TransformerB (None, 1, 128) 69772 dense[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d (Globa (None, 128) 0 transformer_block[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_2 (Dropout) (None, 128) 0 global_average_pooling1d[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 64) 8256 dropout_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "tf.expand_dims (TFOpLambda) (None, 1, 64) 0 dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_1 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_2 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_3 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", - "__________________________________________________________________________________________________\n", - "transformer_block_4 (Transforme (None, 1, 64) 18508 tf.expand_dims[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_1 (Glo (None, 64) 0 transformer_block_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_2 (Glo (None, 64) 0 transformer_block_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_3 (Glo (None, 64) 0 transformer_block_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_average_pooling1d_4 (Glo (None, 64) 0 transformer_block_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_5 (Dropout) (None, 64) 0 global_average_pooling1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_8 (Dropout) (None, 64) 0 global_average_pooling1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_11 (Dropout) (None, 64) 0 global_average_pooling1d_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_14 (Dropout) (None, 64) 0 global_average_pooling1d_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_6 (Dense) (None, 32) 2080 dropout_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_9 (Dense) (None, 32) 2080 dropout_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_12 (Dense) (None, 32) 2080 dropout_11[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_15 (Dense) (None, 32) 2080 dropout_14[0][0] \n", - "__________________________________________________________________________________________________\n", - "bet (Dense) (None, 1) 33 dense_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "mesco (Dense) (None, 1) 33 dense_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "micro (Dense) (None, 1) 33 dense_12[0][0] \n", - "__________________________________________________________________________________________________\n", - "avg (Dense) (None, 1) 33 dense_15[0][0] \n", - "==================================================================================================\n", - "Total params: 245,568\n", - "Trainable params: 245,568\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "prediction_model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "9289f452-a5a4-40c4-b942-f6cb2e348548", - "metadata": {}, - "outputs": [], - "source": [ - "from tensorflow.keras import optimizers\n", - "from tensorflow.python.keras.utils.vis_utils import plot_model" - ] - }, { "cell_type": "code", "execution_count": 24, - "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "id": "eeebafb2-1496-4248-9697-819d065f77b9", "metadata": {}, "outputs": [], "source": [ - "X = np.expand_dims(train[feature_cols].values, axis=1)\n", - "Y = [x for x in train[out_cols].values.T]\n", - "Y_valid = [x for x in valid[out_cols].values.T]" + "from sklearn.model_selection import KFold\n", + "kf = KFold(n_splits=10, shuffle=True, random_state=42)" ] }, { "cell_type": "code", "execution_count": 25, - "id": "9a62dea1-4f05-411b-9756-a91623580581", + "id": "ae7ddb36-2456-45b7-9580-447e1a13ae7f", + "metadata": {}, + "outputs": [], + "source": [ + "from keras import optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "be4ee01b-6e52-47cf-9d43-587334a30dae", "metadata": {}, "outputs": [], "source": [ @@ -871,282 +834,12 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/40\n", - "11/11 [==============================] - 6s 108ms/step - loss: 0.0316 - val_loss: 0.0835\n", - "Epoch 2/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0281 - val_loss: 0.0958\n", - "Epoch 3/40\n", - "11/11 [==============================] - 0s 27ms/step - loss: 0.0278 - val_loss: 0.0891\n", - "Epoch 4/40\n", - "11/11 [==============================] - 0s 21ms/step - loss: 0.0233 - val_loss: 0.0912\n", - "Epoch 5/40\n", - "11/11 [==============================] - 0s 27ms/step - loss: 0.0215 - val_loss: 0.1023\n", - "Epoch 6/40\n", - "11/11 [==============================] - 0s 33ms/step - loss: 0.0348 - val_loss: 0.0864\n", - "Epoch 7/40\n", - "11/11 [==============================] - 0s 16ms/step - loss: 0.0207 - val_loss: 0.0823\n", - "Epoch 8/40\n", - "11/11 [==============================] - 0s 25ms/step - loss: 0.0222 - val_loss: 0.0883\n", - "Epoch 9/40\n", - "11/11 [==============================] - 0s 22ms/step - loss: 0.0258 - val_loss: 0.1029\n", - "Epoch 10/40\n", - "11/11 [==============================] - 0s 26ms/step - loss: 0.0288 - val_loss: 0.0857\n", - "Epoch 11/40\n", - "11/11 [==============================] - 0s 22ms/step - loss: 0.0249 - val_loss: 0.0880\n", - "Epoch 12/40\n", - "11/11 [==============================] - 0s 21ms/step - loss: 0.0219 - val_loss: 0.0882\n", - "Epoch 13/40\n", - "11/11 [==============================] - 0s 24ms/step - loss: 0.0191 - val_loss: 0.0873\n", - "Epoch 14/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0187 - val_loss: 0.0929\n", - "Epoch 15/40\n", - "11/11 [==============================] - 0s 23ms/step - loss: 0.0183 - val_loss: 0.0988\n", - "Epoch 16/40\n", - "11/11 [==============================] - 0s 19ms/step - loss: 0.0189 - val_loss: 0.0905\n", - "Epoch 17/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0209 - val_loss: 0.0823\n", - "Epoch 18/40\n", - "11/11 [==============================] - 0s 27ms/step - loss: 0.0185 - val_loss: 0.0834\n", - "Epoch 19/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0177 - val_loss: 0.0916\n", - "Epoch 20/40\n", - "11/11 [==============================] - 0s 24ms/step - loss: 0.0163 - val_loss: 0.0919\n", - "Epoch 21/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0141 - val_loss: 0.0898\n", - "Epoch 22/40\n", - "11/11 [==============================] - 0s 27ms/step - loss: 0.0144 - val_loss: 0.0923\n", - "Epoch 23/40\n", - "11/11 [==============================] - 0s 19ms/step - loss: 0.0138 - val_loss: 0.0906\n", - "Epoch 24/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0140 - val_loss: 0.0897\n", - "Epoch 25/40\n", - "11/11 [==============================] - 0s 23ms/step - loss: 0.0126 - val_loss: 0.0892\n", - "Epoch 26/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0918\n", - "Epoch 27/40\n", - "11/11 [==============================] - 0s 25ms/step - loss: 0.0123 - val_loss: 0.0935\n", - "Epoch 28/40\n", - "11/11 [==============================] - 0s 25ms/step - loss: 0.0131 - val_loss: 0.0933\n", - "Epoch 29/40\n", - "11/11 [==============================] - 0s 17ms/step - loss: 0.0125 - val_loss: 0.0933\n", - "Epoch 30/40\n", - "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0932\n", - "Epoch 31/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0936\n", - "Epoch 32/40\n", - "11/11 [==============================] - 0s 28ms/step - loss: 0.0114 - val_loss: 0.0933\n", - "Epoch 33/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0122 - val_loss: 0.0932\n", - "Epoch 34/40\n", - "11/11 [==============================] - 0s 21ms/step - loss: 0.0114 - val_loss: 0.0936\n", - "Epoch 35/40\n", - "11/11 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0938\n", - "Epoch 36/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0118 - val_loss: 0.0937\n", - "Epoch 37/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0127 - val_loss: 0.0937\n", - "Epoch 38/40\n", - "11/11 [==============================] - 0s 27ms/step - loss: 0.0123 - val_loss: 0.0937\n", - "Epoch 39/40\n", - "11/11 [==============================] - 0s 19ms/step - loss: 0.0124 - val_loss: 0.0937\n", - "Epoch 40/40\n", - "11/11 [==============================] - 0s 20ms/step - loss: 0.0129 - val_loss: 0.0937\n" - ] - } - ], - "source": [ - "trainable_model.compile(optimizer='adam', loss=None)\n", - "hist = trainable_model.fit([X, Y[0], Y[1], Y[2], Y[3]], epochs=40, batch_size=8, verbose=1, \n", - " validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1], Y_valid[2], Y_valid[3]],\n", - " callbacks=[reduce_lr]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[0.8401114 ],\n", - " [0.4296295 ],\n", - " [0.34763122],\n", - " [0.33006623],\n", - " [0.74300694],\n", - " [0.48508543],\n", - " [0.48184243],\n", - " [0.7309267 ],\n", - " [0.5264127 ],\n", - " [0.7570494 ],\n", - " [0.29492375],\n", - " [0.34379733]], dtype=float32),\n", - " array([[0.9495956 ],\n", - " [0.19964108],\n", - " [0.25691378],\n", - " [0.15781167],\n", - " [0.39773428],\n", - " [0.257546 ],\n", - " [0.2265681 ],\n", - " [0.39088207],\n", - " [0.30309337],\n", - " [0.4006669 ],\n", - " [0.16448957],\n", - " [0.20928389]], dtype=float32),\n", - " array([[0.93163174],\n", - " [0.45915267],\n", - " [0.24377662],\n", - " [0.32275468],\n", - " [0.84771645],\n", - " [0.51101613],\n", - " [0.52240014],\n", - " [0.77952445],\n", - " [0.6746559 ],\n", - " [0.6747417 ],\n", - " [0.3022651 ],\n", - " [0.3458013 ]], dtype=float32),\n", - " array([[0.4518058 ],\n", - " [0.06488091],\n", - " [0.2511762 ],\n", - " [0.0624491 ],\n", - " [0.09656441],\n", - " [0.07555431],\n", - " [0.06494072],\n", - " [0.09723139],\n", - " [0.10824579],\n", - " [0.09783638],\n", - " [0.07164052],\n", - " [0.15804273]], dtype=float32)]" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", - "rst" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.998927703775019, 0.9994643982390371, 0.9991108696677027, 0.9996066810061789]" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", - "metadata": {}, - "outputs": [], - "source": [ - "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", - "metadata": {}, - "outputs": [], - "source": [ - "real_rst = test[out_cols].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", - "metadata": {}, - "outputs": [], - "source": [ - "for col in out_cols:\n", - " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", - " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['碳材料结构特征-比表面积', '碳材料结构特征-总孔体积', '碳材料结构特征-微孔体积', '碳材料结构特征-平均孔径'], dtype='object')" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "real_rst.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "21739f82-d82a-4bde-8537-9504b68a96d5", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_pm25 = pred_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", - "y_pred_pm10 = pred_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", - "y_pred_so2 = pred_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", - "y_pred_no2 = pred_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)\n", - "y_true_pm25 = real_rst['碳材料结构特征-比表面积'].values.reshape(-1,)\n", - "y_true_pm10 = real_rst['碳材料结构特征-总孔体积'].values.reshape(-1,)\n", - "y_true_so2 = real_rst['碳材料结构特征-微孔体积'].values.reshape(-1,)\n", - "y_true_no2 = real_rst['碳材料结构特征-平均孔径'].values.reshape(-1,)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "26ea6cfa-efad-443c-9dd9-844f8be42b91", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "28072e7c-c9d5-4ff6-940d-e94ae879afc9", + "execution_count": 27, + "id": "42cb8083-d37b-41e8-b674-fc8f2789a1b9", "metadata": {}, "outputs": [], "source": [ + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error\n", "def print_eva(y_true, y_pred, tp):\n", " MSE = mean_squared_error(y_true, y_pred)\n", " RMSE = np.sqrt(MSE)\n", @@ -1163,7 +856,6530 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 29, + "id": "7404a0c6-1325-4348-b0a4-25dae3067d78", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 11:13:33.925432: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-04-08 11:13:33.947575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n", + "pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0\n", + "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s\n", + "2024-04-08 11:13:33.947605: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n", + "2024-04-08 11:13:33.968875: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n", + "2024-04-08 11:13:33.968940: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n", + "2024-04-08 11:13:33.972012: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n", + "2024-04-08 11:13:33.972302: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n", + "2024-04-08 11:13:33.972899: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11\n", + "2024-04-08 11:13:33.973713: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n", + "2024-04-08 11:13:33.973880: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n", + "2024-04-08 11:13:33.976420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n", + "2024-04-08 11:13:33.976836: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-04-08 11:13:33.986546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n", + "pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0\n", + "coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s\n", + "2024-04-08 11:13:33.989040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n", + "2024-04-08 11:13:33.989091: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n", + "2024-04-08 11:13:34.622398: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n", + "2024-04-08 11:13:34.622417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n", + "2024-04-08 11:13:34.622422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n", + "2024-04-08 11:13:34.626343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 37675 MB memory) -> physical GPU (device: 0, name: NVIDIA A100-PCIE-40GB, pci bus id: 0000:9c:00.0, compute capability: 8.0)\n", + "2024-04-08 11:13:47.978373: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-04-08 11:13:47.994803: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/280\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-04-08 11:14:01.812069: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n", + "2024-04-08 11:14:02.937519: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8700\n", + "2024-04-08 11:14:03.573600: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n", + "2024-04-08 11:14:03.574110: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n", + "2024-04-08 11:14:03.806121: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15/15 [==============================] - 18s 101ms/step - loss: 6.0853 - val_loss: 6.0270\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 6.0146 - val_loss: 5.9848\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.9665 - val_loss: 5.9331\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.9214 - val_loss: 5.8883\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.8715 - val_loss: 5.8414\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.8291 - val_loss: 5.7992\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.7840 - val_loss: 5.7525\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.7362 - val_loss: 5.7064\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.6912 - val_loss: 5.6601\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 5.6494 - val_loss: 5.6172\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.6031 - val_loss: 5.5690\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.5554 - val_loss: 5.5244\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.5094 - val_loss: 5.4814\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.4629 - val_loss: 5.4334\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.4173 - val_loss: 5.3924\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.3758 - val_loss: 5.3440\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.3243 - val_loss: 5.2960\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.2835 - val_loss: 5.2535\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.2403 - val_loss: 5.2099\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 5.1915 - val_loss: 5.1600\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.1437 - val_loss: 5.1163\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.1032 - val_loss: 5.0668\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 5.0552 - val_loss: 5.0426\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 5.0285 - val_loss: 4.9953\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.9751 - val_loss: 4.9410\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.9293 - val_loss: 4.9016\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.8772 - val_loss: 4.8457\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.8378 - val_loss: 4.8094\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7859 - val_loss: 4.7582\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7452 - val_loss: 4.7266\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.7049 - val_loss: 4.6671\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.6538 - val_loss: 4.6273\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.6117 - val_loss: 4.5850\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.5648 - val_loss: 4.5436\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.5190 - val_loss: 4.4914\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4741 - val_loss: 4.4457\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.4274 - val_loss: 4.4000\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3819 - val_loss: 4.3606\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3497 - val_loss: 4.3305\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3177 - val_loss: 4.3048\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2876 - val_loss: 4.2689\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2592 - val_loss: 4.2384\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.2289 - val_loss: 4.2159\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 4.1975 - val_loss: 4.1815\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1707 - val_loss: 4.1547\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1543 - val_loss: 4.1404\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1142 - val_loss: 4.1112\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0801 - val_loss: 4.0668\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0530 - val_loss: 4.0336\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.0184 - val_loss: 4.0059\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9920 - val_loss: 3.9744\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9605 - val_loss: 3.9486\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.9278 - val_loss: 3.9156\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8994 - val_loss: 3.8840\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.8660 - val_loss: 3.8531\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8381 - val_loss: 3.8223\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8065 - val_loss: 3.7922\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7815 - val_loss: 3.7619\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7489 - val_loss: 3.7333\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7183 - val_loss: 3.7022\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.6889 - val_loss: 3.6773\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.6607 - val_loss: 3.6405\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6283 - val_loss: 3.6107\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5965 - val_loss: 3.5830\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.5705 - val_loss: 3.5539\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5388 - val_loss: 3.5255\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5076 - val_loss: 3.4899\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 3.4752 - val_loss: 3.4631\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4472 - val_loss: 3.4308\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.4196 - val_loss: 3.4010\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3878 - val_loss: 3.3734\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3587 - val_loss: 3.3409\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3293 - val_loss: 3.3142\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2995 - val_loss: 3.2816\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2690 - val_loss: 3.2501\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2388 - val_loss: 3.2263\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2082 - val_loss: 3.1885\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1786 - val_loss: 3.1581\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1471 - val_loss: 3.1334\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1202 - val_loss: 3.1044\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0861 - val_loss: 3.0721\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0574 - val_loss: 3.0442\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0296 - val_loss: 3.0117\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0023 - val_loss: 2.9930\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9710 - val_loss: 2.9580\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9474 - val_loss: 2.9242\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9095 - val_loss: 2.9010\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.8816 - val_loss: 2.8642\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8502 - val_loss: 2.8332\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8203 - val_loss: 2.8042\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7879 - val_loss: 2.7716\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.7570 - val_loss: 2.7430\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.7259 - val_loss: 2.7168\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.7021 - val_loss: 2.6885\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 2.6682 - val_loss: 2.6570\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6432 - val_loss: 2.6263\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6101 - val_loss: 2.5897\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5778 - val_loss: 2.5681\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5558 - val_loss: 2.5327\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 2.5251 - val_loss: 2.5061\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4936 - val_loss: 2.4761\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4649 - val_loss: 2.4512\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4312 - val_loss: 2.4154\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4000 - val_loss: 2.3838\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3727 - val_loss: 2.3540\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.3407 - val_loss: 2.3310\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3078 - val_loss: 2.3006\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2818 - val_loss: 2.2619\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2515 - val_loss: 2.2308\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2204 - val_loss: 2.2044\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1891 - val_loss: 2.1807\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 2.1606 - val_loss: 2.1410\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1317 - val_loss: 2.1113\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0990 - val_loss: 2.0811\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.0764 - val_loss: 2.0522\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0421 - val_loss: 2.0214\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0155 - val_loss: 1.9915\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9799 - val_loss: 1.9629\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9494 - val_loss: 1.9385\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9251 - val_loss: 1.9051\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8949 - val_loss: 1.8759\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8644 - val_loss: 1.8706\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8489 - val_loss: 1.8308\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 1.8087 - val_loss: 1.7977\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7749 - val_loss: 1.7688\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7534 - val_loss: 1.7378\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7127 - val_loss: 1.7045\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6865 - val_loss: 1.6744\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6573 - val_loss: 1.6427\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6235 - val_loss: 1.6109\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5967 - val_loss: 1.5835\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5669 - val_loss: 1.5545\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5339 - val_loss: 1.5174\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5093 - val_loss: 1.4934\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.4793 - val_loss: 1.4596\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4495 - val_loss: 1.4335\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4142 - val_loss: 1.4022\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.3924 - val_loss: 1.3786\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.3609 - val_loss: 1.3442\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3259 - val_loss: 1.3106\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.3040 - val_loss: 1.2859\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2730 - val_loss: 1.2550\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2509 - val_loss: 1.2333\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2160 - val_loss: 1.1922\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.1840 - val_loss: 1.1680\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1514 - val_loss: 1.1352\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1227 - val_loss: 1.1014\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0908 - val_loss: 1.0708\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0584 - val_loss: 1.0415\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0349 - val_loss: 1.0432\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0144 - val_loss: 0.9994\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9710 - val_loss: 0.9684\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9420 - val_loss: 0.9297\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9124 - val_loss: 0.9023\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8858 - val_loss: 0.8702\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8488 - val_loss: 0.8434\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.8263 - val_loss: 0.8078\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7939 - val_loss: 0.7735\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7592 - val_loss: 0.7428\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7293 - val_loss: 0.7199\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7086 - val_loss: 0.6938\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6708 - val_loss: 0.6575\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6533 - val_loss: 0.6233\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6125 - val_loss: 0.5980\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5825 - val_loss: 0.5767\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5598\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5533 - val_loss: 0.5545\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5384 - val_loss: 0.5321\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5180 - val_loss: 0.5248\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5119 - val_loss: 0.5090\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4882 - val_loss: 0.5010\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4835 - val_loss: 0.4774\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4757 - val_loss: 0.4660\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4536 - val_loss: 0.4500\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4341 - val_loss: 0.4243\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4202 - val_loss: 0.4098\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4045 - val_loss: 0.3962\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3846 - val_loss: 0.3807\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.3694 - val_loss: 0.3647\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3567 - val_loss: 0.3471\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3420 - val_loss: 0.3336\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3252 - val_loss: 0.3194\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3148 - val_loss: 0.3046\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2958 - val_loss: 0.2945\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2874 - val_loss: 0.2774\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2675 - val_loss: 0.2683\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2531 - val_loss: 0.2504\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.2370 - val_loss: 0.2337\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2288 - val_loss: 0.2140\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2054 - val_loss: 0.2039\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1923 - val_loss: 0.1865\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1747 - val_loss: 0.1732\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1657 - val_loss: 0.1587\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1380 - val_loss: 0.1423\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1289 - val_loss: 0.1306\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1186 - val_loss: 0.1169\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0992 - val_loss: 0.1038\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0894 - val_loss: 0.0899\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0703 - val_loss: 0.0739\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0535 - val_loss: 0.0572\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0530 - val_loss: 0.0662\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0280 - val_loss: 0.0423\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0344\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0353\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0419\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0343\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0331\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0255 - val_loss: 0.0392\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0245 - val_loss: 0.0384\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0255 - val_loss: 0.0319\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0230 - val_loss: 0.0292\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0255 - val_loss: 0.0305\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0235 - val_loss: 0.0340\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0225 - val_loss: 0.0325\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0210 - val_loss: 0.0295\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0353\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0366\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0321\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0327\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0379\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0216 - val_loss: 0.0369\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0206 - val_loss: 0.0367\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0264 - val_loss: 0.0361\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0352\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0349\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0348\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0348\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0349\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0350\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0343\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0342\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0341\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0339\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0338\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0136 - val_loss: 0.0336\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0336\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0151 - val_loss: 0.0337\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0164 - val_loss: 0.0338\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0339\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0340\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0342\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0342\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0342\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0127 - val_loss: 0.0342\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0156 - val_loss: 0.0342\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0342\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0341\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0172 - val_loss: 0.0341\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0198 - val_loss: 0.0341\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0162 - val_loss: 0.0341\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0342\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0143 - val_loss: 0.0342\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0157 - val_loss: 0.0342\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0341\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0342\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0341\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0341\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0154 - val_loss: 0.0341\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0209 - val_loss: 0.0341\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0341\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0341\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0341\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0134 - val_loss: 0.0341\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0341\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0129 - val_loss: 0.0341\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0341\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0341\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0193 - val_loss: 0.0341\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0197 - val_loss: 0.0341\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0152 - val_loss: 0.0341\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0341\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.0105 - val_loss: 0.0341\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0147 - val_loss: 0.0341\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0125 - val_loss: 0.0341\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0176 - val_loss: 0.0341\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0341\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0341\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0341\n", + "COL: 比表面积, MSE: 1.32E-01,RMSE: 0.3629,MAPE: 3.6700000000000004 %,MAE: 0.2619,R_2: 0.2356\n", + "COL: 总孔体积, MSE: 7.52E-02,RMSE: 0.2742,MAPE: 27.810000000000002 %,MAE: 0.1978,R_2: 0.5771\n", + "COL: 微孔体积, MSE: 3.16E-02,RMSE: 0.1779,MAPE: 27.389999999999997 %,MAE: 0.1412,R_2: 0.3639\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 93ms/step - loss: 1.8382 - val_loss: 1.6969\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.6804 - val_loss: 1.6477\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6384 - val_loss: 1.6156\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6002 - val_loss: 1.5794\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5757 - val_loss: 1.5500\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5482 - val_loss: 1.5252\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5112 - val_loss: 1.4768\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4720 - val_loss: 1.4442\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4436 - val_loss: 1.4154\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4106 - val_loss: 1.3848\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3779 - val_loss: 1.3495\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3496 - val_loss: 1.3266\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3157 - val_loss: 1.2910\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2770 - val_loss: 1.2666\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2492 - val_loss: 1.2274\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2195 - val_loss: 1.1976\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1825 - val_loss: 1.1668\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1551 - val_loss: 1.1331\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1302 - val_loss: 1.1090\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1015 - val_loss: 1.0747\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0671 - val_loss: 1.0434\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0422 - val_loss: 1.0214\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0175 - val_loss: 0.9870\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9689 - val_loss: 0.9571\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.9638 - val_loss: 0.9219\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9229 - val_loss: 0.8921\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8851 - val_loss: 0.8686\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8600 - val_loss: 0.8591\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8503 - val_loss: 0.8390\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8302 - val_loss: 0.8242\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8137 - val_loss: 0.8139\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7981 - val_loss: 0.7926\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7801 - val_loss: 0.7753\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7702 - val_loss: 0.7752\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7537 - val_loss: 0.7512\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7407 - val_loss: 0.7492\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7295 - val_loss: 0.7353\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7083 - val_loss: 0.7060\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6915 - val_loss: 0.6916\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6770 - val_loss: 0.6797\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6640 - val_loss: 0.6657\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6501 - val_loss: 0.6506\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6339 - val_loss: 0.6335\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.6152 - val_loss: 0.6280\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6174 - val_loss: 0.6014\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5801 - val_loss: 0.5853\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5731 - val_loss: 0.5724\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5564 - val_loss: 0.5611\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5450 - val_loss: 0.5549\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5287 - val_loss: 0.5268\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5126 - val_loss: 0.5114\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5044 - val_loss: 0.4975\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4773 - val_loss: 0.4839\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4700 - val_loss: 0.4683\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4451 - val_loss: 0.4576\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4326 - val_loss: 0.4369\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4194 - val_loss: 0.4308\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4123 - val_loss: 0.4054\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3949 - val_loss: 0.4015\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3771 - val_loss: 0.3864\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3633 - val_loss: 0.3728\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3432 - val_loss: 0.3576\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3261 - val_loss: 0.3386\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3135 - val_loss: 0.3321\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3005 - val_loss: 0.3138\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2829 - val_loss: 0.2991\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2684 - val_loss: 0.2838\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2604 - val_loss: 0.2645\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2434 - val_loss: 0.2493\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2266 - val_loss: 0.2490\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2219 - val_loss: 0.2254\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1932 - val_loss: 0.2080\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.1971\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1630 - val_loss: 0.1745\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1646 - val_loss: 0.1629\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.1398 - val_loss: 0.1502\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1169 - val_loss: 0.1299\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1055 - val_loss: 0.1099\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0943 - val_loss: 0.0993\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0771 - val_loss: 0.0771\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0663 - val_loss: 0.0752\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0607\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0305 - val_loss: 0.0504\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0391 - val_loss: 0.0555\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0289 - val_loss: 0.0447\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0503\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0326 - val_loss: 0.0529\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0402 - val_loss: 0.0522\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0297 - val_loss: 0.0481\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0336 - val_loss: 0.0483\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0504\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0344 - val_loss: 0.0517\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0526\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0534\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0334 - val_loss: 0.0468\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0389 - val_loss: 0.0455\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0284 - val_loss: 0.0468\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0488\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0493\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0494\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0492\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0484\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0183 - val_loss: 0.0486\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0498\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0500\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0292 - val_loss: 0.0498\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0498\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0500\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0298 - val_loss: 0.0500\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0323 - val_loss: 0.0499\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0500\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0245 - val_loss: 0.0499\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0499\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0270 - val_loss: 0.0499\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0499\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0499\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0327 - val_loss: 0.0499\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0328 - val_loss: 0.0499\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0499\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0301 - val_loss: 0.0499\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0311 - val_loss: 0.0499\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0499\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0499\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0499\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0499\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0312 - val_loss: 0.0499\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0499\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0223 - val_loss: 0.0499\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0221 - val_loss: 0.0499\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0294 - val_loss: 0.0499\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0499\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0241 - val_loss: 0.0499\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0192 - val_loss: 0.0499\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0295 - val_loss: 0.0499\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0213 - val_loss: 0.0499\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0246 - val_loss: 0.0499\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0499\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0242 - val_loss: 0.0499\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0277 - val_loss: 0.0499\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0177 - val_loss: 0.0499\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0499\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0499\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0499\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0191 - val_loss: 0.0499\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0266 - val_loss: 0.0499\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0499\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0256 - val_loss: 0.0499\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0499\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0271 - val_loss: 0.0499\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0233 - val_loss: 0.0499\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0301 - val_loss: 0.0499\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0499\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0499\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0293 - val_loss: 0.0499\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0499\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0299 - val_loss: 0.0499\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0177 - val_loss: 0.0499\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0194 - val_loss: 0.0499\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0252 - val_loss: 0.0499\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0499\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0499\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0499\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0499\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0499\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0499\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0499\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0278 - val_loss: 0.0499\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0499\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0499\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0269 - val_loss: 0.0499\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0217 - val_loss: 0.0499\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0499\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0499\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0226 - val_loss: 0.0499\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0499\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0499\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0199 - val_loss: 0.0499\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0499\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0178 - val_loss: 0.0499\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0499\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0499\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0252 - val_loss: 0.0499\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0268 - val_loss: 0.0499\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0499\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0499\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0499\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0197 - val_loss: 0.0499\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0269 - val_loss: 0.0499\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0221 - val_loss: 0.0499\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0264 - val_loss: 0.0499\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0218 - val_loss: 0.0499\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0499\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0499\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0499\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0499\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0215 - val_loss: 0.0499\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0499\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0499\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0499\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0229 - val_loss: 0.0499\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0499\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0499\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0289 - val_loss: 0.0499\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0197 - val_loss: 0.0499\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0499\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0499\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0233 - val_loss: 0.0499\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0274 - val_loss: 0.0499\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0499\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0499\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0271 - val_loss: 0.0499\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0499\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0499\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0499\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0287 - val_loss: 0.0499\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0499\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0232 - val_loss: 0.0499\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0179 - val_loss: 0.0499\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0298 - val_loss: 0.0499\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0499\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0499\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0300 - val_loss: 0.0499\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0499\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0168 - val_loss: 0.0499\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0499\n", + "COL: 比表面积, MSE: 1.83E-01,RMSE: 0.4274,MAPE: 3.84 %,MAE: 0.2752,R_2: 0.4599\n", + "COL: 总孔体积, MSE: 1.35E-01,RMSE: 0.368,MAPE: 29.43 %,MAE: 0.251,R_2: 0.582\n", + "COL: 微孔体积, MSE: 1.75E-01,RMSE: 0.4187,MAPE: 32.84 %,MAE: 0.2536,R_2: 0.2184\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 86ms/step - loss: 2.5093 - val_loss: 2.2921\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2438 - val_loss: 2.2513\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.2354 - val_loss: 2.1929\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.1850 - val_loss: 2.2179\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1398 - val_loss: 2.1617\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1034 - val_loss: 2.1344\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1028 - val_loss: 2.0470\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0489 - val_loss: 2.0199\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0085 - val_loss: 2.0242\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9942 - val_loss: 1.9674\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9434 - val_loss: 1.9257\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9492 - val_loss: 1.8951\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9382 - val_loss: 1.8804\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9204 - val_loss: 1.8696\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.8749 - val_loss: 1.8513\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8301 - val_loss: 1.7955\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.8124 - val_loss: 1.7826\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8018 - val_loss: 1.7692\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7991 - val_loss: 1.7415\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7584 - val_loss: 1.7389\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7315 - val_loss: 1.7086\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7082 - val_loss: 1.6742\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6990 - val_loss: 1.7330\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7054 - val_loss: 1.6404\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6263 - val_loss: 1.6385\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6006 - val_loss: 1.6112\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5858 - val_loss: 1.5762\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.6333\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5803 - val_loss: 1.5680\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5278 - val_loss: 1.5337\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5164 - val_loss: 1.5049\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5023 - val_loss: 1.4911\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4772 - val_loss: 1.4716\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4323 - val_loss: 1.4509\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4409 - val_loss: 1.4576\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4506 - val_loss: 1.4298\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4344 - val_loss: 1.4233\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3917 - val_loss: 1.3796\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3798 - val_loss: 1.3587\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3458 - val_loss: 1.3256\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3357 - val_loss: 1.3232\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2982 - val_loss: 1.2909\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2903 - val_loss: 1.2806\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2797 - val_loss: 1.2819\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2652 - val_loss: 1.2418\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2416 - val_loss: 1.2181\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1992 - val_loss: 1.1981\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1843 - val_loss: 1.1742\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1733 - val_loss: 1.1714\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1635 - val_loss: 1.1432\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1295 - val_loss: 1.1217\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1078 - val_loss: 1.0999\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0884 - val_loss: 1.0842\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0660 - val_loss: 1.0670\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0480 - val_loss: 1.0427\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0226 - val_loss: 1.0279\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0134 - val_loss: 1.0064\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9996 - val_loss: 0.9869\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9771 - val_loss: 0.9727\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9675 - val_loss: 0.9556\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9433 - val_loss: 0.9389\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9366 - val_loss: 0.9140\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9133 - val_loss: 0.9077\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8954 - val_loss: 0.8816\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8708 - val_loss: 0.8689\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8601 - val_loss: 0.8506\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8415 - val_loss: 0.8326\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8209 - val_loss: 0.8159\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8046 - val_loss: 0.8116\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7990 - val_loss: 0.7809\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7824 - val_loss: 0.7674\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7634 - val_loss: 0.7477\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7432 - val_loss: 0.7355\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7313 - val_loss: 0.7165\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7137 - val_loss: 0.6973\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6910 - val_loss: 0.6775\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6671 - val_loss: 0.6567\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6503 - val_loss: 0.6431\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6419 - val_loss: 0.6281\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6325 - val_loss: 0.6109\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6073 - val_loss: 0.5969\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5921 - val_loss: 0.5814\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5766 - val_loss: 0.5767\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5570\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5517 - val_loss: 0.5424\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5356 - val_loss: 0.5201\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5284 - val_loss: 0.5093\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4967 - val_loss: 0.4913\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4909 - val_loss: 0.4805\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4778 - val_loss: 0.4694\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4641 - val_loss: 0.4434\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4429 - val_loss: 0.4431\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4261 - val_loss: 0.4184\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4118 - val_loss: 0.4070\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4017 - val_loss: 0.3975\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3843 - val_loss: 0.3721\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3733 - val_loss: 0.3661\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3501 - val_loss: 0.3501\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3431 - val_loss: 0.3297\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3337 - val_loss: 0.3141\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.3057 - val_loss: 0.3023\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2870 - val_loss: 0.3007\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2794 - val_loss: 0.2787\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2690 - val_loss: 0.2698\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2539 - val_loss: 0.2383\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2290 - val_loss: 0.2458\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2229 - val_loss: 0.2211\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2076 - val_loss: 0.1950\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1842 - val_loss: 0.1891\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1702 - val_loss: 0.1710\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1541 - val_loss: 0.1655\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1383 - val_loss: 0.1385\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1276 - val_loss: 0.1214\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1092 - val_loss: 0.1268\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1004 - val_loss: 0.1230\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0888 - val_loss: 0.0899\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0756 - val_loss: 0.0718\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0530 - val_loss: 0.0633\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0413 - val_loss: 0.0487\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0440\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0337 - val_loss: 0.0697\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0481\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0335 - val_loss: 0.0503\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0281 - val_loss: 0.0584\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0400 - val_loss: 0.0570\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0367 - val_loss: 0.0713\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0343 - val_loss: 0.0378\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0355 - val_loss: 0.0768\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0532\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0310 - val_loss: 0.0867\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0276 - val_loss: 0.0567\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0301 - val_loss: 0.0598\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0711\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0405\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0316 - val_loss: 0.0599\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0296 - val_loss: 0.0631\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0415\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0422\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0531\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0517\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0230 - val_loss: 0.0481\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0484\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0277 - val_loss: 0.0496\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0532\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0250 - val_loss: 0.0496\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0543\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0539\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0538\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0536\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0228 - val_loss: 0.0535\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0533\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0530\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0179 - val_loss: 0.0530\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0271 - val_loss: 0.0529\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0174 - val_loss: 0.0526\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0526\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0526\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0527\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0174 - val_loss: 0.0526\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0249 - val_loss: 0.0527\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0194 - val_loss: 0.0527\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0527\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0290 - val_loss: 0.0527\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0219 - val_loss: 0.0527\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0208 - val_loss: 0.0527\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0527\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0527\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0527\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0231 - val_loss: 0.0527\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0527\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0527\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0527\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0527\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0307 - val_loss: 0.0527\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0527\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0527\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0527\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0215 - val_loss: 0.0527\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0344 - val_loss: 0.0527\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0313 - val_loss: 0.0527\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0527\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0527\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0527\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0180 - val_loss: 0.0527\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0527\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0527\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0527\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0527\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0527\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0527\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0228 - val_loss: 0.0527\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0527\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0527\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0224 - val_loss: 0.0527\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0527\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0527\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0527\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0527\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0527\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0527\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0527\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0527\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0527\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0527\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0527\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0527\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0527\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0527\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0527\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0527\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0527\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0183 - val_loss: 0.0527\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0527\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0527\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0166 - val_loss: 0.0527\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0527\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0527\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0316 - val_loss: 0.0527\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0527\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0527\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0263 - val_loss: 0.0527\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0527\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0527\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0527\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0527\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0527\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0213 - val_loss: 0.0527\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0527\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0527\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0527\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0527\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0527\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0158 - val_loss: 0.0527\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0206 - val_loss: 0.0527\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0527\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0194 - val_loss: 0.0527\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0527\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0527\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0527\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0527\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0200 - val_loss: 0.0527\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0527\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0190 - val_loss: 0.0527\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0217 - val_loss: 0.0527\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0527\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0263 - val_loss: 0.0527\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0527\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0527\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0306 - val_loss: 0.0527\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0527\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0210 - val_loss: 0.0527\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0527\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0527\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0527\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0527\n", + "COL: 比表面积, MSE: 1.91E-01,RMSE: 0.4372,MAPE: 4.16 %,MAE: 0.289,R_2: 0.5448\n", + "COL: 总孔体积, MSE: 8.81E-02,RMSE: 0.2969,MAPE: 25.869999999999997 %,MAE: 0.178,R_2: 0.5039\n", + "COL: 微孔体积, MSE: 2.92E-02,RMSE: 0.1709,MAPE: 31.97 %,MAE: 0.1435,R_2: 0.6463\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 87ms/step - loss: 5.1081 - val_loss: 4.9102\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.8975 - val_loss: 4.9026\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.8714 - val_loss: 4.8965\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.8855 - val_loss: 4.8015\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.7490 - val_loss: 4.8035\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7785 - val_loss: 4.7613\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.7197 - val_loss: 4.7055\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.7034 - val_loss: 4.6356\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.6134 - val_loss: 4.6040\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.5380 - val_loss: 4.5892\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.5485 - val_loss: 4.5479\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4834 - val_loss: 4.5093\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.4568 - val_loss: 4.5328\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4267 - val_loss: 4.4700\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.4915 - val_loss: 4.3953\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.4139 - val_loss: 4.3778\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.3356 - val_loss: 4.3524\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.2885 - val_loss: 4.3275\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.2311 - val_loss: 4.2670\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.1935 - val_loss: 4.2596\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1738 - val_loss: 4.1836\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1310 - val_loss: 4.1656\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 4.1215 - val_loss: 4.2036\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0710 - val_loss: 4.1054\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0426 - val_loss: 4.0336\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9955 - val_loss: 4.0605\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9581 - val_loss: 3.9995\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9062 - val_loss: 3.9595\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9252 - val_loss: 3.9659\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.8707 - val_loss: 3.8585\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8102 - val_loss: 3.8593\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7872 - val_loss: 3.7926\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8039 - val_loss: 3.8059\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7222 - val_loss: 3.7581\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7013 - val_loss: 3.7203\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6839 - val_loss: 3.6715\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6679\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5974 - val_loss: 3.6234\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5549 - val_loss: 3.6106\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5219 - val_loss: 3.5373\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4900 - val_loss: 3.5039\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.4689 - val_loss: 3.4978\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4203 - val_loss: 3.4309\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 3.3841 - val_loss: 3.4069\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 3.3521 - val_loss: 3.4026\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3213 - val_loss: 3.3949\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.3278 - val_loss: 3.3207\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2762 - val_loss: 3.2875\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.2363 - val_loss: 3.2245\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1902 - val_loss: 3.2058\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1809 - val_loss: 3.1922\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1383 - val_loss: 3.1311\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0813 - val_loss: 3.1212\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.0824 - val_loss: 3.0604\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 3.0412 - val_loss: 3.0347\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0030 - val_loss: 3.0090\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9850 - val_loss: 2.9901\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9486 - val_loss: 2.9865\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9330 - val_loss: 2.9117\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8916 - val_loss: 2.8976\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8388 - val_loss: 2.8618\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8202 - val_loss: 2.8307\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7957 - val_loss: 2.8076\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7548 - val_loss: 2.7568\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 2.7154 - val_loss: 2.7365\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.6893 - val_loss: 2.6783\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6608 - val_loss: 2.6590\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.6173 - val_loss: 2.6270\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6031 - val_loss: 2.5972\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5819 - val_loss: 2.5621\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5281 - val_loss: 2.5354\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.4967 - val_loss: 2.4950\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4655 - val_loss: 2.4588\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4464 - val_loss: 2.4244\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4075 - val_loss: 2.3982\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3720 - val_loss: 2.3743\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3495 - val_loss: 2.3415\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2947 - val_loss: 2.2965\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2712 - val_loss: 2.2628\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2385 - val_loss: 2.2341\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2093 - val_loss: 2.2007\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1926 - val_loss: 2.1803\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1452 - val_loss: 2.1466\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1080 - val_loss: 2.1139\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0963 - val_loss: 2.0719\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0696 - val_loss: 2.0438\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0350 - val_loss: 2.0082\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9857 - val_loss: 1.9835\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9671 - val_loss: 1.9488\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9304 - val_loss: 1.9033\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9129 - val_loss: 1.8740\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8763 - val_loss: 1.8452\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8298 - val_loss: 1.8235\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.7952 - val_loss: 1.7809\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.7741 - val_loss: 1.7611\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7307 - val_loss: 1.7357\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.7168 - val_loss: 1.7038\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.6875 - val_loss: 1.6567\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.6440 - val_loss: 1.6213\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6178 - val_loss: 1.5933\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5765 - val_loss: 1.5616\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5462 - val_loss: 1.5271\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5152 - val_loss: 1.4967\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.4833 - val_loss: 1.4654\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4444 - val_loss: 1.4358\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4152 - val_loss: 1.4042\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3893 - val_loss: 1.3689\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3534 - val_loss: 1.3381\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3189 - val_loss: 1.3094\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2893 - val_loss: 1.2775\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2663 - val_loss: 1.2617\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2497 - val_loss: 1.2412\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2280 - val_loss: 1.2307\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2131 - val_loss: 1.2178\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1963 - val_loss: 1.2042\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.1870 - val_loss: 1.1858\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1782 - val_loss: 1.1721\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.1521 - val_loss: 1.1573\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1387 - val_loss: 1.1391\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1245 - val_loss: 1.1252\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1089 - val_loss: 1.1109\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0948 - val_loss: 1.0973\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0807 - val_loss: 1.0832\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0651 - val_loss: 1.0653\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.0550 - val_loss: 1.0520\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0281 - val_loss: 1.0342\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.0174 - val_loss: 1.0233\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0004 - val_loss: 1.0092\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.9905 - val_loss: 0.9870\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9755 - val_loss: 0.9740\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9609 - val_loss: 0.9636\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9390 - val_loss: 0.9474\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9285 - val_loss: 0.9306\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9186 - val_loss: 0.9154\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8964 - val_loss: 0.9008\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8825 - val_loss: 0.8857\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8665 - val_loss: 0.8731\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8525 - val_loss: 0.8578\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8374 - val_loss: 0.8368\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8190 - val_loss: 0.8262\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.8082 - val_loss: 0.8144\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7900 - val_loss: 0.7967\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.7780 - val_loss: 0.7780\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7609 - val_loss: 0.7624\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7445 - val_loss: 0.7526\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7292 - val_loss: 0.7464\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7150 - val_loss: 0.7205\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7076 - val_loss: 0.7118\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6928 - val_loss: 0.6972\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.6844\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6611 - val_loss: 0.6654\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6423 - val_loss: 0.6477\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6313 - val_loss: 0.6356\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6113 - val_loss: 0.6156\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5967 - val_loss: 0.6031\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5812 - val_loss: 0.5875\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5702 - val_loss: 0.5728\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5558 - val_loss: 0.5591\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5361 - val_loss: 0.5422\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5235 - val_loss: 0.5270\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5084 - val_loss: 0.5119\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4930 - val_loss: 0.4952\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4763 - val_loss: 0.4804\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4594 - val_loss: 0.4669\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4478 - val_loss: 0.4494\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4318 - val_loss: 0.4370\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4136 - val_loss: 0.4334\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4057 - val_loss: 0.4085\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3844 - val_loss: 0.3921\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3693 - val_loss: 0.3802\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3657 - val_loss: 0.3635\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3474 - val_loss: 0.3528\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3396 - val_loss: 0.3365\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3189 - val_loss: 0.3179\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3041 - val_loss: 0.3039\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2797 - val_loss: 0.2888\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2678 - val_loss: 0.2714\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2664 - val_loss: 0.2648\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2430 - val_loss: 0.2442\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2275 - val_loss: 0.2299\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2174 - val_loss: 0.2133\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1952 - val_loss: 0.2043\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1787 - val_loss: 0.1842\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1641 - val_loss: 0.1706\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1502 - val_loss: 0.1541\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1363 - val_loss: 0.1366\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1143 - val_loss: 0.1261\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1053 - val_loss: 0.1179\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0897 - val_loss: 0.0969\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0726 - val_loss: 0.0757\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0586 - val_loss: 0.0688\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0413 - val_loss: 0.0489\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0385\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0374\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0381\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0407\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0405\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0350\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0399\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0259 - val_loss: 0.0431\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0397\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0377\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0354\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0355\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0168 - val_loss: 0.0316\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0378\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0337\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0377\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0426\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0451\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0412\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0425\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0359\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0429\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0378\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0386\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0387\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0384\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0378\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0224 - val_loss: 0.0372\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0166 - val_loss: 0.0369\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0373\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0160 - val_loss: 0.0372\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0374\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0148 - val_loss: 0.0366\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0366\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0367\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0368\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0368\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0141 - val_loss: 0.0368\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0366\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0364\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0112 - val_loss: 0.0363\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0163 - val_loss: 0.0363\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0252 - val_loss: 0.0364\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0364\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0364\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0364\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0364\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0364\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0147 - val_loss: 0.0365\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0365\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0170 - val_loss: 0.0365\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0189 - val_loss: 0.0365\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0365\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0365\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0365\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0365\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0187 - val_loss: 0.0365\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0365\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0365\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0113 - val_loss: 0.0365\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0109 - val_loss: 0.0365\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0124 - val_loss: 0.0365\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0168 - val_loss: 0.0365\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0123 - val_loss: 0.0365\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0365\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0365\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0136 - val_loss: 0.0365\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0206 - val_loss: 0.0365\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0170 - val_loss: 0.0365\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0136 - val_loss: 0.0365\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0135 - val_loss: 0.0365\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0186 - val_loss: 0.0365\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0365\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0365\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0365\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0149 - val_loss: 0.0365\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0142 - val_loss: 0.0365\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0365\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0365\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0150 - val_loss: 0.0365\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0365\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0365\n", + "COL: 比表面积, MSE: 2.48E-01,RMSE: 0.4978,MAPE: 4.0 %,MAE: 0.302,R_2: 0.2379\n", + "COL: 总孔体积, MSE: 3.02E-01,RMSE: 0.5491,MAPE: 28.02 %,MAE: 0.3058,R_2: 0.1327\n", + "COL: 微孔体积, MSE: 2.86E-02,RMSE: 0.169,MAPE: 28.199999999999996 %,MAE: 0.119,R_2: 0.7352\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 92ms/step - loss: 3.8534 - val_loss: 3.7567\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6472 - val_loss: 3.6221\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4827 - val_loss: 3.5718\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4439 - val_loss: 3.7812\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4902 - val_loss: 3.4990\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4199 - val_loss: 3.5033\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3520 - val_loss: 3.4131\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2274 - val_loss: 3.4744\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.3141 - val_loss: 3.3652\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.2422 - val_loss: 3.3192\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1901 - val_loss: 3.2861\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1546 - val_loss: 3.2805\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 8ms/step - loss: 3.1568 - val_loss: 3.1997\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0975 - val_loss: 3.1395\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0909 - val_loss: 3.1347\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0395 - val_loss: 3.0548\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0171 - val_loss: 3.0526\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8964 - val_loss: 3.0192\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9154 - val_loss: 3.0198\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8078 - val_loss: 2.9941\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7513 - val_loss: 2.9358\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8136 - val_loss: 2.8785\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.8047 - val_loss: 2.8693\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.7416 - val_loss: 2.8484\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.6788 - val_loss: 2.8676\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6403 - val_loss: 2.7848\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 2.5648 - val_loss: 2.6941\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5826 - val_loss: 2.6609\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6405 - val_loss: 2.6603\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4518 - val_loss: 2.5825\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4556 - val_loss: 2.5640\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3909 - val_loss: 2.5145\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4314 - val_loss: 2.5188\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3933 - val_loss: 2.4724\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.3968 - val_loss: 2.4456\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.2659 - val_loss: 2.3750\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3021 - val_loss: 2.3642\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.2431 - val_loss: 2.3579\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2053 - val_loss: 2.2751\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2433 - val_loss: 2.2323\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.0799 - val_loss: 2.2380\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.1074 - val_loss: 2.2114\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0792 - val_loss: 2.1425\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0379 - val_loss: 2.0959\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0600 - val_loss: 2.0612\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9412 - val_loss: 2.0333\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.9411 - val_loss: 2.0240\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.9220 - val_loss: 1.9449\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8270 - val_loss: 1.9371\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8160 - val_loss: 1.9008\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7859 - val_loss: 1.9029\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8180 - val_loss: 1.8543\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6860 - val_loss: 1.8061\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6871 - val_loss: 1.7867\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6814 - val_loss: 1.7409\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6678 - val_loss: 1.7037\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5697 - val_loss: 1.6629\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5739 - val_loss: 1.6251\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.5888\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5003 - val_loss: 1.5611\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4374 - val_loss: 1.5485\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4609 - val_loss: 1.5217\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3757 - val_loss: 1.4869\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3607 - val_loss: 1.4766\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3568 - val_loss: 1.4298\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3402 - val_loss: 1.4201\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2598 - val_loss: 1.3883\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2835 - val_loss: 1.4037\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3104 - val_loss: 1.3474\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.2975 - val_loss: 1.3322\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2389 - val_loss: 1.3066\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2247 - val_loss: 1.2726\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2057 - val_loss: 1.2699\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.1751 - val_loss: 1.2541\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1926 - val_loss: 1.2535\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1240 - val_loss: 1.2125\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1475 - val_loss: 1.1964\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1142 - val_loss: 1.1723\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0919 - val_loss: 1.1463\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0487 - val_loss: 1.1511\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0812 - val_loss: 1.1103\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0616 - val_loss: 1.1049\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0060 - val_loss: 1.0763\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9815 - val_loss: 1.0593\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9701 - val_loss: 1.0384\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0118 - val_loss: 1.0158\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9694 - val_loss: 0.9976\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8923 - val_loss: 0.9689\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8620 - val_loss: 0.9573\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8262 - val_loss: 0.9274\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8643 - val_loss: 0.9043\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7931 - val_loss: 0.8905\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7984 - val_loss: 0.8760\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7855 - val_loss: 0.8485\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7722 - val_loss: 0.8250\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7325 - val_loss: 0.8051\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7136 - val_loss: 0.7794\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6683 - val_loss: 0.7647\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7094 - val_loss: 0.7334\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6586 - val_loss: 0.7116\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6167 - val_loss: 0.6856\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6134 - val_loss: 0.6801\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6155 - val_loss: 0.6491\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5715 - val_loss: 0.6357\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5260 - val_loss: 0.6052\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.5269 - val_loss: 0.5999\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5412 - val_loss: 0.5573\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5057 - val_loss: 0.5422\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4933 - val_loss: 0.5037\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4462 - val_loss: 0.4962\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4479 - val_loss: 0.4797\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4036 - val_loss: 0.4708\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4004 - val_loss: 0.4384\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3423 - val_loss: 0.4223\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3605 - val_loss: 0.3956\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3069 - val_loss: 0.3667\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3148 - val_loss: 0.3675\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3581 - val_loss: 0.3360\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2954 - val_loss: 0.2939\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2562 - val_loss: 0.2863\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2189 - val_loss: 0.2752\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1909 - val_loss: 0.2655\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2091 - val_loss: 0.2517\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1874 - val_loss: 0.2449\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2034 - val_loss: 0.2362\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1790 - val_loss: 0.2391\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.2272\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1812 - val_loss: 0.2234\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1731 - val_loss: 0.2219\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1789 - val_loss: 0.2142\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1547 - val_loss: 0.2143\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1717 - val_loss: 0.1890\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1656 - val_loss: 0.1934\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1378 - val_loss: 0.1874\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1595 - val_loss: 0.1803\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1520 - val_loss: 0.1690\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1372 - val_loss: 0.1707\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1398 - val_loss: 0.1716\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1318 - val_loss: 0.1650\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1382 - val_loss: 0.1633\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1195 - val_loss: 0.1586\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1226 - val_loss: 0.1533\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1163 - val_loss: 0.1529\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1119 - val_loss: 0.1457\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1016 - val_loss: 0.1407\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1107 - val_loss: 0.1242\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1032 - val_loss: 0.1262\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0993 - val_loss: 0.1167\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0960 - val_loss: 0.1096\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0914 - val_loss: 0.1052\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1001 - val_loss: 0.1168\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1094 - val_loss: 0.1104\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1035 - val_loss: 0.0997\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1001 - val_loss: 0.0935\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0838 - val_loss: 0.0931\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0794 - val_loss: 0.0897\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0782 - val_loss: 0.0855\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0685 - val_loss: 0.0867\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0778 - val_loss: 0.0852\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0702 - val_loss: 0.0724\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0626 - val_loss: 0.0764\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0671 - val_loss: 0.0755\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0522 - val_loss: 0.0720\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0624 - val_loss: 0.0724\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0587 - val_loss: 0.0723\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0588 - val_loss: 0.0745\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0559 - val_loss: 0.0627\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0501 - val_loss: 0.0605\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0459 - val_loss: 0.0581\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0480 - val_loss: 0.0530\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0467 - val_loss: 0.0543\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0437 - val_loss: 0.0489\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0441 - val_loss: 0.0496\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0452 - val_loss: 0.0614\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0520\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0361 - val_loss: 0.0529\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0539\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0513\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0346 - val_loss: 0.0491\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0309 - val_loss: 0.0530\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0473\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0479\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0412 - val_loss: 0.0524\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0367 - val_loss: 0.0464\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0316 - val_loss: 0.0448\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0311 - val_loss: 0.0472\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0464\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0293 - val_loss: 0.0466\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0493\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0475\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0446\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0341 - val_loss: 0.0459\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0325 - val_loss: 0.0448\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0351 - val_loss: 0.0464\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0269 - val_loss: 0.0424\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0315 - val_loss: 0.0465\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0448\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0428\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0313 - val_loss: 0.0495\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0429\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0332 - val_loss: 0.0407\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0270 - val_loss: 0.0431\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0314 - val_loss: 0.0446\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0421\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0318 - val_loss: 0.0435\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0471\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0309 - val_loss: 0.0431\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0367\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0426\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0265 - val_loss: 0.0549\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0372 - val_loss: 0.0536\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0376 - val_loss: 0.0454\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0472\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0424\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0386\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0409\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0335 - val_loss: 0.0425\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0253 - val_loss: 0.0384\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0238 - val_loss: 0.0379\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0278 - val_loss: 0.0385\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0389\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0386\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0260 - val_loss: 0.0388\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0219 - val_loss: 0.0376\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0382\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0313 - val_loss: 0.0377\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0376\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0369\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0368\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0368\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0368\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0368\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0191 - val_loss: 0.0368\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0227 - val_loss: 0.0368\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0369\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0369\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0369\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0240 - val_loss: 0.0369\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0369\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0369\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0206 - val_loss: 0.0369\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0274 - val_loss: 0.0368\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0368\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0368\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0368\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0368\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0368\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0368\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0368\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0368\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0368\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0203 - val_loss: 0.0368\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0368\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0368\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0368\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0368\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0229 - val_loss: 0.0368\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0368\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0368\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0368\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0258 - val_loss: 0.0368\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0253 - val_loss: 0.0368\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0189 - val_loss: 0.0368\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0368\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0368\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0368\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0368\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0368\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0368\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0368\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0368\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0171 - val_loss: 0.0368\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0368\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0368\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0368\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0368\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0231 - val_loss: 0.0368\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0368\n", + "WARNING:tensorflow:5 out of the last 5 calls to .predict_function at 0x7f925acbf710> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 比表面积, MSE: 4.64E-02,RMSE: 0.2154,MAPE: 1.9900000000000002 %,MAE: 0.1412,R_2: 0.8076\n", + "COL: 总孔体积, MSE: 7.08E-02,RMSE: 0.2661,MAPE: 26.39 %,MAE: 0.2135,R_2: 0.7685\n", + "COL: 微孔体积, MSE: 6.68E-02,RMSE: 0.2585,MAPE: 32.42 %,MAE: 0.1907,R_2: 0.5484\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 129ms/step - loss: 4.2350 - val_loss: 4.1776\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1771 - val_loss: 4.1449\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.1223 - val_loss: 4.1071\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 4.0915 - val_loss: 4.0765\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0640 - val_loss: 4.0501\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 4.0344 - val_loss: 4.0214\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9994 - val_loss: 3.9829\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.9724 - val_loss: 3.9461\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.9340 - val_loss: 3.9392\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.9182 - val_loss: 3.8950\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8824 - val_loss: 3.8600\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.8552 - val_loss: 3.8238\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.8206 - val_loss: 3.8020\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7942 - val_loss: 3.7723\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.7578 - val_loss: 3.7352\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.7251 - val_loss: 3.7036\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6941 - val_loss: 3.6750\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6685 - val_loss: 3.6430\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6137\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.6061 - val_loss: 3.5869\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.5691 - val_loss: 3.5518\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.5421 - val_loss: 3.5220\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.5133 - val_loss: 3.4925\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4835 - val_loss: 3.4649\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4501 - val_loss: 3.4398\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.4284 - val_loss: 3.4076\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 3.3958 - val_loss: 3.3724\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3619 - val_loss: 3.3404\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.3317 - val_loss: 3.3143\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.3014 - val_loss: 3.2824\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2694 - val_loss: 3.2526\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2441 - val_loss: 3.2212\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.2127 - val_loss: 3.1935\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.1830 - val_loss: 3.1647\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.1553 - val_loss: 3.1322\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 3.1277 - val_loss: 3.1093\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 3.1034 - val_loss: 3.0807\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0715 - val_loss: 3.0497\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0388 - val_loss: 3.0153\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 3.0073 - val_loss: 2.9840\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9731 - val_loss: 2.9659\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.9443 - val_loss: 2.9278\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.9167 - val_loss: 2.8959\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8854 - val_loss: 2.8698\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8597 - val_loss: 2.8404\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.8206 - val_loss: 2.8021\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7924 - val_loss: 2.7780\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7626 - val_loss: 2.7478\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7273 - val_loss: 2.7211\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.7038 - val_loss: 2.6861\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6716 - val_loss: 2.6589\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6406 - val_loss: 2.6293\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.6130 - val_loss: 2.5971\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.5839 - val_loss: 2.5632\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.5519 - val_loss: 2.5352\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.5227 - val_loss: 2.5058\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4997 - val_loss: 2.4834\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 2.4671 - val_loss: 2.4525\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.4349 - val_loss: 2.4193\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.4018 - val_loss: 2.3864\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3721 - val_loss: 2.3595\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.3478 - val_loss: 2.3276\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.3097 - val_loss: 2.2973\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.2812 - val_loss: 2.2712\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2538 - val_loss: 2.2394\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2251 - val_loss: 2.2097\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 2.1940 - val_loss: 2.1783\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1662 - val_loss: 2.1537\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1373 - val_loss: 2.1187\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1071 - val_loss: 2.0918\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0699 - val_loss: 2.0571\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.0393 - val_loss: 2.0277\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0126 - val_loss: 1.9989\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9778 - val_loss: 1.9675\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9482 - val_loss: 1.9420\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9198 - val_loss: 1.9099\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8941 - val_loss: 1.8831\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8612 - val_loss: 1.8509\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.8312 - val_loss: 1.8193\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.7970 - val_loss: 1.7916\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7704 - val_loss: 1.7650\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.7411 - val_loss: 1.7316\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7098 - val_loss: 1.7020\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.6814 - val_loss: 1.6727\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6875 - val_loss: 1.6626\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.6399 - val_loss: 1.6227\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.6033 - val_loss: 1.5946\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5851 - val_loss: 1.5840\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5594 - val_loss: 1.5417\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5161 - val_loss: 1.4966\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4775 - val_loss: 1.4701\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4455 - val_loss: 1.4386\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4202 - val_loss: 1.4165\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3911 - val_loss: 1.3943\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.3817 - val_loss: 1.3952\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3680 - val_loss: 1.3710\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3466 - val_loss: 1.3535\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3385 - val_loss: 1.3448\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3236 - val_loss: 1.3263\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3062 - val_loss: 1.3080\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2936 - val_loss: 1.2946\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2701 - val_loss: 1.2771\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2579 - val_loss: 1.2622\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 1.2446 - val_loss: 1.2502\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2254 - val_loss: 1.2349\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2156 - val_loss: 1.2179\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1976 - val_loss: 1.2026\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1824 - val_loss: 1.1901\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1684 - val_loss: 1.1713\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1515 - val_loss: 1.1574\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1371 - val_loss: 1.1436\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1210 - val_loss: 1.1278\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1079 - val_loss: 1.1117\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0938 - val_loss: 1.0997\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0749 - val_loss: 1.0823\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0648 - val_loss: 1.0685\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0468 - val_loss: 1.0526\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0304 - val_loss: 1.0357\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0189 - val_loss: 1.0223\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0012 - val_loss: 1.0106\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9899 - val_loss: 0.9892\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9714 - val_loss: 0.9827\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9620 - val_loss: 0.9605\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9377 - val_loss: 0.9512\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9255 - val_loss: 0.9344\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9109 - val_loss: 0.9205\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8942 - val_loss: 0.9064\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8785 - val_loss: 0.8916\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8666 - val_loss: 0.8720\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8547 - val_loss: 0.8645\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8382 - val_loss: 0.8417\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8257 - val_loss: 0.8341\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8092 - val_loss: 0.8199\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7950 - val_loss: 0.7961\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7771 - val_loss: 0.7816\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7597 - val_loss: 0.7659\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7482 - val_loss: 0.7576\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7291 - val_loss: 0.7387\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7124 - val_loss: 0.7247\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7003 - val_loss: 0.7087\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6847 - val_loss: 0.6923\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6712 - val_loss: 0.6787\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6533 - val_loss: 0.6627\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.6407 - val_loss: 0.6493\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6234 - val_loss: 0.6340\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6140 - val_loss: 0.6221\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5983 - val_loss: 0.6026\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5782 - val_loss: 0.5869\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5630 - val_loss: 0.5720\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5503 - val_loss: 0.5613\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5378 - val_loss: 0.5463\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5210 - val_loss: 0.5323\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5066 - val_loss: 0.5201\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4905 - val_loss: 0.5045\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4781 - val_loss: 0.4834\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4675 - val_loss: 0.4931\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4599 - val_loss: 0.4624\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4379 - val_loss: 0.4451\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4309 - val_loss: 0.4277\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4098 - val_loss: 0.4075\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.3912 - val_loss: 0.4006\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3752 - val_loss: 0.3833\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3610 - val_loss: 0.3686\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.3427 - val_loss: 0.3496\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3290 - val_loss: 0.3453\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3412 - val_loss: 0.3276\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3087 - val_loss: 0.3055\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.2839 - val_loss: 0.2904\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2721 - val_loss: 0.2758\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2592 - val_loss: 0.2670\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2400 - val_loss: 0.2452\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2272 - val_loss: 0.2401\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2209 - val_loss: 0.2176\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1969 - val_loss: 0.2003\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1867 - val_loss: 0.1853\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1629 - val_loss: 0.1700\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1446 - val_loss: 0.1559\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1336 - val_loss: 0.1466\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1169 - val_loss: 0.1309\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1060 - val_loss: 0.1130\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0892 - val_loss: 0.0973\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0694 - val_loss: 0.0806\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0578 - val_loss: 0.0643\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0404 - val_loss: 0.0528\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0270 - val_loss: 0.0374\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0134 - val_loss: 0.0347\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0132 - val_loss: 0.0325\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0332\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0343\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0133 - val_loss: 0.0459\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0305 - val_loss: 0.0773\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0612\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0294 - val_loss: 0.0348\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0497\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0454\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0371 - val_loss: 0.0446\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0415\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0405\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0390\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0367\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0162 - val_loss: 0.0360\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0129 - val_loss: 0.0349\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0343\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0340\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0131 - val_loss: 0.0343\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0342\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0340\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0137 - val_loss: 0.0340\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0119 - val_loss: 0.0340\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0340\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0110 - val_loss: 0.0340\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0130 - val_loss: 0.0340\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0127 - val_loss: 0.0339\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0339\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0154 - val_loss: 0.0338\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0338\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0338\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0113 - val_loss: 0.0338\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0141 - val_loss: 0.0338\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0338\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0337\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0112 - val_loss: 0.0337\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0145 - val_loss: 0.0337\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0119 - val_loss: 0.0337\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0190 - val_loss: 0.0337\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0103 - val_loss: 0.0337\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0337\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0117 - val_loss: 0.0337\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0144 - val_loss: 0.0337\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0337\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0111 - val_loss: 0.0337\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0337\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0337\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0148 - val_loss: 0.0337\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0337\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0337\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0141 - val_loss: 0.0337\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0111 - val_loss: 0.0337\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0121 - val_loss: 0.0337\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0120 - val_loss: 0.0337\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0118 - val_loss: 0.0337\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0101 - val_loss: 0.0337\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0337\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0337\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0111 - val_loss: 0.0337\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0111 - val_loss: 0.0337\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0126 - val_loss: 0.0337\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0121 - val_loss: 0.0337\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0113 - val_loss: 0.0337\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0118 - val_loss: 0.0337\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0337\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0133 - val_loss: 0.0337\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0114 - val_loss: 0.0337\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0149 - val_loss: 0.0337\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0120 - val_loss: 0.0337\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0107 - val_loss: 0.0337\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0118 - val_loss: 0.0337\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0135 - val_loss: 0.0337\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0337\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0160 - val_loss: 0.0337\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0105 - val_loss: 0.0337\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0144 - val_loss: 0.0337\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0105 - val_loss: 0.0337\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0337\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0337\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0141 - val_loss: 0.0337\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0116 - val_loss: 0.0337\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0337\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0114 - val_loss: 0.0337\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0337\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0151 - val_loss: 0.0337\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0337\n", + "WARNING:tensorflow:6 out of the last 6 calls to .predict_function at 0x7f925b0ad5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 比表面积, MSE: 5.17E-01,RMSE: 0.7191,MAPE: 6.8500000000000005 %,MAE: 0.4346,R_2: -0.097\n", + "COL: 总孔体积, MSE: 4.80E-02,RMSE: 0.2191,MAPE: 26.93 %,MAE: 0.1605,R_2: 0.7029\n", + "COL: 微孔体积, MSE: 2.84E-02,RMSE: 0.1685,MAPE: 25.85 %,MAE: 0.1176,R_2: -0.2583\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 93ms/step - loss: 2.8437 - val_loss: 2.7256\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.2702 - val_loss: 2.2922\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.1285 - val_loss: 2.2275\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0685 - val_loss: 2.2036\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 2.1385 - val_loss: 2.1282\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.2805 - val_loss: 2.0756\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 2.0905 - val_loss: 2.0699\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9892 - val_loss: 1.9419\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.9954 - val_loss: 1.9155\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9310 - val_loss: 1.9522\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.8701 - val_loss: 1.7135\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7483 - val_loss: 1.7967\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.8521 - val_loss: 1.7321\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.9484 - val_loss: 1.6460\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6651 - val_loss: 1.5944\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7173 - val_loss: 1.7414\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7019 - val_loss: 1.6067\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5441 - val_loss: 1.4960\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.6313 - val_loss: 1.5536\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.5156 - val_loss: 1.5032\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3779 - val_loss: 1.4505\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3311 - val_loss: 1.5608\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3291 - val_loss: 1.4962\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6878 - val_loss: 1.5311\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.4834 - val_loss: 1.6844\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5190 - val_loss: 1.4949\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4538 - val_loss: 1.5663\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3375 - val_loss: 1.4548\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2507 - val_loss: 1.3878\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3738 - val_loss: 1.5167\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4214 - val_loss: 1.3964\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3014 - val_loss: 1.3950\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4419 - val_loss: 1.4415\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.3593 - val_loss: 1.5711\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.5495 - val_loss: 1.3657\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4482 - val_loss: 1.3499\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1884 - val_loss: 1.3848\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2024 - val_loss: 1.3474\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2285 - val_loss: 1.3377\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1912 - val_loss: 1.2759\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1634 - val_loss: 1.2572\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1176 - val_loss: 1.2016\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1759 - val_loss: 1.2444\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2093 - val_loss: 1.1987\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1634 - val_loss: 1.2866\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1392 - val_loss: 1.2245\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.1493 - val_loss: 1.1568\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1597 - val_loss: 1.2035\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1228 - val_loss: 1.1142\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0477 - val_loss: 1.1329\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0531 - val_loss: 1.1023\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0341 - val_loss: 1.1485\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0826 - val_loss: 1.1135\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0008 - val_loss: 1.1561\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9532 - val_loss: 1.1634\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 1.1328 - val_loss: 1.1310\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1301 - val_loss: 1.0378\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9525 - val_loss: 0.9811\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9580 - val_loss: 1.0715\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.9260 - val_loss: 1.0331\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9992 - val_loss: 1.0986\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9204 - val_loss: 0.9960\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8821 - val_loss: 0.9305\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8649 - val_loss: 1.1025\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9599 - val_loss: 1.0614\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8632 - val_loss: 1.0509\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.9691 - val_loss: 1.1349\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9685 - val_loss: 0.9593\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8677 - val_loss: 1.1233\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8588 - val_loss: 0.9489\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9050 - val_loss: 0.9777\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8488 - val_loss: 1.0039\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7910 - val_loss: 1.1226\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9317 - val_loss: 1.1152\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8711 - val_loss: 1.0150\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8256 - val_loss: 0.9442\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7474 - val_loss: 0.9368\n", + "Epoch 78/280\n", + "15/15 [==============================] - 1s 45ms/step - loss: 0.8441 - val_loss: 0.9155\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8362 - val_loss: 0.9105\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6999 - val_loss: 0.8841\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8195 - val_loss: 0.8843\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9369 - val_loss: 0.8868\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7068 - val_loss: 0.8889\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8259 - val_loss: 0.8968\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7408 - val_loss: 0.8902\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7861 - val_loss: 0.8727\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7262 - val_loss: 0.8774\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7490 - val_loss: 0.8948\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6618 - val_loss: 0.9126\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7437 - val_loss: 0.9090\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7419 - val_loss: 0.8906\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7467 - val_loss: 0.8905\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7137 - val_loss: 0.8789\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7013 - val_loss: 0.8966\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7207 - val_loss: 0.8925\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7286 - val_loss: 0.8800\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.7276 - val_loss: 0.8799\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7898 - val_loss: 0.8788\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6746 - val_loss: 0.8754\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7684 - val_loss: 0.8749\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7802 - val_loss: 0.8750\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8266 - val_loss: 0.8737\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7013 - val_loss: 0.8763\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7689 - val_loss: 0.8773\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6588 - val_loss: 0.8778\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7105 - val_loss: 0.8786\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7149 - val_loss: 0.8787\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7227 - val_loss: 0.8786\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8082 - val_loss: 0.8787\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6884 - val_loss: 0.8787\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7927 - val_loss: 0.8786\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7040 - val_loss: 0.8788\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8264 - val_loss: 0.8789\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7923 - val_loss: 0.8789\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7125 - val_loss: 0.8791\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7218 - val_loss: 0.8791\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8000 - val_loss: 0.8791\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7109 - val_loss: 0.8791\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.7337 - val_loss: 0.8791\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7503 - val_loss: 0.8792\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7335 - val_loss: 0.8791\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7336 - val_loss: 0.8792\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6997 - val_loss: 0.8792\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7372 - val_loss: 0.8792\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7741 - val_loss: 0.8792\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7265 - val_loss: 0.8792\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7360 - val_loss: 0.8792\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6817 - val_loss: 0.8792\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7266 - val_loss: 0.8792\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.7865 - val_loss: 0.8792\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7684 - val_loss: 0.8791\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6881 - val_loss: 0.8791\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7407 - val_loss: 0.8791\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6522 - val_loss: 0.8791\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7314 - val_loss: 0.8791\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7476 - val_loss: 0.8792\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7464 - val_loss: 0.8792\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6859 - val_loss: 0.8792\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7779 - val_loss: 0.8792\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6772 - val_loss: 0.8792\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8044 - val_loss: 0.8792\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7219 - val_loss: 0.8792\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.6646 - val_loss: 0.8792\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7779 - val_loss: 0.8792\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.6820 - val_loss: 0.8792\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7926 - val_loss: 0.8792\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7444 - val_loss: 0.8792\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6365 - val_loss: 0.8792\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6924 - val_loss: 0.8792\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7474 - val_loss: 0.8792\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7185 - val_loss: 0.8792\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7213 - val_loss: 0.8792\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6504 - val_loss: 0.8792\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7632 - val_loss: 0.8792\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7266 - val_loss: 0.8792\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7428 - val_loss: 0.8792\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6903 - val_loss: 0.8792\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7046 - val_loss: 0.8792\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7896 - val_loss: 0.8792\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7370 - val_loss: 0.8792\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7506 - val_loss: 0.8792\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8063 - val_loss: 0.8792\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.6800 - val_loss: 0.8792\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7201 - val_loss: 0.8792\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8093 - val_loss: 0.8792\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7165 - val_loss: 0.8792\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.7439 - val_loss: 0.8792\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7300 - val_loss: 0.8792\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7328 - val_loss: 0.8792\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7744 - val_loss: 0.8792\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7365 - val_loss: 0.8792\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7826 - val_loss: 0.8792\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7927 - val_loss: 0.8792\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7579 - val_loss: 0.8792\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.8792\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6909 - val_loss: 0.8792\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8066 - val_loss: 0.8792\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.7066 - val_loss: 0.8792\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7584 - val_loss: 0.8792\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.8062 - val_loss: 0.8792\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7403 - val_loss: 0.8792\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7637 - val_loss: 0.8792\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6912 - val_loss: 0.8792\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8562 - val_loss: 0.8792\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.6582 - val_loss: 0.8792\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7655 - val_loss: 0.8792\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7286 - val_loss: 0.8792\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6811 - val_loss: 0.8792\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7644 - val_loss: 0.8792\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7392 - val_loss: 0.8792\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7449 - val_loss: 0.8792\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7034 - val_loss: 0.8792\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7862 - val_loss: 0.8792\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6387 - val_loss: 0.8792\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6400 - val_loss: 0.8792\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7576 - val_loss: 0.8792\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7338 - val_loss: 0.8792\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7033 - val_loss: 0.8792\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7311 - val_loss: 0.8792\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6497 - val_loss: 0.8792\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6690 - val_loss: 0.8792\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7215 - val_loss: 0.8792\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6935 - val_loss: 0.8792\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7524 - val_loss: 0.8792\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7540 - val_loss: 0.8792\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.7667 - val_loss: 0.8792\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7282 - val_loss: 0.8792\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.6796 - val_loss: 0.8792\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7949 - val_loss: 0.8792\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7585 - val_loss: 0.8792\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6325 - val_loss: 0.8792\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7478 - val_loss: 0.8792\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8418 - val_loss: 0.8792\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7796 - val_loss: 0.8792\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6865 - val_loss: 0.8792\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7084 - val_loss: 0.8792\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7607 - val_loss: 0.8792\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7437 - val_loss: 0.8792\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7961 - val_loss: 0.8792\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7305 - val_loss: 0.8792\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7307 - val_loss: 0.8792\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7287 - val_loss: 0.8792\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7232 - val_loss: 0.8792\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7253 - val_loss: 0.8792\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7278 - val_loss: 0.8792\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7520 - val_loss: 0.8792\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7960 - val_loss: 0.8792\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6855 - val_loss: 0.8792\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6357 - val_loss: 0.8792\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.7140 - val_loss: 0.8792\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7365 - val_loss: 0.8792\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7653 - val_loss: 0.8792\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7424 - val_loss: 0.8792\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7894 - val_loss: 0.8792\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7743 - val_loss: 0.8792\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7000 - val_loss: 0.8792\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8851 - val_loss: 0.8792\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7107 - val_loss: 0.8792\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6897 - val_loss: 0.8792\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.6979 - val_loss: 0.8792\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7000 - val_loss: 0.8792\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7234 - val_loss: 0.8792\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.8380 - val_loss: 0.8792\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7250 - val_loss: 0.8792\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8193 - val_loss: 0.8792\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8497 - val_loss: 0.8792\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6870 - val_loss: 0.8792\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7090 - val_loss: 0.8792\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6365 - val_loss: 0.8792\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6939 - val_loss: 0.8792\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8079 - val_loss: 0.8792\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6614 - val_loss: 0.8792\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7441 - val_loss: 0.8792\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6896 - val_loss: 0.8792\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7967 - val_loss: 0.8792\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7492 - val_loss: 0.8792\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7201 - val_loss: 0.8792\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6958 - val_loss: 0.8792\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7668 - val_loss: 0.8792\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6800 - val_loss: 0.8792\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6966 - val_loss: 0.8792\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7082 - val_loss: 0.8792\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6733 - val_loss: 0.8792\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7413 - val_loss: 0.8792\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6969 - val_loss: 0.8792\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7081 - val_loss: 0.8792\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6802 - val_loss: 0.8792\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8230 - val_loss: 0.8792\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7199 - val_loss: 0.8792\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7807 - val_loss: 0.8792\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6880 - val_loss: 0.8792\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7277 - val_loss: 0.8792\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6574 - val_loss: 0.8792\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7361 - val_loss: 0.8792\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7203 - val_loss: 0.8792\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6350 - val_loss: 0.8792\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6775 - val_loss: 0.8792\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8293 - val_loss: 0.8792\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6803 - val_loss: 0.8792\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8161 - val_loss: 0.8792\n", + "COL: 比表面积, MSE: 3.15E+00,RMSE: 1.7749,MAPE: 23.9 %,MAE: 1.745,R_2: -9.6787\n", + "COL: 总孔体积, MSE: 2.57E+00,RMSE: 1.6022,MAPE: 267.31 %,MAE: 1.5898,R_2: -15.1375\n", + "COL: 微孔体积, MSE: 9.74E-01,RMSE: 0.9872,MAPE: 211.20000000000002 %,MAE: 0.9771,R_2: -7.2983\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 87ms/step - loss: 1.8668 - val_loss: 1.1992\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4484 - val_loss: 1.4379\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2950 - val_loss: 1.2383\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3274 - val_loss: 1.3438\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4247 - val_loss: 1.1749\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3966 - val_loss: 1.1068\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.2165 - val_loss: 1.1996\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.3044 - val_loss: 1.0921\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1830 - val_loss: 1.0342\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.1450 - val_loss: 1.1071\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0652 - val_loss: 0.8831\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0268 - val_loss: 0.8447\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9613 - val_loss: 1.0174\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0751 - val_loss: 0.8795\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9335 - val_loss: 0.8544\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8992 - val_loss: 0.8532\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.0178 - val_loss: 0.9018\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0000 - val_loss: 0.7768\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9628 - val_loss: 0.7968\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8164 - val_loss: 0.8871\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.9036 - val_loss: 0.7119\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7970 - val_loss: 0.8270\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9493 - val_loss: 0.7180\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8372 - val_loss: 0.7380\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8457 - val_loss: 0.7112\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7195 - val_loss: 0.6870\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.7943 - val_loss: 0.6575\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7529 - val_loss: 0.7097\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6944 - val_loss: 0.6601\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6873 - val_loss: 0.7422\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.6164\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6947 - val_loss: 0.6660\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6314 - val_loss: 0.7325\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7560 - val_loss: 0.6896\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7288 - val_loss: 0.6792\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7292 - val_loss: 0.6967\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6105 - val_loss: 0.6262\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7251 - val_loss: 0.5785\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6971 - val_loss: 0.5742\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6764 - val_loss: 0.6914\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7370 - val_loss: 0.7835\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6001 - val_loss: 0.6084\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5410 - val_loss: 0.6046\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6696 - val_loss: 0.5749\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6586 - val_loss: 0.5534\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5560 - val_loss: 0.6006\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6033 - val_loss: 0.5684\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5913 - val_loss: 0.6091\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6363 - val_loss: 0.5119\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5681 - val_loss: 0.5731\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5341 - val_loss: 0.5061\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5671 - val_loss: 0.5192\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5239 - val_loss: 0.6527\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5469 - val_loss: 0.5055\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5339 - val_loss: 0.5661\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5102 - val_loss: 0.5130\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4892 - val_loss: 0.5354\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5005 - val_loss: 0.4813\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5486 - val_loss: 0.4677\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.4578 - val_loss: 0.5658\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5319 - val_loss: 0.4716\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5149 - val_loss: 0.4338\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4531 - val_loss: 0.4298\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4922 - val_loss: 0.4973\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.5464 - val_loss: 0.5038\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4528 - val_loss: 0.4625\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4555 - val_loss: 0.4653\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4918 - val_loss: 0.4619\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4516 - val_loss: 0.4529\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4390 - val_loss: 0.4423\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4243 - val_loss: 0.4243\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4119 - val_loss: 0.5160\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4005 - val_loss: 0.4408\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4297 - val_loss: 0.4306\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4293 - val_loss: 0.4610\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4115 - val_loss: 0.3962\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3773 - val_loss: 0.4735\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3768 - val_loss: 0.4152\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4012 - val_loss: 0.4378\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4226 - val_loss: 0.4112\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4643 - val_loss: 0.4040\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3945 - val_loss: 0.3504\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3447 - val_loss: 0.3626\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4264 - val_loss: 0.4764\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5110 - val_loss: 0.3595\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3792 - val_loss: 0.4096\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3393 - val_loss: 0.3632\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3819 - val_loss: 0.3798\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3427 - val_loss: 0.3594\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3622 - val_loss: 0.3543\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3277 - val_loss: 0.3369\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3397 - val_loss: 0.3200\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3350 - val_loss: 0.3255\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2814 - val_loss: 0.3326\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2897 - val_loss: 0.3047\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.3236 - val_loss: 0.3356\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2907 - val_loss: 0.3490\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2919 - val_loss: 0.3174\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3346 - val_loss: 0.3385\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2826 - val_loss: 0.2934\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2927 - val_loss: 0.3279\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2928 - val_loss: 0.3109\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2679 - val_loss: 0.2795\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2976 - val_loss: 0.3162\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3202 - val_loss: 0.2745\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2536 - val_loss: 0.3388\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3343 - val_loss: 0.2917\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2965 - val_loss: 0.3187\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2604 - val_loss: 0.2665\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2710 - val_loss: 0.2802\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2739 - val_loss: 0.2383\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2721 - val_loss: 0.2425\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2272 - val_loss: 0.3003\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2751 - val_loss: 0.2282\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2650 - val_loss: 0.2294\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2643 - val_loss: 0.2636\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3353 - val_loss: 0.2320\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2664 - val_loss: 0.2094\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2427 - val_loss: 0.2413\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2390 - val_loss: 0.2177\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2449 - val_loss: 0.2402\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2631 - val_loss: 0.2137\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2387 - val_loss: 0.2068\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2433 - val_loss: 0.2341\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1834 - val_loss: 0.2072\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1973 - val_loss: 0.2419\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1934 - val_loss: 0.2124\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.2440 - val_loss: 0.2692\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2326 - val_loss: 0.2106\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1797 - val_loss: 0.1862\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2054 - val_loss: 0.2030\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1795 - val_loss: 0.2119\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2056 - val_loss: 0.2118\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2077 - val_loss: 0.2097\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2364 - val_loss: 0.2304\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1959 - val_loss: 0.2032\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1983 - val_loss: 0.2122\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2184 - val_loss: 0.2047\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1817 - val_loss: 0.2111\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1568 - val_loss: 0.2090\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1812 - val_loss: 0.2002\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1744 - val_loss: 0.1984\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1886 - val_loss: 0.1954\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1793 - val_loss: 0.1943\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1885 - val_loss: 0.1946\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2055 - val_loss: 0.1927\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2046 - val_loss: 0.1919\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2020 - val_loss: 0.1875\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1720 - val_loss: 0.1847\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1480 - val_loss: 0.1880\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1829 - val_loss: 0.1865\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1643 - val_loss: 0.1906\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1688 - val_loss: 0.1954\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1584 - val_loss: 0.1961\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1657 - val_loss: 0.1927\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1860 - val_loss: 0.1914\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1627 - val_loss: 0.1891\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1599 - val_loss: 0.1923\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1583 - val_loss: 0.1945\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1653 - val_loss: 0.1946\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1630 - val_loss: 0.1938\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1849 - val_loss: 0.1935\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1816 - val_loss: 0.1934\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1787 - val_loss: 0.1929\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1755 - val_loss: 0.1932\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1952 - val_loss: 0.1934\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1885 - val_loss: 0.1932\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1582 - val_loss: 0.1931\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1774 - val_loss: 0.1935\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1551 - val_loss: 0.1935\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1984 - val_loss: 0.1935\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1613 - val_loss: 0.1936\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1437 - val_loss: 0.1936\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1804 - val_loss: 0.1936\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1749 - val_loss: 0.1936\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1621 - val_loss: 0.1936\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1619 - val_loss: 0.1937\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1722 - val_loss: 0.1936\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1642 - val_loss: 0.1936\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1880 - val_loss: 0.1936\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1713 - val_loss: 0.1936\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2098 - val_loss: 0.1936\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1343 - val_loss: 0.1936\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1974 - val_loss: 0.1936\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1773 - val_loss: 0.1936\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1614 - val_loss: 0.1936\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1606 - val_loss: 0.1936\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1926 - val_loss: 0.1936\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1581 - val_loss: 0.1936\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1727 - val_loss: 0.1936\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1736 - val_loss: 0.1936\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1535 - val_loss: 0.1936\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1823 - val_loss: 0.1936\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1547 - val_loss: 0.1936\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1697 - val_loss: 0.1936\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1593 - val_loss: 0.1936\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1424 - val_loss: 0.1936\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1490 - val_loss: 0.1936\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1651 - val_loss: 0.1936\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1833 - val_loss: 0.1936\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1759 - val_loss: 0.1936\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1895 - val_loss: 0.1936\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1532 - val_loss: 0.1936\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1492 - val_loss: 0.1936\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1820 - val_loss: 0.1936\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1774 - val_loss: 0.1936\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1547 - val_loss: 0.1936\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.1821 - val_loss: 0.1936\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1591 - val_loss: 0.1936\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1618 - val_loss: 0.1936\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1773 - val_loss: 0.1936\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1511 - val_loss: 0.1936\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1963 - val_loss: 0.1936\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1926 - val_loss: 0.1936\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1658 - val_loss: 0.1936\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2016 - val_loss: 0.1936\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 8ms/step - loss: 0.1397 - val_loss: 0.1936\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.1688 - val_loss: 0.1936\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 9ms/step - loss: 0.1774 - val_loss: 0.1936\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1549 - val_loss: 0.1936\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1472 - val_loss: 0.1936\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1730 - val_loss: 0.1936\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1665 - val_loss: 0.1936\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1703 - val_loss: 0.1936\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1613 - val_loss: 0.1936\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1654 - val_loss: 0.1936\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1493 - val_loss: 0.1936\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1761 - val_loss: 0.1936\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1988 - val_loss: 0.1936\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1459 - val_loss: 0.1936\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1515 - val_loss: 0.1936\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1596 - val_loss: 0.1936\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1751 - val_loss: 0.1936\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1762 - val_loss: 0.1936\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1753 - val_loss: 0.1936\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1923 - val_loss: 0.1936\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1639 - val_loss: 0.1936\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1644 - val_loss: 0.1936\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1670 - val_loss: 0.1936\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1719 - val_loss: 0.1936\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1763 - val_loss: 0.1936\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1647 - val_loss: 0.1936\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1762 - val_loss: 0.1936\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1605 - val_loss: 0.1936\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1380 - val_loss: 0.1936\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1861 - val_loss: 0.1936\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1579 - val_loss: 0.1936\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1840 - val_loss: 0.1936\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1631 - val_loss: 0.1936\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1912 - val_loss: 0.1936\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1586 - val_loss: 0.1936\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1587 - val_loss: 0.1936\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1489 - val_loss: 0.1936\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1425 - val_loss: 0.1936\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1698 - val_loss: 0.1936\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1693 - val_loss: 0.1936\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1612 - val_loss: 0.1936\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.1393 - val_loss: 0.1936\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1759 - val_loss: 0.1936\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1346 - val_loss: 0.1936\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1715 - val_loss: 0.1936\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1747 - val_loss: 0.1936\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1783 - val_loss: 0.1936\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2145 - val_loss: 0.1936\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1618 - val_loss: 0.1936\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1594 - val_loss: 0.1936\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1733 - val_loss: 0.1936\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1540 - val_loss: 0.1936\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1798 - val_loss: 0.1936\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1628 - val_loss: 0.1936\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1702 - val_loss: 0.1936\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1807 - val_loss: 0.1936\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1776 - val_loss: 0.1936\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1753 - val_loss: 0.1936\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1638 - val_loss: 0.1936\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1876 - val_loss: 0.1936\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1817 - val_loss: 0.1936\n", + "COL: 比表面积, MSE: 3.31E+00,RMSE: 1.8196,MAPE: 23.23 %,MAE: 1.7385,R_2: -5.7583\n", + "COL: 总孔体积, MSE: 2.00E-01,RMSE: 0.4469,MAPE: 73.2 %,MAE: 0.3261,R_2: 0.383\n", + "COL: 微孔体积, MSE: 8.91E-02,RMSE: 0.2985,MAPE: 67.27 %,MAE: 0.2204,R_2: -0.409\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 87ms/step - loss: 1.9754 - val_loss: 1.9920\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.8934 - val_loss: 1.8108\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8678 - val_loss: 1.7804\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8099 - val_loss: 1.7808\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7852 - val_loss: 1.7365\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8005 - val_loss: 1.7159\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.8002 - val_loss: 1.7278\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.7150 - val_loss: 1.6841\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.7070 - val_loss: 1.6588\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6771 - val_loss: 1.6260\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.6522 - val_loss: 1.6379\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.6243 - val_loss: 1.5841\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5996 - val_loss: 1.5591\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.6091 - val_loss: 1.5598\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.5881 - val_loss: 1.5620\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5255 - val_loss: 1.5440\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5364 - val_loss: 1.5459\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.5452 - val_loss: 1.5002\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4664 - val_loss: 1.4460\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4699 - val_loss: 1.4584\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4165 - val_loss: 1.4341\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.4157 - val_loss: 1.4142\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4067 - val_loss: 1.4117\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3716 - val_loss: 1.3767\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3796 - val_loss: 1.3504\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.3564 - val_loss: 1.3289\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2984 - val_loss: 1.3355\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2968 - val_loss: 1.3116\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2898 - val_loss: 1.2716\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2706 - val_loss: 1.2734\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2616 - val_loss: 1.2720\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.2315 - val_loss: 1.2208\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2314 - val_loss: 1.2156\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2090 - val_loss: 1.1900\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1728 - val_loss: 1.1776\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1574 - val_loss: 1.1586\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1426 - val_loss: 1.1430\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1540 - val_loss: 1.1104\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1075 - val_loss: 1.1120\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0754 - val_loss: 1.0676\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0708 - val_loss: 1.0648\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 1.0629 - val_loss: 1.0402\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0278 - val_loss: 1.0344\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0377 - val_loss: 1.0089\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0029 - val_loss: 0.9939\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9828 - val_loss: 0.9852\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9729 - val_loss: 0.9580\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9498 - val_loss: 0.9360\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9313 - val_loss: 0.9178\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.9114 - val_loss: 0.9116\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8911 - val_loss: 0.8786\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8728 - val_loss: 0.8649\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8607 - val_loss: 0.8530\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8456 - val_loss: 0.8272\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8353 - val_loss: 0.8141\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.8056 - val_loss: 0.8010\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.7975 - val_loss: 0.7785\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7706 - val_loss: 0.7631\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7487 - val_loss: 0.7437\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7475 - val_loss: 0.7329\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.7218 - val_loss: 0.7051\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6961 - val_loss: 0.7014\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6748 - val_loss: 0.6711\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6677 - val_loss: 0.6656\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6512 - val_loss: 0.6350\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6440 - val_loss: 0.6271\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.6210 - val_loss: 0.6103\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5943 - val_loss: 0.5891\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5832 - val_loss: 0.5770\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5636 - val_loss: 0.5581\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5530 - val_loss: 0.5416\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.5373 - val_loss: 0.5298\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5210 - val_loss: 0.5089\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5073 - val_loss: 0.5021\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4912 - val_loss: 0.4890\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4719 - val_loss: 0.4673\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4608 - val_loss: 0.4523\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4363 - val_loss: 0.4391\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.4274 - val_loss: 0.4227\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.4084 - val_loss: 0.4116\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3988 - val_loss: 0.3912\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3783 - val_loss: 0.3845\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3693 - val_loss: 0.3615\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3551 - val_loss: 0.3554\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3354 - val_loss: 0.3415\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3240 - val_loss: 0.3161\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.3123 - val_loss: 0.3055\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.2883 - val_loss: 0.2887\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2689 - val_loss: 0.2721\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2605 - val_loss: 0.2620\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2468 - val_loss: 0.2441\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2334 - val_loss: 0.2355\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2148 - val_loss: 0.2202\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2101 - val_loss: 0.1989\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1962 - val_loss: 0.1927\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1746 - val_loss: 0.1719\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1619 - val_loss: 0.1531\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.1445 - val_loss: 0.1398\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1297 - val_loss: 0.1240\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1157 - val_loss: 0.1125\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0919 - val_loss: 0.0965\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0815 - val_loss: 0.0808\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0625 - val_loss: 0.0682\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0499 - val_loss: 0.0650\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0425 - val_loss: 0.0400\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0262 - val_loss: 0.0429\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0370\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0235 - val_loss: 0.0440\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0243 - val_loss: 0.0438\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0374\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0298 - val_loss: 0.0396\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0444\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0318 - val_loss: 0.0488\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0515\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0436\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0380 - val_loss: 0.0419\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0430\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0451\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0450\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0427\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0263 - val_loss: 0.0422\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0261 - val_loss: 0.0422\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0432\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0220 - val_loss: 0.0437\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0433\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0434\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0327 - val_loss: 0.0443\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0255 - val_loss: 0.0442\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0443\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0244 - val_loss: 0.0444\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0443\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0443\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0442\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0444\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0215 - val_loss: 0.0444\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0444\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0445\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0210 - val_loss: 0.0445\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0182 - val_loss: 0.0445\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0445\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0445\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0445\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0252 - val_loss: 0.0445\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0446\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0446\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0446\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0174 - val_loss: 0.0446\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0446\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0233 - val_loss: 0.0446\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0201 - val_loss: 0.0446\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0446\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0260 - val_loss: 0.0446\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0446\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0446\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0446\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0446\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0446\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0446\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0165 - val_loss: 0.0446\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0188 - val_loss: 0.0446\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0446\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0234 - val_loss: 0.0446\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0319 - val_loss: 0.0446\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0446\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0446\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0446\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0183 - val_loss: 0.0446\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0446\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0320 - val_loss: 0.0446\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0446\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0446\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0446\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0191 - val_loss: 0.0446\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0237 - val_loss: 0.0446\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0446\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0191 - val_loss: 0.0446\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0278 - val_loss: 0.0446\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0446\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0446\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0204 - val_loss: 0.0446\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0446\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0446\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0446\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0446\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0446\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0446\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0446\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0446\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0446\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0446\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0446\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0446\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0446\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0250 - val_loss: 0.0446\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0446\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0177 - val_loss: 0.0446\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0446\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0446\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0274 - val_loss: 0.0446\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0446\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0446\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0223 - val_loss: 0.0446\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0446\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0226 - val_loss: 0.0446\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0446\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0446\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0446\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0180 - val_loss: 0.0446\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0255 - val_loss: 0.0446\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0446\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0446\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0221 - val_loss: 0.0446\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0256 - val_loss: 0.0446\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0446\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0206 - val_loss: 0.0446\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0446\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0446\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0446\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0446\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0446\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0446\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0253 - val_loss: 0.0446\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0218 - val_loss: 0.0446\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0446\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0203 - val_loss: 0.0446\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0446\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0254 - val_loss: 0.0446\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0446\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0260 - val_loss: 0.0446\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0446\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0446\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0446\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0446\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0446\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0446\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0446\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0237 - val_loss: 0.0446\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0225 - val_loss: 0.0446\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0446\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0346 - val_loss: 0.0446\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0446\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0212 - val_loss: 0.0446\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0218 - val_loss: 0.0446\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0446\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0446\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0446\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0446\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0446\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0446\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0446\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0222 - val_loss: 0.0446\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0218 - val_loss: 0.0446\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0446\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0446\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0446\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0259 - val_loss: 0.0446\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0446\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0446\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0205 - val_loss: 0.0446\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0446\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0446\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0446\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0446\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0446\n", + "COL: 比表面积, MSE: 8.57E-02,RMSE: 0.2928,MAPE: 3.18 %,MAE: 0.2321,R_2: 0.6612\n", + "COL: 总孔体积, MSE: 1.47E-01,RMSE: 0.3828,MAPE: 23.830000000000002 %,MAE: 0.2373,R_2: 0.5847\n", + "COL: 微孔体积, MSE: 3.54E-02,RMSE: 0.1882,MAPE: 25.540000000000003 %,MAE: 0.1482,R_2: 0.6431\n", + "Epoch 1/280\n", + "15/15 [==============================] - 6s 87ms/step - loss: 1.7915 - val_loss: 1.5831\n", + "Epoch 2/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.4854 - val_loss: 1.5479\n", + "Epoch 3/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.4388 - val_loss: 1.5548\n", + "Epoch 4/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4186 - val_loss: 1.5243\n", + "Epoch 5/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4387 - val_loss: 1.4333\n", + "Epoch 6/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.3341 - val_loss: 1.4208\n", + "Epoch 7/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.4012 - val_loss: 1.3857\n", + "Epoch 8/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2815 - val_loss: 1.3240\n", + "Epoch 9/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.2213 - val_loss: 1.2801\n", + "Epoch 10/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 1.1352 - val_loss: 1.2286\n", + "Epoch 11/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1376 - val_loss: 1.1920\n", + "Epoch 12/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.1759 - val_loss: 1.1531\n", + "Epoch 13/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0811 - val_loss: 1.0861\n", + "Epoch 14/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0537 - val_loss: 1.0637\n", + "Epoch 15/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 1.0311 - val_loss: 1.0354\n", + "Epoch 16/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 1.0069 - val_loss: 0.9676\n", + "Epoch 17/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9223 - val_loss: 0.9667\n", + "Epoch 18/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.8913 - val_loss: 0.9186\n", + "Epoch 19/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.9217 - val_loss: 0.9701\n", + "Epoch 20/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.9211 - val_loss: 0.8864\n", + "Epoch 21/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.8601 - val_loss: 0.8302\n", + "Epoch 22/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.8128 - val_loss: 0.8375\n", + "Epoch 23/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7730 - val_loss: 0.7847\n", + "Epoch 24/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.7332 - val_loss: 0.7959\n", + "Epoch 25/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6908 - val_loss: 0.7689\n", + "Epoch 26/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.7353 - val_loss: 0.7359\n", + "Epoch 27/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.6959 - val_loss: 0.6988\n", + "Epoch 28/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6375 - val_loss: 0.6795\n", + "Epoch 29/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.6234 - val_loss: 0.6861\n", + "Epoch 30/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5952 - val_loss: 0.6636\n", + "Epoch 31/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5796 - val_loss: 0.6411\n", + "Epoch 32/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5588 - val_loss: 0.5939\n", + "Epoch 33/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.5059 - val_loss: 0.5869\n", + "Epoch 34/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.5317 - val_loss: 0.5622\n", + "Epoch 35/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5272 - val_loss: 0.5156\n", + "Epoch 36/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.5096 - val_loss: 0.5028\n", + "Epoch 37/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4625 - val_loss: 0.4561\n", + "Epoch 38/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.4435 - val_loss: 0.4643\n", + "Epoch 39/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.4074 - val_loss: 0.3901\n", + "Epoch 40/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3852 - val_loss: 0.3962\n", + "Epoch 41/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3540 - val_loss: 0.3499\n", + "Epoch 42/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.3305 - val_loss: 0.3387\n", + "Epoch 43/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.2808 - val_loss: 0.3249\n", + "Epoch 44/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2860 - val_loss: 0.2871\n", + "Epoch 45/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2449 - val_loss: 0.2715\n", + "Epoch 46/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.2613 - val_loss: 0.2669\n", + "Epoch 47/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.2432 - val_loss: 0.2790\n", + "Epoch 48/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.2318 - val_loss: 0.2070\n", + "Epoch 49/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1715 - val_loss: 0.2061\n", + "Epoch 50/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1673 - val_loss: 0.1978\n", + "Epoch 51/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1722 - val_loss: 0.2138\n", + "Epoch 52/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1803 - val_loss: 0.2211\n", + "Epoch 53/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1670 - val_loss: 0.2144\n", + "Epoch 54/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1683 - val_loss: 0.1846\n", + "Epoch 55/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1614 - val_loss: 0.1948\n", + "Epoch 56/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.1550 - val_loss: 0.1736\n", + "Epoch 57/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1552 - val_loss: 0.1860\n", + "Epoch 58/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1453 - val_loss: 0.1971\n", + "Epoch 59/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1453 - val_loss: 0.1728\n", + "Epoch 60/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1203 - val_loss: 0.1919\n", + "Epoch 61/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1411 - val_loss: 0.1633\n", + "Epoch 62/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1373 - val_loss: 0.1609\n", + "Epoch 63/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1333 - val_loss: 0.1626\n", + "Epoch 64/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1308 - val_loss: 0.1722\n", + "Epoch 65/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1480 - val_loss: 0.1668\n", + "Epoch 66/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1385 - val_loss: 0.1745\n", + "Epoch 67/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1379 - val_loss: 0.1651\n", + "Epoch 68/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1351 - val_loss: 0.1787\n", + "Epoch 69/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1341 - val_loss: 0.1736\n", + "Epoch 70/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1363 - val_loss: 0.1608\n", + "Epoch 71/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1263 - val_loss: 0.1555\n", + "Epoch 72/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1054 - val_loss: 0.1488\n", + "Epoch 73/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1091 - val_loss: 0.1449\n", + "Epoch 74/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1201 - val_loss: 0.1533\n", + "Epoch 75/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1039 - val_loss: 0.1477\n", + "Epoch 76/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0856 - val_loss: 0.1472\n", + "Epoch 77/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1063 - val_loss: 0.1346\n", + "Epoch 78/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.1038 - val_loss: 0.1357\n", + "Epoch 79/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0987 - val_loss: 0.1458\n", + "Epoch 80/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0980 - val_loss: 0.1408\n", + "Epoch 81/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1013 - val_loss: 0.1488\n", + "Epoch 82/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.1106 - val_loss: 0.1252\n", + "Epoch 83/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0947 - val_loss: 0.1318\n", + "Epoch 84/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.1007 - val_loss: 0.1268\n", + "Epoch 85/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0989 - val_loss: 0.1345\n", + "Epoch 86/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0691 - val_loss: 0.1216\n", + "Epoch 87/280\n", + "15/15 [==============================] - 0s 9ms/step - loss: 0.0807 - val_loss: 0.1239\n", + "Epoch 88/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0750 - val_loss: 0.1082\n", + "Epoch 89/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0807 - val_loss: 0.1143\n", + "Epoch 90/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0740 - val_loss: 0.1164\n", + "Epoch 91/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0715 - val_loss: 0.1135\n", + "Epoch 92/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0710 - val_loss: 0.1208\n", + "Epoch 93/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0686 - val_loss: 0.1155\n", + "Epoch 94/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0684 - val_loss: 0.0992\n", + "Epoch 95/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0770 - val_loss: 0.1185\n", + "Epoch 96/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0633 - val_loss: 0.0914\n", + "Epoch 97/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0657 - val_loss: 0.0985\n", + "Epoch 98/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0635 - val_loss: 0.0949\n", + "Epoch 99/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0546 - val_loss: 0.0930\n", + "Epoch 100/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0536 - val_loss: 0.0830\n", + "Epoch 101/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0486 - val_loss: 0.0842\n", + "Epoch 102/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0479 - val_loss: 0.0841\n", + "Epoch 103/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0545 - val_loss: 0.0764\n", + "Epoch 104/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0476 - val_loss: 0.0760\n", + "Epoch 105/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0482 - val_loss: 0.0715\n", + "Epoch 106/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0476 - val_loss: 0.0705\n", + "Epoch 107/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0411 - val_loss: 0.0652\n", + "Epoch 108/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0474 - val_loss: 0.0638\n", + "Epoch 109/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0408 - val_loss: 0.0665\n", + "Epoch 110/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0408 - val_loss: 0.0645\n", + "Epoch 111/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0369 - val_loss: 0.0660\n", + "Epoch 112/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0345 - val_loss: 0.0544\n", + "Epoch 113/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0384 - val_loss: 0.0657\n", + "Epoch 114/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0342 - val_loss: 0.0542\n", + "Epoch 115/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0341 - val_loss: 0.0580\n", + "Epoch 116/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0588\n", + "Epoch 117/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0608\n", + "Epoch 118/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0521\n", + "Epoch 119/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0333 - val_loss: 0.0608\n", + "Epoch 120/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0640\n", + "Epoch 121/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0703\n", + "Epoch 122/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0619\n", + "Epoch 123/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0253 - val_loss: 0.0610\n", + "Epoch 124/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0299 - val_loss: 0.0641\n", + "Epoch 125/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0281 - val_loss: 0.0571\n", + "Epoch 126/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0676\n", + "Epoch 127/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0286 - val_loss: 0.0611\n", + "Epoch 128/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0323 - val_loss: 0.0576\n", + "Epoch 129/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0573\n", + "Epoch 130/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0563\n", + "Epoch 131/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0201 - val_loss: 0.0559\n", + "Epoch 132/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0563\n", + "Epoch 133/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0564\n", + "Epoch 134/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0286 - val_loss: 0.0567\n", + "Epoch 135/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0176 - val_loss: 0.0560\n", + "Epoch 136/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0300 - val_loss: 0.0556\n", + "Epoch 137/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0544\n", + "Epoch 138/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0538\n", + "Epoch 139/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0538\n", + "Epoch 140/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0539\n", + "Epoch 141/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0540\n", + "Epoch 142/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0224 - val_loss: 0.0541\n", + "Epoch 143/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0542\n", + "Epoch 144/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0228 - val_loss: 0.0542\n", + "Epoch 145/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0259 - val_loss: 0.0542\n", + "Epoch 146/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0543\n", + "Epoch 147/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0258 - val_loss: 0.0544\n", + "Epoch 148/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0544\n", + "Epoch 149/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0224 - val_loss: 0.0544\n", + "Epoch 150/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0544\n", + "Epoch 151/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0544\n", + "Epoch 152/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0544\n", + "Epoch 153/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0183 - val_loss: 0.0544\n", + "Epoch 154/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0544\n", + "Epoch 155/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0544\n", + "Epoch 156/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0544\n", + "Epoch 157/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0544\n", + "Epoch 158/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0203 - val_loss: 0.0544\n", + "Epoch 159/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0181 - val_loss: 0.0544\n", + "Epoch 160/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0544\n", + "Epoch 161/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544\n", + "Epoch 162/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0544\n", + "Epoch 163/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0151 - val_loss: 0.0544\n", + "Epoch 164/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0239 - val_loss: 0.0544\n", + "Epoch 165/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544\n", + "Epoch 166/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0544\n", + "Epoch 167/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544\n", + "Epoch 168/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0544\n", + "Epoch 169/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0544\n", + "Epoch 170/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0544\n", + "Epoch 171/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544\n", + "Epoch 172/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0544\n", + "Epoch 173/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544\n", + "Epoch 174/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0313 - val_loss: 0.0544\n", + "Epoch 175/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0544\n", + "Epoch 176/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0317 - val_loss: 0.0544\n", + "Epoch 177/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0544\n", + "Epoch 178/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0544\n", + "Epoch 179/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0231 - val_loss: 0.0544\n", + "Epoch 180/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544\n", + "Epoch 181/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0215 - val_loss: 0.0544\n", + "Epoch 182/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0544\n", + "Epoch 183/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544\n", + "Epoch 184/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0206 - val_loss: 0.0544\n", + "Epoch 185/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0544\n", + "Epoch 186/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0544\n", + "Epoch 187/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0544\n", + "Epoch 188/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0544\n", + "Epoch 189/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0243 - val_loss: 0.0544\n", + "Epoch 190/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0544\n", + "Epoch 191/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0544\n", + "Epoch 192/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0204 - val_loss: 0.0544\n", + "Epoch 193/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0544\n", + "Epoch 194/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0227 - val_loss: 0.0544\n", + "Epoch 195/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0544\n", + "Epoch 196/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0544\n", + "Epoch 197/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0544\n", + "Epoch 198/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0188 - val_loss: 0.0544\n", + "Epoch 199/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544\n", + "Epoch 200/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0544\n", + "Epoch 201/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0544\n", + "Epoch 202/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0283 - val_loss: 0.0544\n", + "Epoch 203/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0544\n", + "Epoch 204/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0544\n", + "Epoch 205/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0544\n", + "Epoch 206/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0544\n", + "Epoch 207/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0544\n", + "Epoch 208/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544\n", + "Epoch 209/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0544\n", + "Epoch 210/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544\n", + "Epoch 211/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0544\n", + "Epoch 212/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0544\n", + "Epoch 213/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0544\n", + "Epoch 214/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0544\n", + "Epoch 215/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0544\n", + "Epoch 216/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0544\n", + "Epoch 217/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0544\n", + "Epoch 218/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544\n", + "Epoch 219/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544\n", + "Epoch 220/280\n", + "15/15 [==============================] - 0s 16ms/step - loss: 0.0200 - val_loss: 0.0544\n", + "Epoch 221/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0544\n", + "Epoch 222/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0183 - val_loss: 0.0544\n", + "Epoch 223/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0544\n", + "Epoch 224/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0544\n", + "Epoch 225/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0544\n", + "Epoch 226/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544\n", + "Epoch 227/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0248 - val_loss: 0.0544\n", + "Epoch 228/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0238 - val_loss: 0.0544\n", + "Epoch 229/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544\n", + "Epoch 230/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0544\n", + "Epoch 231/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0544\n", + "Epoch 232/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544\n", + "Epoch 233/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544\n", + "Epoch 234/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0544\n", + "Epoch 235/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0544\n", + "Epoch 236/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0544\n", + "Epoch 237/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0544\n", + "Epoch 238/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544\n", + "Epoch 239/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0211 - val_loss: 0.0544\n", + "Epoch 240/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0263 - val_loss: 0.0544\n", + "Epoch 241/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0544\n", + "Epoch 242/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0544\n", + "Epoch 243/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0544\n", + "Epoch 244/280\n", + "15/15 [==============================] - 0s 17ms/step - loss: 0.0166 - val_loss: 0.0544\n", + "Epoch 245/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544\n", + "Epoch 246/280\n", + "15/15 [==============================] - 0s 10ms/step - loss: 0.0247 - val_loss: 0.0544\n", + "Epoch 247/280\n", + "15/15 [==============================] - 0s 11ms/step - loss: 0.0236 - val_loss: 0.0544\n", + "Epoch 248/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544\n", + "Epoch 249/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0226 - val_loss: 0.0544\n", + "Epoch 250/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0544\n", + "Epoch 251/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0544\n", + "Epoch 252/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0544\n", + "Epoch 253/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0265 - val_loss: 0.0544\n", + "Epoch 254/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0544\n", + "Epoch 255/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0211 - val_loss: 0.0544\n", + "Epoch 256/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0544\n", + "Epoch 257/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0544\n", + "Epoch 258/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0544\n", + "Epoch 259/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0248 - val_loss: 0.0544\n", + "Epoch 260/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0544\n", + "Epoch 261/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0544\n", + "Epoch 262/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0210 - val_loss: 0.0544\n", + "Epoch 263/280\n", + "15/15 [==============================] - 0s 12ms/step - loss: 0.0240 - val_loss: 0.0544\n", + "Epoch 264/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0544\n", + "Epoch 265/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0163 - val_loss: 0.0544\n", + "Epoch 266/280\n", + "15/15 [==============================] - 0s 15ms/step - loss: 0.0201 - val_loss: 0.0544\n", + "Epoch 267/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0216 - val_loss: 0.0544\n", + "Epoch 268/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0544\n", + "Epoch 269/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0265 - val_loss: 0.0544\n", + "Epoch 270/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0193 - val_loss: 0.0544\n", + "Epoch 271/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0544\n", + "Epoch 272/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544\n", + "Epoch 273/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0544\n", + "Epoch 274/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0544\n", + "Epoch 275/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0544\n", + "Epoch 276/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0150 - val_loss: 0.0544\n", + "Epoch 277/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0544\n", + "Epoch 278/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0544\n", + "Epoch 279/280\n", + "15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0544\n", + "Epoch 280/280\n", + "15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0544\n", + "COL: 比表面积, MSE: 5.96E-02,RMSE: 0.244,MAPE: 2.69 %,MAE: 0.2008,R_2: 0.6614\n", + "COL: 总孔体积, MSE: 1.18E-01,RMSE: 0.3435,MAPE: 23.05 %,MAE: 0.2474,R_2: 0.5394\n", + "COL: 微孔体积, MSE: 1.40E-02,RMSE: 0.1182,MAPE: 12.839999999999998 %,MAE: 0.0836,R_2: 0.6906\n" + ] + } + ], + "source": [ + "total_bet = list()\n", + "total_micro = list()\n", + "total_mesco = list()\n", + "for train_index, test_index in kf.split(use_data):\n", + " test = use_data.iloc[test_index].copy()\n", + " train = use_data.iloc[train_index].copy()\n", + " train, valid = train_test_split(train, test_size=0.2, random_state=42, shuffle=True)\n", + " prediction_model = get_prediction_model()\n", + " trainable_model = get_trainable_model(prediction_model)\n", + " X = np.expand_dims(train[feature_cols].values, axis=1)\n", + " Y = [x for x in train[out_cols].values.T]\n", + " Y_valid = [x for x in valid[out_cols].values.T]\n", + " X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n", + " trainable_model.compile(optimizer='adam', loss=None)\n", + " hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, \n", + " validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]],\n", + " callbacks=[reduce_lr]\n", + " )\n", + " rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n", + " pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)\n", + " real_rst = test[out_cols].copy()\n", + " for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " pred_rst['比表面积'] = np.expm1(pred_rst['比表面积'])\n", + " real_rst['比表面积'] = np.expm1(real_rst['比表面积'])\n", + " y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,)\n", + " y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,)\n", + " y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,)\n", + " y_true_pm25 = real_rst['比表面积'].values.reshape(-1,)\n", + " y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,)\n", + " y_true_so2 = real_rst['微孔体积'].values.reshape(-1,)\n", + " bet_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", + " mesco_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", + " micro_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", + " total_bet.append(bet_eva)\n", + " total_mesco.append(mesco_eva)\n", + " total_micro.append(micro_eva)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "54c1df2c-c297-4b8d-be8a-3a99cff22545", + "metadata": {}, + "outputs": [], + "source": [ + "train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n", + "valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e7a914da-b9c2-40d9-96e0-459b0888adba", + "metadata": {}, + "outputs": [], + "source": [ + "prediction_model = get_prediction_model()\n", + "trainable_model = get_trainable_model(prediction_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4f832a1e-48e2-4467-b381-35b9d2f1271a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_20\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input (InputLayer) [(None, 1, 9)] 0 \n", + "__________________________________________________________________________________________________\n", + "conv1d_10 (Conv1D) (None, 1, 64) 640 input[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_40 (Dropout) (None, 1, 64) 0 conv1d_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "bidirectional_10 (Bidirectional (None, 1, 128) 66048 dropout_40[0][0] \n", + "__________________________________________________________________________________________________\n", + "transformer_block_10 (Transform (None, 1, 128) 201612 bidirectional_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "global_average_pooling1d_10 (Gl (None, 128) 0 transformer_block_10[0][0] \n", + "__________________________________________________________________________________________________\n", + "dropout_43 (Dropout) (None, 128) 0 global_average_pooling1d_10[0][0]\n", + "__________________________________________________________________________________________________\n", + "dense_73 (Dense) (None, 64) 8256 dropout_43[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_74 (Dense) (None, 32) 2080 dense_73[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_75 (Dense) (None, 32) 2080 dense_73[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_76 (Dense) (None, 32) 2080 dense_73[0][0] \n", + "__________________________________________________________________________________________________\n", + "bet2 (Dense) (None, 1) 33 dense_74[0][0] \n", + "__________________________________________________________________________________________________\n", + "mesco2 (Dense) (None, 1) 33 dense_75[0][0] \n", + "__________________________________________________________________________________________________\n", + "micro2 (Dense) (None, 1) 33 dense_76[0][0] \n", + "==================================================================================================\n", + "Total params: 282,895\n", + "Trainable params: 282,895\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "prediction_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.expand_dims(train[feature_cols].values, axis=1)\n", + "Y = [x for x in train[out_cols].values.T]\n", + "Y_valid = [x for x in valid[out_cols].values.T]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "32cd89b1-3379-4c40-92f9-e5426c8b229d", + "metadata": {}, + "outputs": [], + "source": [ + "X_valid = np.expand_dims(valid[feature_cols].values, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/280\n", + "14/14 [==============================] - 6s 102ms/step - loss: 1.9726 - val_loss: 1.9315\n", + "Epoch 2/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.9151 - val_loss: 1.8694\n", + "Epoch 3/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 1.8556 - val_loss: 1.8408\n", + "Epoch 4/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.8443 - val_loss: 1.8135\n", + "Epoch 5/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.7872 - val_loss: 1.7786\n", + "Epoch 6/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.7575 - val_loss: 1.7491\n", + "Epoch 7/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.7200 - val_loss: 1.7100\n", + "Epoch 8/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.7015 - val_loss: 1.6676\n", + "Epoch 9/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.6513 - val_loss: 1.6266\n", + "Epoch 10/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.6336 - val_loss: 1.5949\n", + "Epoch 11/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.5841 - val_loss: 1.5725\n", + "Epoch 12/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.5839 - val_loss: 1.5405\n", + "Epoch 13/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.5170 - val_loss: 1.5139\n", + "Epoch 14/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.5071 - val_loss: 1.4670\n", + "Epoch 15/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 1.4613 - val_loss: 1.4348\n", + "Epoch 16/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.4164 - val_loss: 1.4158\n", + "Epoch 17/280\n", + "14/14 [==============================] - 0s 16ms/step - loss: 1.4005 - val_loss: 1.3993\n", + "Epoch 18/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.3717 - val_loss: 1.3793\n", + "Epoch 19/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.3516 - val_loss: 1.3384\n", + "Epoch 20/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.3167 - val_loss: 1.2922\n", + "Epoch 21/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.2712 - val_loss: 1.2535\n", + "Epoch 22/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.2406 - val_loss: 1.2342\n", + "Epoch 23/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.2112 - val_loss: 1.2048\n", + "Epoch 24/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.1871 - val_loss: 1.1615\n", + "Epoch 25/280\n", + "14/14 [==============================] - 0s 17ms/step - loss: 1.1634 - val_loss: 1.1365\n", + "Epoch 26/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.1384 - val_loss: 1.1151\n", + "Epoch 27/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.1062 - val_loss: 1.1013\n", + "Epoch 28/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 1.0915 - val_loss: 1.0817\n", + "Epoch 29/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 1.0771 - val_loss: 1.0612\n", + "Epoch 30/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 1.0636 - val_loss: 1.0381\n", + "Epoch 31/280\n", + "14/14 [==============================] - 0s 16ms/step - loss: 1.0393 - val_loss: 1.0242\n", + "Epoch 32/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 1.0218 - val_loss: 1.0148\n", + "Epoch 33/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 1.0062 - val_loss: 0.9933\n", + "Epoch 34/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.9939 - val_loss: 0.9830\n", + "Epoch 35/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.9961 - val_loss: 0.9677\n", + "Epoch 36/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.9708 - val_loss: 0.9525\n", + "Epoch 37/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.9551 - val_loss: 0.9321\n", + "Epoch 38/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.9344 - val_loss: 0.9344\n", + "Epoch 39/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.9231 - val_loss: 0.9070\n", + "Epoch 40/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.9001 - val_loss: 0.8912\n", + "Epoch 41/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.9015 - val_loss: 0.8871\n", + "Epoch 42/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.8841 - val_loss: 0.8634\n", + "Epoch 43/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.8646 - val_loss: 0.8536\n", + "Epoch 44/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.8481 - val_loss: 0.8452\n", + "Epoch 45/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.8417 - val_loss: 0.8216\n", + "Epoch 46/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.8287 - val_loss: 0.8122\n", + "Epoch 47/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.8082 - val_loss: 0.7948\n", + "Epoch 48/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.7929 - val_loss: 0.7807\n", + "Epoch 49/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.7867 - val_loss: 0.7734\n", + "Epoch 50/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.7649 - val_loss: 0.7528\n", + "Epoch 51/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.7520 - val_loss: 0.7400\n", + "Epoch 52/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.7381 - val_loss: 0.7289\n", + "Epoch 53/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.7259 - val_loss: 0.7111\n", + "Epoch 54/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.7023 - val_loss: 0.7003\n", + "Epoch 55/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.6926 - val_loss: 0.6845\n", + "Epoch 56/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.6750 - val_loss: 0.6706\n", + "Epoch 57/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.6691 - val_loss: 0.6609\n", + "Epoch 58/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.6442 - val_loss: 0.6402\n", + "Epoch 59/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.6329 - val_loss: 0.6283\n", + "Epoch 60/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.6259 - val_loss: 0.6103\n", + "Epoch 61/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.6193 - val_loss: 0.6077\n", + "Epoch 62/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.5972 - val_loss: 0.5861\n", + "Epoch 63/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.5805 - val_loss: 0.5720\n", + "Epoch 64/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.5700 - val_loss: 0.5619\n", + "Epoch 65/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.5630 - val_loss: 0.5535\n", + "Epoch 66/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.5327 - val_loss: 0.5283\n", + "Epoch 67/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.5211 - val_loss: 0.5135\n", + "Epoch 68/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.5094 - val_loss: 0.5006\n", + "Epoch 69/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.4960 - val_loss: 0.4898\n", + "Epoch 70/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.4774 - val_loss: 0.4719\n", + "Epoch 71/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.4671 - val_loss: 0.4640\n", + "Epoch 72/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.4524 - val_loss: 0.4413\n", + "Epoch 73/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.4415 - val_loss: 0.4306\n", + "Epoch 74/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.4202 - val_loss: 0.4134\n", + "Epoch 75/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.4092 - val_loss: 0.4068\n", + "Epoch 76/280\n", + "14/14 [==============================] - 0s 17ms/step - loss: 0.4043 - val_loss: 0.3906\n", + "Epoch 77/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.3894 - val_loss: 0.3789\n", + "Epoch 78/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.3753 - val_loss: 0.3564\n", + "Epoch 79/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.3551 - val_loss: 0.3447\n", + "Epoch 80/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.3397 - val_loss: 0.3312\n", + "Epoch 81/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.3253 - val_loss: 0.3141\n", + "Epoch 82/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.3186 - val_loss: 0.3010\n", + "Epoch 83/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.2883 - val_loss: 0.2862\n", + "Epoch 84/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.2842 - val_loss: 0.2716\n", + "Epoch 85/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.2776 - val_loss: 0.2592\n", + "Epoch 86/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.2546 - val_loss: 0.2622\n", + "Epoch 87/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.2422 - val_loss: 0.2374\n", + "Epoch 88/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.2283 - val_loss: 0.2267\n", + "Epoch 89/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.2138 - val_loss: 0.2042\n", + "Epoch 90/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.1944 - val_loss: 0.1909\n", + "Epoch 91/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.1891 - val_loss: 0.1766\n", + "Epoch 92/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.1744 - val_loss: 0.1632\n", + "Epoch 93/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.1597 - val_loss: 0.1546\n", + "Epoch 94/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.1430 - val_loss: 0.1371\n", + "Epoch 95/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.1227 - val_loss: 0.1261\n", + "Epoch 96/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.1142 - val_loss: 0.1068\n", + "Epoch 97/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.1003 - val_loss: 0.1063\n", + "Epoch 98/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0965 - val_loss: 0.0777\n", + "Epoch 99/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0717 - val_loss: 0.0723\n", + "Epoch 100/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0618 - val_loss: 0.0501\n", + "Epoch 101/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0442 - val_loss: 0.0546\n", + "Epoch 102/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0280 - val_loss: 0.0273\n", + "Epoch 103/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0294\n", + "Epoch 104/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0245 - val_loss: 0.0293\n", + "Epoch 105/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0178 - val_loss: 0.0306\n", + "Epoch 106/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0260 - val_loss: 0.0284\n", + "Epoch 107/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0279 - val_loss: 0.0327\n", + "Epoch 108/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0304\n", + "Epoch 109/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0290\n", + "Epoch 110/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0223 - val_loss: 0.0399\n", + "Epoch 111/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0323\n", + "Epoch 112/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0295\n", + "Epoch 113/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0178 - val_loss: 0.0283\n", + "Epoch 114/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0283\n", + "Epoch 115/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0166 - val_loss: 0.0282\n", + "Epoch 116/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0282\n", + "Epoch 117/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0281\n", + "Epoch 118/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0280\n", + "Epoch 119/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0271\n", + "Epoch 120/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0162 - val_loss: 0.0280\n", + "Epoch 121/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0268\n", + "Epoch 122/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0171 - val_loss: 0.0270\n", + "Epoch 123/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0154 - val_loss: 0.0276\n", + "Epoch 124/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0270\n", + "Epoch 125/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0164 - val_loss: 0.0265\n", + "Epoch 126/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0276\n", + "Epoch 127/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0151 - val_loss: 0.0285\n", + "Epoch 128/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0280\n", + "Epoch 129/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0275\n", + "Epoch 130/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0267\n", + "Epoch 131/280\n", + "14/14 [==============================] - 0s 17ms/step - loss: 0.0224 - val_loss: 0.0264\n", + "Epoch 132/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0270\n", + "Epoch 133/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0146 - val_loss: 0.0270\n", + "Epoch 134/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0172 - val_loss: 0.0274\n", + "Epoch 135/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0279\n", + "Epoch 136/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0275\n", + "Epoch 137/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0135 - val_loss: 0.0274\n", + "Epoch 138/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0153 - val_loss: 0.0272\n", + "Epoch 139/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0141 - val_loss: 0.0271\n", + "Epoch 140/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0165 - val_loss: 0.0269\n", + "Epoch 141/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0269\n", + "Epoch 142/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0269\n", + "Epoch 143/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0233 - val_loss: 0.0269\n", + "Epoch 144/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0269\n", + "Epoch 145/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0148 - val_loss: 0.0268\n", + "Epoch 146/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0268\n", + "Epoch 147/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0268\n", + "Epoch 148/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 149/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0144 - val_loss: 0.0268\n", + "Epoch 150/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0268\n", + "Epoch 151/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0268\n", + "Epoch 152/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0146 - val_loss: 0.0268\n", + "Epoch 153/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0268\n", + "Epoch 154/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0144 - val_loss: 0.0268\n", + "Epoch 155/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0268\n", + "Epoch 156/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0173 - val_loss: 0.0268\n", + "Epoch 157/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0202 - val_loss: 0.0268\n", + "Epoch 158/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0268\n", + "Epoch 159/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0268\n", + "Epoch 160/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0165 - val_loss: 0.0268\n", + "Epoch 161/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0268\n", + "Epoch 162/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0185 - val_loss: 0.0268\n", + "Epoch 163/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0268\n", + "Epoch 164/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0282 - val_loss: 0.0268\n", + "Epoch 165/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0268\n", + "Epoch 166/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 167/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0268\n", + "Epoch 168/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0268\n", + "Epoch 169/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268\n", + "Epoch 170/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0148 - val_loss: 0.0268\n", + "Epoch 171/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 172/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0197 - val_loss: 0.0268\n", + "Epoch 173/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0194 - val_loss: 0.0268\n", + "Epoch 174/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0157 - val_loss: 0.0268\n", + "Epoch 175/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0148 - val_loss: 0.0268\n", + "Epoch 176/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0173 - val_loss: 0.0268\n", + "Epoch 177/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 178/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 179/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0268\n", + "Epoch 180/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0136 - val_loss: 0.0268\n", + "Epoch 181/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0268\n", + "Epoch 182/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0268\n", + "Epoch 183/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0268\n", + "Epoch 184/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0268\n", + "Epoch 185/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 186/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0268\n", + "Epoch 187/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0121 - val_loss: 0.0268\n", + "Epoch 188/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0268\n", + "Epoch 189/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0268\n", + "Epoch 190/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 191/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0194 - val_loss: 0.0268\n", + "Epoch 192/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0157 - val_loss: 0.0268\n", + "Epoch 193/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268\n", + "Epoch 194/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 195/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0306 - val_loss: 0.0268\n", + "Epoch 196/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 197/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0268\n", + "Epoch 198/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0268\n", + "Epoch 199/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268\n", + "Epoch 200/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0268\n", + "Epoch 201/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0268\n", + "Epoch 202/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0145 - val_loss: 0.0268\n", + "Epoch 203/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0268\n", + "Epoch 204/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 205/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0156 - val_loss: 0.0268\n", + "Epoch 206/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268\n", + "Epoch 207/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 208/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0249 - val_loss: 0.0268\n", + "Epoch 209/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0268\n", + "Epoch 210/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0163 - val_loss: 0.0268\n", + "Epoch 211/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0268\n", + "Epoch 212/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0131 - val_loss: 0.0268\n", + "Epoch 213/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0161 - val_loss: 0.0268\n", + "Epoch 214/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0268\n", + "Epoch 215/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0268\n", + "Epoch 216/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0268\n", + "Epoch 217/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0268\n", + "Epoch 218/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0268\n", + "Epoch 219/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0224 - val_loss: 0.0268\n", + "Epoch 220/280\n", + "14/14 [==============================] - 0s 12ms/step - loss: 0.0218 - val_loss: 0.0268\n", + "Epoch 221/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0268\n", + "Epoch 222/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0184 - val_loss: 0.0268\n", + "Epoch 223/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0175 - val_loss: 0.0268\n", + "Epoch 224/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0168 - val_loss: 0.0268\n", + "Epoch 225/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 226/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0268\n", + "Epoch 227/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0268\n", + "Epoch 228/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0146 - val_loss: 0.0268\n", + "Epoch 229/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0146 - val_loss: 0.0268\n", + "Epoch 230/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0268\n", + "Epoch 231/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0211 - val_loss: 0.0268\n", + "Epoch 232/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0268\n", + "Epoch 233/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0268\n", + "Epoch 234/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0138 - val_loss: 0.0268\n", + "Epoch 235/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0268\n", + "Epoch 236/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0180 - val_loss: 0.0268\n", + "Epoch 237/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0268\n", + "Epoch 238/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0137 - val_loss: 0.0268\n", + "Epoch 239/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0193 - val_loss: 0.0268\n", + "Epoch 240/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0268\n", + "Epoch 241/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0130 - val_loss: 0.0268\n", + "Epoch 242/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0268\n", + "Epoch 243/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0268\n", + "Epoch 244/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0126 - val_loss: 0.0268\n", + "Epoch 245/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0190 - val_loss: 0.0268\n", + "Epoch 246/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0268\n", + "Epoch 247/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0268\n", + "Epoch 248/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 249/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0170 - val_loss: 0.0268\n", + "Epoch 250/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0268\n", + "Epoch 251/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0163 - val_loss: 0.0268\n", + "Epoch 252/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0131 - val_loss: 0.0268\n", + "Epoch 253/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0268\n", + "Epoch 254/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0268\n", + "Epoch 255/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0172 - val_loss: 0.0268\n", + "Epoch 256/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0143 - val_loss: 0.0268\n", + "Epoch 257/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0268\n", + "Epoch 258/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0268\n", + "Epoch 259/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0121 - val_loss: 0.0268\n", + "Epoch 260/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0262 - val_loss: 0.0268\n", + "Epoch 261/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0268\n", + "Epoch 262/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268\n", + "Epoch 263/280\n", + "14/14 [==============================] - 0s 11ms/step - loss: 0.0155 - val_loss: 0.0268\n", + "Epoch 264/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268\n", + "Epoch 265/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0268\n", + "Epoch 266/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0194 - val_loss: 0.0268\n", + "Epoch 267/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0268\n", + "Epoch 268/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0268\n", + "Epoch 269/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0268\n", + "Epoch 270/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0268\n", + "Epoch 271/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0268\n", + "Epoch 272/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0268\n", + "Epoch 273/280\n", + "14/14 [==============================] - 0s 15ms/step - loss: 0.0156 - val_loss: 0.0268\n", + "Epoch 274/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0268\n", + "Epoch 275/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0268\n", + "Epoch 276/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268\n", + "Epoch 277/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0268\n", + "Epoch 278/280\n", + "14/14 [==============================] - 0s 14ms/step - loss: 0.0174 - val_loss: 0.0268\n", + "Epoch 279/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0268\n", + "Epoch 280/280\n", + "14/14 [==============================] - 0s 13ms/step - loss: 0.0147 - val_loss: 0.0268\n" + ] + } + ], + "source": [ + "trainable_model.compile(optimizer='adam', loss=None)\n", + "hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, \n", + " validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]],\n", + " callbacks=[reduce_lr]\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04", + "metadata": {}, + "outputs": [], + "source": [ + "rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7de501e9-05a2-424c-a5f4-85d43ad37592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.9991102134329165, 0.9990587871483716, 0.9990097447684705]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "b0d5d8ad-aadd-4218-b5b7-9691a2d3eeef", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "0a2bcb45-da86-471b-a61d-314e29430d6a", + "metadata": {}, + "outputs": [], + "source": [ + "real_rst = test[out_cols].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "e124f7c0-fdd5-43b9-b649-ff7d9dd59641", + "metadata": {}, + "outputs": [], + "source": [ + "for col in out_cols:\n", + " pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n", + " real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "82cca0af-4aef-47d5-830a-04a51837c005", + "metadata": {}, + "outputs": [], + "source": [ + "pred_rst['比表面积'] = np.expm1(pred_rst['比表面积'])\n", + "real_rst['比表面积'] = np.expm1(real_rst['比表面积'])" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['比表面积', '总孔体积', '微孔体积'], dtype='object')" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real_rst.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "21739f82-d82a-4bde-8537-9504b68a96d5", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,)\n", + "y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,)\n", + "y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,)\n", + "y_true_pm25 = real_rst['比表面积'].values.reshape(-1,)\n", + "y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,)\n", + "y_true_so2 = real_rst['微孔体积'].values.reshape(-1,)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304", "metadata": {}, "outputs": [ @@ -1171,18 +7387,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "COL: 比表面积, MSE: 2.36E+05,RMSE: 485.5891,MAPE: 25.86 %,MAE: 340.8309,R_2: -0.1091\n", - "COL: 总孔体积, MSE: 5.15E-02,RMSE: 0.2268,MAPE: 23.810000000000002 %,MAE: 0.1519,R_2: 0.7657\n", - "COL: 微孔体积, MSE: 4.53E-02,RMSE: 0.2128,MAPE: 34.75 %,MAE: 0.1536,R_2: -0.0412\n", - "COL: 平均孔径, MSE: 4.63E-01,RMSE: 0.6802,MAPE: 15.620000000000001 %,MAE: 0.415,R_2: 0.5929\n" + "COL: 比表面积, MSE: 1.41E-01,RMSE: 0.3751,MAPE: 4.15 %,MAE: 0.2805,R_2: 0.5069\n", + "COL: 总孔体积, MSE: 3.31E-02,RMSE: 0.1819,MAPE: 21.86 %,MAE: 0.1473,R_2: 0.7538\n", + "COL: 微孔体积, MSE: 2.67E-02,RMSE: 0.1635,MAPE: 25.290000000000003 %,MAE: 0.1261,R_2: 0.6851\n" ] } ], "source": [ "pm25_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积')\n", "pm10_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积')\n", - "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')\n", - "nox_eva = print_eva(y_true_no2, y_pred_no2, tp='平均孔径')" + "so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')" ] }, { @@ -1204,9 +7418,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "python37", "language": "python", - "name": "python3" + "name": "python37" }, "language_info": { "codemirror_mode": { @@ -1218,7 +7432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.7.16" } }, "nbformat": 4, diff --git a/multi-task0102.ipynb b/multi-task0102.ipynb index 3f3c53f..38a78be 100644 --- a/multi-task0102.ipynb +++ b/multi-task0102.ipynb @@ -549,7 +549,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 16:46:07.061819: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" + "2024-01-08 18:28:46.594783: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n" ] } ], @@ -660,12 +660,20 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "id": "80f32155-e71f-4615-8d0c-01dfd04988fe", "metadata": {}, "outputs": [], "source": [ "def get_prediction_model():\n", + " def build_output(out, out_name):\n", + " self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn')\n", + " out = self_block(out)\n", + " out = layers.GlobalAveragePooling1D()(out)\n", + " out = layers.Dropout(0.1)(out)\n", + " out = layers.Dense(32, activation=\"relu\")(out)\n", + " # out = layers.Dense(1, name=out_name, activation=\"sigmoid\")(out)\n", + " return out\n", " inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n", " x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n", " # x = layers.Dropout(rate=0.1)(x)\n", @@ -676,10 +684,12 @@ " out = layers.GlobalAveragePooling1D()(out)\n", " out = layers.Dropout(0.1)(out)\n", " out = layers.Dense(64, activation='relu')(out)\n", - " # out = K.expand_dims(out, axis=1)\n", + " out = K.expand_dims(out, axis=1)\n", "\n", - " bet = layers.Dense(32, activation=\"relu\")(out)\n", - " mesco = layers.Dense(32, activation=\"relu\")(out)\n", + " # bet = layers.Dense(32, activation=\"relu\")(out)\n", + " # mesco = layers.Dense(32, activation=\"relu\")(out)\n", + " bet = build_output(out, 'bet')\n", + " mesco = build_output(out, 'mesco')\n", "\n", " bet = layers.Dense(1, activation='sigmoid', name='vad')(bet)\n", " mesco = layers.Dense(1, activation='sigmoid', name='fcad')(mesco)\n", @@ -940,7 +950,7 @@ "outputs": [], "source": [ "# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n", - "feature_cols = [x for x in train_data.columns if x not in out_cols]\n", + "feature_cols = [x for x in train_data.columns if x not in out_cols and '编号' not in x]\n", "use_cols = feature_cols + out_cols" ] }, @@ -1013,10 +1023,26 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "10213bc5-bf13-46ed-9ce9-b1dbc5af72ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-08 18:28:48.712096: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n", + "2024-01-08 18:28:48.770197: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n", + "2024-01-08 18:28:48.770270: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n", + "2024-01-08 18:28:48.770284: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n", + "2024-01-08 18:28:48.770578: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n", + "2024-01-08 18:28:48.770639: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n", + "2024-01-08 18:28:48.770650: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n", + "2024-01-08 18:28:48.771267: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "prediction_model = get_prediction_model()\n", "trainable_model = get_trainable_model(prediction_model)\n", @@ -1025,18 +1051,18 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "4a1be90d-b8f1-4fe1-9952-1cdcc489fab5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1047,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0", "metadata": {}, "outputs": [ @@ -1055,28 +1081,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-05 16:53:33.639598: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", - "2024-01-05 16:53:33.658810: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" + "2024-01-08 18:28:51.289876: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n", + "2024-01-08 18:28:51.306804: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "COL: 挥发分Vad, MSE: 1.73E+00,RMSE: 1.316,MAPE: 3.178 %,MAE: 0.961,R_2: 0.626\n", - "COL: 固定炭Fcad, MSE: 2.28E+02,RMSE: 15.111,MAPE: 28.087 %,MAE: 15.088,R_2: -8.016\n", - "COL: 挥发分Vad, MSE: 8.81E-01,RMSE: 0.939,MAPE: 2.56 %,MAE: 0.745,R_2: 0.893\n", - "COL: 固定炭Fcad, MSE: 1.18E+00,RMSE: 1.085,MAPE: 1.679 %,MAE: 0.836,R_2: 0.977\n", - "COL: 挥发分Vad, MSE: 4.92E-01,RMSE: 0.701,MAPE: 1.855 %,MAE: 0.548,R_2: 0.874\n", - "COL: 固定炭Fcad, MSE: 9.80E-01,RMSE: 0.99,MAPE: 1.413 %,MAE: 0.774,R_2: 0.93\n", - "COL: 挥发分Vad, MSE: 4.56E+01,RMSE: 6.756,MAPE: 22.559 %,MAE: 6.7,R_2: -6.995\n", - "COL: 固定炭Fcad, MSE: 1.12E+02,RMSE: 10.605,MAPE: 20.153 %,MAE: 10.579,R_2: -2.311\n", - "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7f2c5ec9aa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 8.31E-01,RMSE: 0.912,MAPE: 2.389 %,MAE: 0.728,R_2: 0.811\n", - "COL: 固定炭Fcad, MSE: 1.75E+02,RMSE: 13.221,MAPE: 24.517 %,MAE: 13.188,R_2: -7.209\n", - "WARNING:tensorflow:6 out of the last 11 calls to .predict_function at 0x7f2c5e414940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", - "COL: 挥发分Vad, MSE: 5.16E-01,RMSE: 0.719,MAPE: 2.022 %,MAE: 0.591,R_2: 0.894\n", - "COL: 固定炭Fcad, MSE: 1.33E+00,RMSE: 1.154,MAPE: 1.496 %,MAE: 0.739,R_2: 0.944\n" + "COL: 挥发分Vad, MSE: 5.90E-01,RMSE: 0.768,MAPE: 2.097 %,MAE: 0.628,R_2: 0.872\n", + "COL: 固定炭Fcad, MSE: 8.06E-01,RMSE: 0.898,MAPE: 1.432 %,MAE: 0.753,R_2: 0.968\n", + "COL: 挥发分Vad, MSE: 3.46E+01,RMSE: 5.885,MAPE: 18.37 %,MAE: 5.539,R_2: -3.196\n", + "COL: 固定炭Fcad, MSE: 3.66E+00,RMSE: 1.912,MAPE: 3.032 %,MAE: 1.563,R_2: 0.929\n", + "COL: 挥发分Vad, MSE: 1.77E+01,RMSE: 4.212,MAPE: 12.828 %,MAE: 3.718,R_2: -3.53\n", + "COL: 固定炭Fcad, MSE: 1.25E+00,RMSE: 1.119,MAPE: 1.541 %,MAE: 0.855,R_2: 0.91\n", + "COL: 挥发分Vad, MSE: 6.66E-01,RMSE: 0.816,MAPE: 2.165 %,MAE: 0.633,R_2: 0.883\n", + "COL: 固定炭Fcad, MSE: 4.77E-01,RMSE: 0.69,MAPE: 1.072 %,MAE: 0.551,R_2: 0.986\n", + "WARNING:tensorflow:5 out of the last 9 calls to .predict_function at 0x7fba6eb73040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "COL: 挥发分Vad, MSE: 8.97E-01,RMSE: 0.947,MAPE: 2.346 %,MAE: 0.722,R_2: 0.796\n", + "COL: 固定炭Fcad, MSE: 1.29E+00,RMSE: 1.137,MAPE: 1.633 %,MAE: 0.886,R_2: 0.939\n" ] } ], @@ -1118,26 +1141,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MSE 0.890323\n", - "RMSE 0.917209\n", - "MAE 0.714643\n", - "MAPE 0.024009\n", - "R_2 0.819531\n", - "dtype: float64" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n", "vad_df.sort_values(by='R_2')[1:].mean()" @@ -1145,22 +1152,22 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MSE 58.149181\n", - "RMSE 5.410969\n", - "MAE 5.223220\n", - "MAPE 0.098516\n", - "R_2 -1.333914\n", + "MSE 0.820345\n", + "RMSE 0.899216\n", + "MAE 0.723321\n", + "MAPE 0.013728\n", + "R_2 0.967491\n", "dtype: float64" ] }, - "execution_count": 34, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1401,7 +1408,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/multioutput_regression.ipynb b/multioutput_regression.ipynb index 1114588..eca96dc 100644 --- a/multioutput_regression.ipynb +++ b/multioutput_regression.ipynb @@ -185,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.18" } }, "nbformat": 4, diff --git 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12萃取中级烟煤NaNNaNNaN75.114.731.380.0018.781100.02.02.000316.00.481NaNNaN
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45褐煤14.914.3548.4267.764.571.293.5622.82650.010.00.5102609.01.4380.6700.768
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" - ], - "text/plain": [ - " O N SAmicro(m2/g) SAmeso(m2/g)\n", - "0 0.00 0.00 0 0\n", - "3 17.00 15.60 0 0\n", - "6 8.50 7.80 0 0\n", - "9 0.00 0.00 120 216\n", - "13 0.00 0.00 107 315\n", - ".. ... ... ... ...\n", - "159 6.25 9.57 640 184\n", - "160 8.49 5.38 563 120\n", - "161 7.84 7.02 680 641\n", - "164 0.00 0.00 0 1082\n", - "165 14.97 0.00 1590 1030\n", - "\n", - "[63 rows x 4 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nature_data[nature_data.electrolyte=='6MKOH'][['O', 'N', 'SAmicro(m2/g)', 'SAmeso(m2/g)']].drop_duplicates()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "101dba3e-4029-4d53-b64a-89c5a90f3471", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/未命名.ipynb b/未命名.ipynb new file mode 100644 index 0000000..5c817e2 --- /dev/null +++ b/未命名.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "798076ab-a8f3-47d4-a221-00b465568f41", + "metadata": {}, + "outputs": [], + "source": [ + "from statistics import mean\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import explained_variance_score,r2_score,median_absolute_error,mean_squared_error,mean_absolute_error\n", + "from scipy import stats\n", + "import numpy as np\n", + "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n", + "plt.rcParams[\"font.size\"] = 16\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "697bdeac-4ca7-4cf9-ad01-2ee00b4c7c72", + "metadata": {}, + "outputs": [], + "source": [ + "def scatter_out_1(x,y): ## x,y为两个需要做对比分析的两个量。\n", + " # ==========计算评价指标==========\n", + " BIAS = mean(x - y)\n", + " MSE = mean_squared_error(x, y)\n", + " RMSE = np.power(MSE, 0.5)\n", + " R2 = r2_score(x, y)\n", + " MAE = mean_absolute_error(x, y)\n", + " EV = explained_variance_score(x, y)\n", + " print('==========算法评价指标==========')\n", + " print('BIAS:', '%.3f' % (BIAS))\n", + " print('Explained Variance(EV):', '%.3f' % (EV))\n", + " print('Mean Absolute Error(MAE):', '%.3f' % (MAE))\n", + " print('Mean squared error(MSE):', '%.3f' % (MSE))\n", + " print('Root Mean Squard Error(RMSE):', '%.3f' % (RMSE))\n", + " print('R_squared:', '%.3f' % (R2))\n", + " # ===========Calculate the point density==========\n", + " xy = np.vstack([x, y])\n", + " z = stats.gaussian_kde(xy)(xy)\n", + " # ===========Sort the points by density, so that the densest points are plotted last===========\n", + " idx = z.argsort()\n", + " x, y, z = x[idx], y[idx], z[idx]\n", + " def best_fit_slope_and_intercept(xs, ys):\n", + " m = (((mean(xs) * mean(ys)) - mean(xs * ys)) / ((mean(xs) * mean(xs)) - mean(xs * xs)))\n", + " b = mean(ys) - m * mean(xs)\n", + " return m, b\n", + " m, b = best_fit_slope_and_intercept(x, y)\n", + " regression_line = []\n", + " for a in x:\n", + " regression_line.append((m * a) + b)\n", + " fig,ax=plt.subplots(figsize=(12,9),dpi=600)\n", + " scatter=ax.scatter(x,y,marker='o',c=z, edgecolors='b',s=15,label='LST',cmap='Spectral_r')\n", + " cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency', )\n", + " plt.plot([0,35],[0,35],'black',lw=1.5) # 画的1:1线，线的颜色为black，线宽为0.8\n", + " plt.plot(x,regression_line,'red',lw=1.5) # 预测与实测数据之间的回归线\n", + " plt.axis([0,35,0,35]) # 设置线的范围\n", + " plt.title(\"总孔体积拟合结果 $10^2 cm^3$\", fontdict={\"fontsize\":16})\n", + " plt.xlabel('预测值', fontdict={\"fontsize\":16})\n", + " plt.ylabel('真实值', fontdict={\"fontsize\":16})\n", + " plt.text(0.5,34, '$N=%.f$' % len(y), fontdict={\"fontsize\":16}) # text的位置需要根据x,y的大小范围进行调整。\n", + " plt.text(0.5,33, '$R^2=%.3f$' % R2, fontdict={\"fontsize\":16})\n", + " plt.text(0.5,32, '$BIAS=%.4f$' % BIAS, fontdict={\"fontsize\":16})\n", + " plt.text(0.5,31, '$RMSE=%.3f$' % RMSE, fontdict={\"fontsize\":16})\n", + " plt.xlim(0,35) # 设置x坐标轴的显示范围\n", + " plt.ylim(0,35) # 设置y坐标轴的显示范围\n", + " plt.savefig('./总孔体积.png',dpi=300, bbox_inches='tight',pad_inches=0)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9e1279b5-4b18-4f57-bdc0-197ba84cda33", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2d5bcdf5-cc45-40b7-8000-a0e41b18ef93", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./rst/总孔体积_比表.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8908a8b3-6bd5-4d71-b012-cbdd690b6a31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count184.000000184.000000
mean267.871020272.776239
std696.475264693.395059
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" + ], + "text/plain": [ + " 真实值 预测值\n", + "count 184.000000 184.000000\n", + "mean 267.871020 272.776239\n", + "std 696.475264 693.395059\n", + "min 0.060000 0.069085\n", + "25% 0.539250 0.570501\n", + "50% 0.877000 0.889113\n", + "75% 1.673250 1.551479\n", + "max 3322.000000 3225.575700" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d6bfbaae-a201-4963-be4e-4731a3fe8a37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==========算法评价指标==========\n", + "BIAS: 0.049\n", + "Explained Variance(EV): 0.921\n", + "Mean Absolute Error(MAE): 0.622\n", + "Mean squared error(MSE): 3.797\n", + "Root Mean Squard Error(RMSE): 1.949\n", + "R_squared: 0.921\n" + ] + }, + { + "data": { + "image/png": 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6178u+L07ef2LDH/7298W9CV7nfWpT30q2267bYdzt9xyS5566qluvx8AoFjeMd/KZ5kAAFBZiq4BVlOPPvpobrjhhk49c8YZZ2SXXXbp9iw77rhjjjvuuA7n5s2bl8suu6zb7wcAoHvNmjUrM2bM6HBum222ycCBA0sfCACAklldSgh/9KMfZeXKlQXPr7XWWrn00kvTr1+/bs/y9a9/PYMGDepw7rLLLsv8+fO7/X4AgFJZsmRJ6uvr87nPfS6rVq0q6JmamppcdNFF2XjjjUucrnTuu+++guZGjhxZ4iQAAL2PAsK3UgwDAFC8pqam/OIXvyhodsSIETnvvPNKnOhfNtlkkxx55JEFz7e0tGTy5Mnddr/PMgEAitfS0pKzzz67oNljjjkmI0aMKHGifxk3blzBs0888URRd61cubJTX2KYJJ/5zGey1157FXVvW2pqanL66acXNFvO/w4AAFAI75hv57NMAACoLEXXAKupQj+kesNOO+2Ur3zlKyVKk5x22mkFlb74DUAAgJ6v0N8AfN/73lfiJAAAlMrqVEI4Z86cXHDBBZ165tvf/na22mqrkuQZMGBATj311A7nli1blosuuqgkGQAAutuDDz6YXXbZpVNffFxXV5crr7wyxx9/fAmTlZ7PUwEAukYB4VsphgEA6B7XXnttGhoaOpyrrq7O//t//y91dXVlSPUvBx10UKfmH3vssW6722eZAADFu/POOwv64re6urp897vfLUOifxk+fHjBszNmzCjqrquuuiozZ84seH6dddbJt7/97aLu7MhRRx2V7bffvsO5yy+/PMuXLy9pFgCAzvCO+XY+ywQAgMpSdA2wGmpoaMjVV1/dqWe+973vpaampkSJkmHDhhX0F5CffPLJov9CBwAApXXvvfcWNOebbgEAeqfVrYTw17/+dZYuXVrw/KabbpovfOELJUyUfP7zn8/gwYM7nLvwwgtLmgMAoFgtLS354Q9/mD322CPPPfdcwc+tvfba+ctf/pJDDz20hOnKo5C/UFFdXZ0RI0aUIQ0AQO+hgPCtFMMAAHSPX/7ylwXNffKTn8yuu+5a4jRvt88++3Rqfs6cOd12tz8bCgBQvIsvvrigufr6+myyySYlTvNWW2yxRcGzxb5n/va3v+3U/IknnpiNNtqoqDs7Ul1dnS9/+csdzi1cuDA33HBDSbMAAHSGd8y381kmAABUlqJrgNXQpZdemubm5oLnR44cmY985CMlTPS6r371q6mqqupw7oorrih5FgAAuq7Q3wDcY489SpwEAIDutLqWEF5yySWdmv/GN76RNddcs0RpXrfuuuvmxBNP7HDuueeey8MPP1zSLAAAxViwYEG++tWvZtWqVQU/s/HGG+eOO+7IBz7wgRImK49p06Zl9uzZHc7tuOOOWXfddcuQCACg91BA+FaKYQAAijdt2rTceeedHc6ts846+e53v1uGRG+30UYbZeONNy54vrvKYVasWJFHH320w7mBAwfm3e9+d7fcCQDQ1yxatCh/+tOfOpwr9HO17rbeeusVPNuZ3+P/d7Nnz87tt99e8HxtbW0+97nPdfm+zjj88MOz9tprdzj3xz/+sQxpAAA65h3z7XyWCQAAlafoGmA19Pvf/75T8yeddFJpgvybLbbYInvvvXeHc1dddVVaWlrKkAgAgM5qamrKgw8+2OHcOuusk5133rkMiQAA6C6rYwnhAw88kOeff77g+UGDBqW+vr6Eif7l2GOPLeiLA/2FCgCgL9ljjz3y4IMPZrfddqt0lG7hSwMBALpGAeFbKYYBAOgel156aUF/V2XChAnZYIMNypCodQMHDix49v+zd99RVpXX/4D3FGZgho4IiAiKBRsoiigIKrYoaqyJJfaW2EvsxpZY8o0tlsTeE0tsUVEkEAsiIBZUQLAgWChiAQGpM/P7Iz+JRmDOHe65d8rzrDVLndnveT9jop6133v2+fLLL7Oy55tvvhmLFy+utm7rrbdOdI4NANAQPfzww/Hdd99VW7frrrvG+uuvn4NEP5bJfVxhYc3HlAwaNCgqKysT1++9996x5ppr1ni/TDRt2jT222+/auuGDBkSFRUVOUgEALBy7jF/Si8TAADyz6BrgAZm8uTJMWHChMT17du3j/333z/FRD92xBFHVFszY8aMePPNN9MPAwBAxt59991Eh6K9e/eOoqKiHCQCACBf6sMQwqeffjqj+qOOOirKyspSSvNjSV8c+Oyzz+YgDQBA+o455ph44YUXon379vmOkjWjR49OVNe3b9+UkwAA1C0GEP6YwTAAANmx3nrrxVFHHRVbb711NG/efIV1uXppyIo0a9YscW22hsPoZQIArLpNN900Lr/88jj44INjs802iyZNmiy37qijjspxsv+YP39+4toWLVrUeJ/nnnsuo/pf/epXNd6rJg4//PBqa2bPnp34HhkAIE3uMX9KLxMAAPKvON8BAMitTA8ADzzwwCguzt1/Lvbff/846aSTqh2O+Pzzz8cWW2yRo1QAACQ1atSoRHX9+/dPOQkAAPl0zDHHxM033xwlJSX5jrJK6sIDFS+//PJKa8aNGxfTpk2LNdZYI0epAACyq3HjxnHttdfGb37zm3xHyTr9VACAmvl+AOGECRNiwoQJ8e233y63rqEMIKwLfcz77rtvpTXfD4bp06dPjlIBAPzUwQcfHAcffPCyv/70009jwoQJMX78+Bg/fnxMmDAh2rVrFxtttFEeU0YsWbIkcW22XtSslwkAsOp69+4dvXv3XvbXlZWV8fHHHy/rc06YMCE+/fTT2GuvvfKSb/r06Ylr27ZtW+N9qvvM4w+1bNkydttttxrvVRPbbbddNG/efIV95+8NGzZMPxMAyDv3mD+llwkAAPln0DVAA/PSSy9lVH/QQQellGT5mjVrFjvssEMMGjRopXXDhg2L888/P0epAABIKukB4HbbbZdyEgAA8qE+DSGcO3duvPXWW4nrN9xww+jRo0eKiX5qzz33jIKCgqiqqlpp3bBhw+LQQw/NUSoAgOzZZJNN4sEHH4xNNtkk31GybtGiRYnuN7t06RJrrbVWDhIBANQdBhD+mMEwAADp6NSpU3Tq1Cl23XXXfEf5kU8//TRxbXl5eVb2TPLZ0MaNG/9oqA4AACtXWFgYXbt2ja5du8aee+6Z7zjx0UcfJa7t2rVrjfb44IMP4osvvkhcv9tuu0VJSUmN9qqpoqKi6NevX7XPuY8ePTpHiQAAkmuI95j/Sy8TAADyrzDfAQDIrREjRiSuXWONNWKrrbZKMc3ybb/99tXWvP7661FZWZl+GAAAMuIAEACg4dpkk01izJgx9WLIdcR/7m0rKioS1++9997phVmBtm3bJhrW44EKAKAuOvnkk2PMmDH1csh1RMRbb70VixcvrrYuyfk5AEBD9/3wwTPOOCPuvPPOGDlyZDz55JP5jpWTAYR1aTBMdfQxAQCqt2jRopg1a1bi+mbNmq3ynjNmzIipU6dWW7f11ltHaWnpKu8HAEB+ZNKf22CDDWq0x5gxYzKqz9dLZ3bYYYdqazL9XQAAGqJc3GP+kF4mAADUDgZdAzQg06ZNi2nTpiWu32WXXVJMs2JJDgC//fbbmDhxYg7SAACQ1OzZs+P999+vtq5Pnz7RuHHjHCQCACBX6uMQwvr0QMVrr72WgyQAANnRvn37GDRoUNxwww31uo+Y9AGOAQMGpJwEAIA05GoAYX3qYxoMAwBQvXfffTeqqqoS13fp0mWV99TLBABoGF544YVEdYWFhbHFFlvUaI933303o/p8Peee5IXUX3zxRUbP7AMANES5uMf8Ib1MAACoHQy6BmhAMj0AzNcDDZtvvnm0aNGi2rqxY8emHwYAgMRGjRqV6AGKHXfcMQdpAADIhfo8hDCTfmrTpk2jT58+KaZZsSQPVLzzzjtRWVmZfhgAgFVQUFAQxxxzTLz33nux++675ztO6kaOHJmozgMVAAB1U64GEBoMAwDQsPz73//OqH6DDTZY5T2T9jJ9NhQAoO6aMGFCTJgwIVHtpptuWuMX940bNy5xbdeuXaNDhw412mdVbbLJJlFYWP0olkmTJuUgDQBA3ZSre8wf0ssEAIDawaBrgAYkkwPAiIhtt902pSQrV1hYGBtvvHG1dQ4AAQBql1GjRiWqy9eDswAAZE9DGEKYST+1d+/e0ahRoxTTrFj37t2rrVm0aFFMnTo1B2kAAGpm/fXXjxdeeCFuv/32aNmyZb7j5ESSfurGG28cHTt2zEEaAACyLVcDCA2GAQBoWF544YWM6rfaaqtV3jNJL7NFixZZ2QsAgPy46aabEtfuueeeNd7nww8/TFy79dZb13ifVVVaWhqdOnWqtk4/EwBgxXJ1j/lDepkAAFA7FOc7AAC5k8kBYMeOHWPNNddMMc3KrbfeevHqq6+utMYBIABA7ZLkALBt27axxRZb/OT7VVVV8e6778bbb78d48ePjwkTJsSUKVNizpw5MWfOnJg/f340adIkmjVrFi1atIjOnTvHhhtuGBtttFH06tUrevTokcavBADAcqy//vpx2223xXbbbZfvKKn66KOPEtfm84GKtddeO4qLi2Pp0qUrrZs0aVKsvfbaOUoFAJBMUVFRXHDBBfG73/0uSktL8x0nZ2bMmJHoRSQ/+9nPlvv9+fPnx+jRo2P8+PExfvz4eO+992LmzJkxZ86c+Pbbb2PJkiXRrFmzaN68ebRq1SrWX3/92GijjWLjjTeOfv36xeqrr57tXwkAgP+RqwGEdW0wTHX3wZMmTYoddtghR6kAAOqW+fPnx/DhwxPXb7DBBtG2bdtV2rOioiJef/31aut22mmnKC7+6aOqixYtijFjxsS4ceNiwoQJMX78+Jg+ffqyXubChQujadOm0bx582jZsmWsu+66sdFGG8VGG20U2267baLhggAArJrPPvss7rnnnsT1+++/f432qaqqiilTpiSuz2c/M+I/z7kn6WcCAPBTubrH/CG9TAAAqD0MugZoQCZPnpy4tjYcAFbHASAAQO1RVVUVo0ePrrZut912i4KCgoiImD59egwaNCiGDh0a//73v2PWrFkrXTt37tyYO3duTJs2Ld57770YPHjwsp916NAhdtlll/j5z38ee+yxRzRq1GjVfiEAAH6iIQ0hnD59eixYsCBxfT77qcXFxdGlS5dqB9pMmjRphYMSAQDypVmzZvGHP/wh3zFybuTIkYnqBg4cuOzPX3vttRg8eHAMHTo0Ro0aFUuWLFnp2q+//jq+/vrrmDJlSrz11lvLvl9QUBA9e/aMXXfdNQ488MDYdNNNa/ZLAACwQrkaQGgwDABAw/Loo4/G/PnzE9fvtttuq7znO++8k2jPH/Yyx48fH88++2wMHTo0hg8fXu3Z++zZs2P27NnxySefxDvvvBOPP/74sp9ttNFGseuuu8YBBxwQ22yzTc1/EQAAVuj0009P/HnJfv36RY8ePWq0z8yZM2PhwoWJ67t3716jfbJlvfXWi6FDh660Rj8TAGD5cnWP+UN6mQAAUHsYdA3QgFT3gMAP1YYDwOq8//77UVVVtWxQIgAA+TNx4sSYPXt2tXU77LBD3H///fHAAw/EsGHDoqKiIiv7T58+Pe6999649957o127dnHkkUfG8ccfH126dMnK9QEAaFhDCDPppUbUjn5qkkHXAADUDqNGjaq2pmXLltG+ffu45JJL4m9/+1u193tJVVVVxRtvvBFvvPFGXHHFFbH11lvHcccdFwcddFA0btw4K3sAADR0uRpAaDAMAEDDcs8992RUv/fee6/ynkl6mYWFhbHZZpvF1VdfHQ888EC8/fbbq7zv9yZMmBATJkyI6667LjbZZJM47rjj4rDDDosWLVpkbQ8AgIbsrrvuikcffTRx/dlnn13jvWbMmJFR/SabbFLjvbJhjTXWqLZGPxMA4KdyeY/5Q3qZAABQexTmOwAAuZPJIWBdOAD87rvv4rPPPstBGgAAqpPkADAi4thjj43DDjsshgwZkrUh1/9r5syZcdVVV8X6668fJ554YsycOTOVfQAAqL8y6aU2b9481lprrRTTVM8DFQAAdUuSfuq8efNio402iksvvTRrQ65XlOWoo46K9ddfP+6+++7U+rYAAA1JrgYQGgwDANBwTJkyJV566aXE9WuuuWb069dvlfdN+tnQLbfcMs4666ysDob5X+PGjYtTTjklunbtGtdee20sWrQotb0AABqCF198MU488cTE9TvvvHPsscceNd7viy++SFzboUOHaN26dY33yobVVlut2pqpU6c6YwcA+IFc32P+kF4mAADUHgZdAzQQixcvjjlz5iSuz/cDDUkOACMiJk+enHISAACSePXVVxPVLV26NOUk/7VkyZL4y1/+El27do3/+7//i6qqqpztDQBA3ZbJAxUbb7xxikmSSdJP1UsFAKgdlixZEmPGjKm2Lpe91IiITz/9NI466qjo3r17jB49Oqd7AwDUJ7kcQGgwDABAw/HnP/85o89AHnnkkVFYuOqPjib5bGhlZWVUVlau8l5JffXVV3HmmWfG+uuvH4MGDcrZvgAA9cmQIUNir732ioULFyaqb9KkSdxwww2rtOeXX36ZuHa99dZbpb2yoW3bttXWVFRUxMyZM3OQBgCg9svHPeYP6WUCAEDtYdA1QAORyQFgUVFRrLPOOimmqV6SA8CIiM8//zzlJAAAJJF00HU+zJ8/P84555zYZZddYsaMGfmOAwBAHVAfH6iYNm1aDpIAAFCdsWPHxoIFC/IdY4UmTJgQ/fr18/JAAIAayuUAwvrYxzQYBgDgp7766qu4/fbbE9cXFRXFUUcdtcr7zpo1Kz788MNVvk5aPvnkk9hzzz3jjDPOiMWLF+c7DgBAnVBZWRlXX311DBw4MObOnZt43Z///Ofo1q3bKu397bffJq5de+21V2mvbEjy4r4In80EAMjnPeb39DIBAKB2MegaoIHI5ABwzTXXjOLi4hTTVK9Vq1ZRVFRUbZ0DQACA/Js9e3a89957+Y5RraFDh8bmm28eEyZMyHcUAABqufr4QMXixYszGnwDAEA6Ro4cme8I1VqyZEmcc845ccABB8TSpUvzHQcAoM7I9QDC+tjHjPC5UACA/3XllVfG/PnzE9fvv//+0aVLl1Xety70MquqquK6666LAQMGZDREBwCgIRo1alRsu+22cdZZZ2V0DnzUUUfFscceu8r7Z3K/Vhv6mW3atElUp58JADRk+b7H/J5eJgAA1C4GXQM0EHXtALCwsDBatmxZbZ0DQACA/Bs1alRUVVXlO0YiM2bMiB133DEmTpyY7ygAANRida2f6oEKAIC649VXX813hMQee+yxOOiggwy7BgBIKNcDCPUxAQDqv88++yxuvvnmjNacddZZWdm7LvUyR4wYEbvvvnvMmzcv31EAAGqVqqqqGDx4cAwcODC22WabjAcA7rHHHnHrrbdmJUsmvdPOnTtnZc9V0axZs0R106dPTzkJAEDtUpvuMb+nlwkAALWLQdcADURdOwCMSHYI6AAQACD/6sKbbn/o+2HXX375Zb6jAABQS9W1fqoHKgAA6o661k999NFH44QTTsh3DACAWi8fAwj1MQEA6r+zzjorFi5cmLh+4MCBscUWW2Rl77rWy3zllVdi//33j6qqqnxHAQDIq++++y6GDh0ap556aqy11lqx2267xbPPPpvxdXbfffd45JFHori4OCu5Fi1alLi2ffv2WdlzVTRv3jxRnRf3AQANQW29x/yeXiYAANQu2b3jB6DWqmsHgBHJDgEdAAIA5N+IESNW+RoFBQWx0UYbRY8ePaJHjx6x4YYbRuvWraNVq1ZRVlYWs2fPjq+//jo+/fTTGDVqVIwePTrGjh1b40O8adOmxXHHHRePP/74KmcHAKD+qWv9VA9UAADUDZ999ll88sknq3yd8vLy2HzzzaNHjx6x2WabRefOnaNly5bRqlWrqKqqiq+//jq+/vrrGD9+fIwaNSpeffXVVRoWePvtt8fAgQPj5z//+SpnBwCor/IxgFAfEwCgfnvppZfioYceSlxfUFAQf/jDH7Ky95IlS2LMmDGrfJ3S0tJlnwvdbLPNomvXrst6mUVFRfH111/HN998E5MmTYpRo0bFyJEj4+OPP67xfs8//3zccMMNceqpp65ydgCA2m7x4sXx8ccfx9SpU+ODDz6Id955J95+++148803Y8mSJat07UMOOSTuueeerA4gXLp0aeLadu3aZW3fmkraz/TiPgCgPqlr95gRepkAAFAbGXQN0EDUtQPAiGSHgA4AAQDyq6KiIkaPHl3j9dtuu20cccQRseeee8bqq6+eaM3hhx8eERGTJ0+OO+64I+66666YOXNmxns/8cQTce+99y67HgAAfK+u9VM9UAEAUDesyksDy8vL44ADDohDDjkk+vfvHyUlJdWu2W233SIiorKyMv71r3/F7bffHk8++WRUVFRkvP+xxx4bffv2jdVWWy3jtQAA9V2+BhDqYwIA1F9LliyJE088MaM1v/jFL2KzzTbLyv5vvvlmLFiwoEZrS0pKYq+99orDDjssdtxxxygrK1thbdeuXSMiYpdddomTTz45Iv7TR73jjjvioYceyuhlMt8799xz42c/+1lssMEGNcoPAFBX/P3vf48jjzwyq9csKiqKyy+/PM4555ysXjciMjqnrg0v7isqKoqysrL47rvvVlo3e/bs3AQCAMiBunaPGaGXCQAAtVFhvgMAkBt17QAwItlDDQ4AAQDy6+2334558+ZlvG7nnXeO0aNHx/Dhw+Poo49OPOT6h9ZZZ5244oor4sMPP4wzzzyzRm/xPe+886r90BkAAA1P0n5qaWlptGrVKuU01Us6IEY/FQAgv2oy6Lpx48Zx3nnnxdSpU+Puu++OnXbaKdGQ6x8qLCyMXXfdNR599NF44403om/fvhnnmDVrVvz+97/PeB0AQH2XzwGEde1zod8PhqmOPiYAQMRVV10V48ePT1zfpEmT+L//+7+s7V+TXmZRUVH8+te/jo8++ij+8Y9/xJ577pno/u9/9e3bN+6+++6YMGFC7LHHHhmvX7hwYZx99tkZrwMAaOjWWGONeP7551MbQFhVVZW4tk2bNqlkyFSTJk2qrfn2229zkAQAoG5K+x4zQi8TAABqI4OuARoIB4AAAKQh0wPAVq1axUMPPRRDhgyJrbbaKisZmjZtGldffXW8/vrrsdZaa2W0dvr06XH99ddnJQcAAPVH0n5q69atU06STJJeaoR+KgBAvmXaT+3Xr1+MGzcurrjiiqyd4/fo0SOGDx8e11xzTRQWZvbRsVtuuSWmTJmSlRwAAPVFPgcQ+lwoAED9NHHixLj88sszWvPb3/42489PrkymvcxNNtkkXnvttfjrX/8aa665ZlYyrL322vH000/H3//+92jcuHFGa5966ql49dVXs5IDAKAhOPjgg2PcuHGx4447prZHQUFBorrS0tKMX/6cliT3oXPmzMlBEgCAuicX95gRepkAAFAbGXQN0EAkPQCMiGjevHmKSZJL0rxZtGhRLFq0KAdpAABYnuHDhyeu7dGjR7z11lvxy1/+MpUsPXr0iFGjRkXPnj0zWnf11VfHvHnzUskEAEDdlLSfWpd6qREeqAAAyKe5c+fG22+/nbj+zDPPjBdeeCG6du2a9SwFBQVxxhlnxGOPPZb4pSkREYsXL854wA4AQH2W7wGEBsMAANQ/FRUVcfjhh2f0nMw666wT5513XlZzvPLKK4lrDzrooBg9enTGn93M5PrDhg3L+OUtF198cSp5AADqm6KiomjWrFnqz9UkfRFzixYtUs2RiST9TC/uAwD4qVzdY0boZQIAQG1k0DVAA5H0ADCi9hwCJh3O4hAQACB/Xn755UR12267bbz00kvRuXPnVPN06NAhhg0bFhtssEHiNd98803cfvvtKaYCAKCuqWsPVBQXF0dRUVG1dXqpAAD588orr0RFRUWi2muuuSauvvrqRPd4q2LvvfeOf/zjHxl9nuD++++PGTNmpJgKAKBuqA0DCOtaHzPCYBgAgOpcddVV8dprr2W05i9/+UtGL7SrznvvvRdffPFFotpTTz01/va3v0VZWVnW9l+ePn36xJAhQzLaZ+jQofHWW2+lmAoAoH6oqKiIW2+9NdZbb7046aSTUjsPTnr+3bRp01T2r4nS0tJqa7y4DwDgp3J1j6mXCQAAtZNB1wANRCYPwNaWQ8AkB4ARDgEBAPJl3LhxMXPmzGrrNt988xg0aFDOHp5t2bJlPPPMM9G6devEa66//vqorKxMMRUAAHWJByoAAMi2YcOGJar7/e9/H2eccUbKaf5r4MCBcfXVVyeuX7RoUdx0000pJgIAqBtqwwBCfUwAgPrlrbfeissuuyyjNQcffHDsuuuuWc2RtJd59NFHx/XXXx8FBQVZ3X9FevbsGffdd19G+11zzTUpJgIAqF8WLVoUN998c2y88cbx+OOPZ/36jRo1SlRXUlKS9b1rKsmL+/QzAQBWLO17TL1MAAConQy6Bmggkh4ARtSeQ8AkB4ARDgEBAPKlU6dOMWzYsLj77rvj0ksvjaOPPjp23nnn6NatW5SXl0dExOqrrx5PPfVUNG/ePKfZ1l133bjzzjsT13/yyScxZMiQFBMBAFCXeKACAIBs+/Wvfx1PPfVU3HTTTXH22WfHgQceGH369Ik111xz2YDCgw8+OC688MKcZzv99NPj5z//eeL6u+++OyoqKlJMBABQu9WWAYT6mAAA9cd3330XBx98cCxevDjxmg4dOsSNN96Y9Sw///nPY9CgQXHLLbfE+eefH7/61a+if//+0aVLl2X3oP37949bbrkl63tXZ7/99otTTjklcf1jjz0Ws2fPTi8QAEAetWvXLtZaa62sD+v7+uuvY7/99oujjz465s2bl7XrJu1TZvI8fNqSvGxw/vz5OUgCAJAbde0eUy8TAABqp+J8BwAgNzJ5UKG2HAImOQCMiKw2sQAASK5FixYxYMCAFf78q6++iqVLl0a7du1ymOq/9t5779htt93iueeeS1R/9913x89+9rOUUwEAUBfU1wcq9FIBAPJn3XXXjXXXXXe5P6uoqIjPP/88Vl999Ryn+q/rr78+hgwZEgsWLKi2dtq0aTFkyJDYbbfdcpAMAKB2qU0DCOtrH9NgGACgITr99NNj4sSJGa254447onXr1lnP0qlTp+jUqdNyf1ZZWRnTp0+PZs2aRXFxfh5Nveyyy+Lhhx+OGTNmVFu7cOHCePDBB+M3v/lNDpIBAOTWbrvtFlOnTo158+bFuHHjYsyYMfHaa6/Fyy+/HJ988skqX/+uu+6Kd955J4YMGRKtWrVa5euVlpYmqsvXfebyFBYWVltTVVUVS5curVW5AQBqqq7dY+plAgBA7VR9ZxWAeiHpAWBE7TkETHIAGBGxZMmSlJMAAFATbdq0yduQ6+/dcMMNiV+g8vTTTyca4gIAQP1XXx+o0EsFAKidioqKYq211orGjRvnLUOXLl3irLPOSlz/yCOPpJgGAKD2qk0DCOtrH/P7wTAAAA3FY489FrfddltGa4499tjYfffdU0q0YoWFhdGxY8do3rx5zvf+XvPmzeOKK65IXK+XCQDUd02bNo2tt946Tj755Lj//vtj6tSp8cEHH8Sf//zn2GGHHRI/U7M8r7/+egwYMCC+/PLLVc5ZVlaWqK6ysnKV98qWpH/v9DMBgPqmrtxjroxeJgAA5I9B1wANRNIDwIjacwjoABAAgFW17rrrxm677ZaodsGCBTFs2LCUEwEAUBfU1wcq9FIBAFiZE044IfEQxEGDBtWq+2EAgFyobQMI62sfM0IvEwBoOD7++OM4+uijM1rTrVu3uO6661JKVDcccsgh0aZNm0S1r7zySsyePTvdQAAAtcy6664bp5xySvz73/+OTz75JC6//PJYc801a3StsWPHxg477BBz585dpUxJ+5mLFy9epX2yKWk/c8mSJSknAQDIv9p4j1kX6GUCANAQGXQN0EBkMui6thwCOgAEACAbjjnmmMS1zz77bIpJAACoK+rrAxV6qQAArEy7du1ijz32SFQ7a9aseP3111NOBABQe9TGAYT1tY8ZoZcJADQMixcvjl/84hcxZ86cxGtKS0vjoYceivLy8hST1X4lJSVx6KGHJqpdunRpDBkyJOVEAAC11xprrBHnn39+fPTRR3H77bdHhw4dMr7GuHHj4thjj12lHEnvYetib9CL+wCAhqa23GPWBXqZAAA0RAZdAzQQmXyIra4dAjoABABgZQYOHBgtWrRIVPvKK6+knAYAgLqgvj5QoZcKAEB1Dj744MS1+qkAQENRWwcQ1tc+ZoReJgDQMPz2t7/N+GVyf/rTn6JHjx4pJapb9DIBADJTUlISxxxzTLz//vtx0kknZbz+4YcfjltvvbXG+yd9rmfevHk13iPbFi5cmKiuLvZgAQCyId/3mHWFXiYAAA2NQdcADUTSA8CI2nMI6AAQAIBsKC4uji233DJR7fjx4+Pbb79NOREAALVdfX2gQi8VAIDqbLPNNolrR44cmWISAIDao7YOIKyvfcwIvUwAoP576KGH4sYbb8xozT777BMnn3xySonqns033zwaN26cqFYvEwDgv5o2bRo33nhjPPnkk1FWVpbR2tNOOy0++OCDGu2btJ+ZyQsH0/bdd98lqtPPBAAaunzdY9YVepkAADQ0Bl0DNBBNmjSJkpKSRLW15RDQASAAANmy1VZbJaqrrKyMd999N+U0AADUdvX1gQq9VAAAqrPmmmtGhw4dEtWOHTs23TAAALVAbR5AWF/7mBF6mQBA/fbee+/Fsccem9GaddddN+6+++6UEtVNxcXFsfnmmyeqfffdd6OysjLlRAAAdcvPf/7zGDp0aJSXlydes3DhwjjnnHNqtF/r1q0T1S1YsCAWL15coz2ybcGCBYnqKioqUk4CAFA35Poes67QywQAoKEx6BqgAWnVqlWiutmzZ6cbJCEHgAAAZEuPHj0S106cODHFJAAA1AVJH6ioLb3UiGT9VL1UAACSSNpP/fjjj2vNA8YAAGmo7QMIDYYBAKh75s6dG/vuu2/Mmzcv8ZrGjRvHo48+mvhFJw1J0l7mokWL4uOPP045DQBA3bPNNtvEww8/HAUFBYnXPPHEEzF+/PiM91pttdUS13755ZcZXz8NSfuZxcXFKScBAKg7cnmPWZfoZQIA0JAYdA3QgCQ9BHQACABAfdOmTZvEte+//36KSQAAqAuS9lK/+uqrqKqqSjlN9RYvXhyVlZXV1umlAgCQRNJ+akVFRXz00UcppwEAyI+6MIDQYBgAgLqlqqoqDjvssJg4cWJG62666abEQ1AaGp8NBQBYdQMHDozTTz89ozU33XRTxvu0bt06CguTjTf57LPPMr5+GubOnZuorlGjRiknAQCoW3J1j1mX6GUCANCQGHQN0IC0bds2UZ0DQAAA6ptWrVolrp0+fXqKSQAAqAuS9lIXL14cs2bNSjlN9fRSAQDIJv1UAKChqysDCA2GAQCoWy6//PJ48sknM1pz7LHHxtFHH51OoHpALxMAIDsuvfTSaN++feL6xx57LCorKzPao7CwMNq1a5eo9vPPP8/o2mmYM2dO4hch6mcCAPxULu4x6xK9TAAAGhKDrgEakA4dOiSqqw0HgBHJH6xwAAgAQHVatGiRuHbmzJkpJgEAoC7I5MN0taGfqpcKAEA26acCAA1dXRlAaDAMAEDd8dxzz8XFF1+c0ZrevXvHTTfdlFKi+kEvEwAgO5o2bRpnnXVW4vpZs2bF6NGjM96nY8eOieqmTJmS8bWz7dNPP01c26RJkxSTAADUTbm6x6wr9DIBAGhIDLoGaEDq0gFgRPJDQAeAAABUZ/HixYlrv/rqqxSTAABQF5SWlsZqq62WqLY29FP1UgEAyCb9VACgIatrAwjr0udCDYYBABqqSZMmxUEHHRSVlZWJ17Rr1y4ee+yxKCkpSTFZ3aeXCQCQPUcddVRG95+vvvpqxnustdZaieomTZqU8bWzLWk/s7S0NEpLS1NOAwBQN+XiHrOu0MsEAKAhMegaoAGpSweACxYsSNx4yeStZQAANEzz5s1LXLtw4cIUkwAAUFfUpX5q0gcq9FIBAEhCPxUAaKjq4gDC+tjHNBgGAKhP5syZEz//+c9jzpw5idcUFxfHP/7xj8QvNWnI9DIBALKnZcuWMWDAgMT1b775ZsZ7rLPOOonqJk6cmPG1s+2zzz5LVOdzmQAAK5aLe8y6Qi8TAICGxKBrgAYk6QHgRx99FEuWLEk5zcolPQCMcAgIAED15s+fn7h20aJFKSYBAKCu8EAFAAANlX4qANAQ1dUBhPqYAAC1V2VlZRx00EEZv3Tkz3/+c/Tr1y+lVPWLXiYAQHbtuOOOiWs//vjjjK+/7rrrJqp79913M752tn3wwQeJ6lq1apVyEgCAui3te8y6Qi8TAICGxKBrgAYk6QHg0qVL8/5QQ9IDwAiHgAAAVG/69OmJawsKClJMAgBAXeGBCgAAGir9VACgoanLAwj1MQEAaq8zzzwznnvuuYzWHH/88XHCCSeklKj+0csEAMiuLbfcMnFt0hfb/dCGG26YqO7rr7+OyZMnZ3z9bHrzzTcT1XXo0CHlJAAAdVva95h1hV4mAAANiUHXAA1I165do6SkJFHtmDFjUk6zckkPAIuKiqJt27YppwEAoK776KOPEtc2bdo0xSQAANQVSR+oePfdd2PRokUpp1k5D1QAAJBN+qkAQENTlwcQGgwDAFA73XbbbXH99ddntKZ///5x4403phOontLLBADIrnXWWSdx7dy5czO+/qabbpq4Nt/Pub/11luJ6jp27JhyEgCAui3te8y6Qi8TAICGxKBrgAakuLg4unXrlqg23weASR9oaN++fRQVFaWcBgCAuu7DDz9MXOsAEACAiOQPVCxZsiTGjh2bbpiVmDNnTuIBNR6oAACgOkuWLIlPPvkkcb1+KgBQ19X1AYQGwwAA1D7Dhg2Lk046KaM1nTt3jkcffTQaNWqUUqr6yWdDAQCya4011khcu2DBgoyv36pVq+jatWui2uHDh2d8/WyZMmVKfP3114lq9TMBAFYu7XvMukIvEwCAhsSga4AGplevXonq8nkAGJF80LUDQACA2qmioiLfEX7knXfeSVybyaEpAAD11yabbBJNmjRJVJvPfupbb70VVVVViWr1UwEAap/a1kudMGFCLF26NHG9fioAUJfVhwGEBsMAANQu48ePj/322y+WLFmSeE15eXn885//jLZt26aYbNVVVVUlPpvOhW+++SY+/fTTxPV6mQAA1SsuLo7i4uJEtUVFRTXao2/fvonqhg0bVqPrZ0PSZ9wjInF/FgCgocrFPeb/0ssEAID8MugaoIFJegA4fvz4mDlzZspplu+rr76KqVOnJqp1AAgAUDt899138eyzz8Ypp5wS66+/flxzzTX5jrTM7NmzMxp07R4TAICIiEaNGiV+caAHKgAAyMSnn34at99+e+y3336x2mqrxYwZM/IdaZmXXnopo3r3mABAXVWfBhAaDAMAUDvMmDEjBg4cGHPmzEm8pqCgIB544IHo0aNHislqbtasWfHAAw/Er371q2jXrl289tpr+Y60zPDhwzMaVuM+EwAgmYKCgkR1TZo0qdH1+/Tpk6hu4sSJ8fnnn9doj1U1dOjQxLXdunVLMQkAQP2Q9j1mhF4mAADUJsledQNAvZH0ADDiPwdxhxxySIppli+ThykcAAIA5M+ECRNi8ODBMXjw4Hj55Zdj0aJFy3729NNPx9lnn53HdP81fPjwqKysTFzvABAAgO/16dMnXn755Wrrhg8fHosWLYrS0tIcpPoxD1QAANR+ixYtipdffnlZP3XChAk/+vmgQYPi6KOPzlO6H8tk0HVRUVF06dIlvTAAACmpbwMI+/TpE/fdd1+1dd8PhunYsWMOUv2YPiYAUN/NnTs3dt9995g6dWpG66688srYe++90wlVA0uXLo1Ro0Yt62W++eabPxrA8swzz0Tv3r3zmPC/vLQPAKirKisrY/LkyTF+/PiYMGFCjB8/PsaPHx+NGjXK+zC+RYsWJX45YIsWLWq0R9IX90VEPPHEE3HSSSfVaJ9V8dxzzyWu3WCDDVJMAgCQTEO8x9TLBACA2suga4AGZoMNNojVVlstvvzyy2prn3jiibwMunYACABQu51++unx+OOPxyeffLLCmpEjR8Y333wTrVq1ymGy5Rs0aFBG9VtssUVKSQAAqGuSPlAxf/78+Ne//hV77LFHyol+bOHChfHiiy8mqi0rK4tOnTqlGwgAgB+ZNGlSnHnmmfHCCy/Ed999t8K62jLoeuHChRm9mHrTTTeNRo0apZgIACD76ssAwh8yGAYAIL8WL14c++67b7z11lsZrTv88MPjnHPOSSlVZmbNmhUnnHBC/Otf/1rpC2EGDRoUv//973OYbMUy+Wxoq1atYu21104xDQDAyj300EPxzDPPxPjx42PixImxcOHC5da9+eab0bNnzxyn+6/PPvssce0aa6xRoz023njjWGONNWLatGnV1j722GM572dOnDgxpkyZkqi2ffv20a5du3QDAQCsQEO9x9TLBACA2q8w3wEAyL2dd945Ud1zzz230odt01BVVRWDBw9OXL/ZZpulFwYAgOUaO3bsSodcR0RUVFTEk08+mZtAK7F48eJ45JFHEtc3bdo0NtlkkxQTAQBQl2y33XZRUlKSqPaxxx5LOc1Pvfjii7FgwYJEtd27d4+CgoKUEwEA8EONGzeOQYMGVXvuPmTIkJg3b16OUq3Y008/vdIHP/7XNttsk2IaAIDsqw8DCJfn+8EwSeSjj2kwDABQn1VWVsahhx4aQ4cOzWhd375947bbbkspVeaaN2+eqD/41ltvxeTJk3OUasXGjBkTkyZNSly/9dZbOy8HAPJq7Nix8be//S3Gjh27wgGEERH33ntvDlP9VCb3WB07dqzRHgUFBbHHHnskqn355Zfj008/rdE+NZXJS/u22mqrFJMAAKxcQ73H1MsEAIDaz6BrgAZozz33TFT33XffxRNPPJFymh8bO3ZszJgxI1Fty5YtY/311085EQAA/2vbbbdNVHfnnXemnKR6zzzzTHzzzTeJ63v37h1FRUUpJgIAoC5p1qxZbL/99olqn3jiiZy/ONADFQAAtVvnzp1jzTXXrLZu/vz58dBDD+Ug0crdf//9GdX36dMnpSQAANlXXwYQLo/BMAAA+XP88cfHI488ktGatddeO5544onEL13OhdLS0ujVq1ei2rvuuivlNNXTywQA6prevXsnqnvggQdi7ty5KadZsZEjRyau3WijjWq8T9Ln3CsrK+OOO+6o8T41kclz9Un/dwUASENDvcfUywQAgNrPoGuABmi33XaL4uLiRLW5fkAjkwPAXr16eQsZAEAe9O/fP1HdiBEjYuLEiSmnWbk//elPGdXvvvvuKSUBAKCuSvpAxZw5c+Lhhx9OOc1/VVVVxZNPPpm43gMVAAD5kbSfmuuHc//XhAkTYtCgQYnrCwsLY9ddd00xEQBAdtWXAYQrYjAMAEDunXnmmRnfW7Vs2TIGDRoUbdu2TSlVzSXtZd5zzz1RUVGRcpoV+/LLL+Oee+7JaI3PhgIA+danT59Ez0N//fXXcc011+Qg0fINGTIkce0mm2xS43123HHHKCsrS1R7xx13xOLFi2u8VyY++uijGD58eOL67bbbLsU0AAAr15DvMfUyAQCgdjPoGqABatmyZfTr1y9R7csvvxzjxo1LOdF/VFVVxX333Ze43gEgAEB+9O/fP8rLyxPV/vnPf045zYo988wzMWrUqIzW/PznP08pDQAAdVXSATEREX/5y19STPJjL7zwQnzyySeJ6/VTAQDyY7fddktUN3r06Bg9enTKaVbsoosuisrKysT1ffr0qZXDeAAAlqe+DSBcHoNhAABy6+yzz45rr702ozXFxcXx6KOPxoYbbphSqlWTtJf5+eefx6OPPppymhW76qqrYu7cuYnrO3XqFD179kwxEQBA9dq1a5f4nuS6666Lr776KuVEPzV58uR47bXXEtevykvrmjRpEvvss0+i2mnTpsVdd91V470ycdtttyWubdGihRf3AQB51ZDvMfUyAQCgdjPoGqCBOvjggxPX/uEPf0gxyX8NHjw4pk6dmrh+l112STENAAArUlpaGjvvvHOi2rvuuiuje7xsWbx4cZx//vkZrdl0002ja9euKSUCAKCu6ty5c/Tt2zdR7euvvx6DBw9OOdF/3HrrrYlrN9poo+jYsWOKaQAAWJHdd989ioqKEtVedNFFKadZvpEjR8bjjz+e0ZqkDx0DAORbfRxAuDwGwwAA5M5ZZ50Vf/rTnzJe99e//jV23HHHFBJlxzbbbBOrrbZaotpLL700oxfnZcvHH3+c8Quo995773TCAABkaODAgYnqvv3224yfh8mGm2++OXFtNj6TeOSRRyauvfLKK2PhwoWrtF915syZE7fcckvi+gEDBkRxcXGKiQAAqtdQ7zH1MgEAoHYz6BqggfrFL34RZWVliWr/8Y9/xLhx41JOFPHHP/4xce1qq60WW2yxRYppAABYmaQPyC5evDguvfTSlNP81DnnnBPvvvtuRmuOOeaYlNIAAFDXZfJAxSWXXBJVVVUppon46KOP4rHHHktcv+uuu6aYBgCAlWndunX0798/Ue2QIUPi5ZdfTjnRj82ZMycOPvjgjO5hS0pK4tBDD00xFQBAdtTXAYQrYjAMAEC6qqqq4oQTToirr74647XnnHNOrf+MYlFRUey5556Jat9777144IEHUk70Y0uWLIkDDzwwFixYkNG62v73HQBoOH75y18mrr3tttti0KBBKab5sS+++CJuv/32xPVJByquzIABA2KttdZKVPvJJ59k9Ax6TVx99dXx7bffJq4/4IADUkwDAJBMQ73H1MsEAIDazaBrgAaqefPmse+++yaqraysjJNPPjnVPMOGDYuXXnopcf2+++4bhYX+MwYAkC/77bdfNG3aNFHtPffcE8OGDUs50X899dRTcf3112e0pqysLA4//PB0AgEAUOdl8uLA0aNHx3333ZdqnksuuSQqKioS13ugAgAgvzLpPR533HEZP9xQU1VVVXH00UfHlClTMlq3//77R9u2bdMJBQCQBfV9AOGKGAwDAJCeqqqqOO644+Kvf/1rxmv322+/uPLKK1NIlX2Z9DLPPPPMmDlzZoppfuzcc8+N1157LaM1ffr0ie7du6eUCAAgMxtttFH07t07cf3RRx8dX3zxRYqJ/uvCCy+MuXPnJq4/7LDDVnnPgoKCjF7ed9VVV8V77723yvsuz5QpUzLqJ5eVlcVee+2VShYAgEw05HtMvUwAAKi9TAgFaMCOPvroxLUvvvhi3HPPPankWLp0aZx22mkZrTnwwANTyQIAQDLl5eWJHzKtqqqKI488MmbPnp1uqIgYOnRoje4VDz/88GjRokUKiQAAqA+aNWuW0ZCVs88+O7UP/40aNSr+9re/Ja7v3LlzbLPNNqlkAQAgmf333z+aNWuWqHbSpElxzjnnpJzoP33b3/zmN/HYY49lvPaUU05JIREAQHY0lAGEy2MwDABAek488cS44447Ml639dZbx/333x8FBQUppMq+/v37R9euXRPVfvnll3HcccelnOg/rrjiirj22mszXnfqqaemkAYAoOYyuX+aOXNm7LHHHjFv3rwUE/3nOZxM7nW32mqr2GSTTbKy9wknnBCNGzdOVLtw4cI48MADY+HChVnZ+3uVlZVxxBFHZHTdfffdN8rLy7OaAwCgphrqPaZeJgAA1F4GXQM0YNtvv31svvnmietPPvnkeP/997Oe49JLL41x48Ylru/cuXNst912Wc8BAEBmTjrppMS1n376aeyzzz5Z/0DZDw0ePDj23HPPWLBgQUbrysvL46KLLkopFQAA9cXpp5+euPaLL76Iww47LKqqqrKa4bvvvosjjjgio+seeuihWc0AAEDmysvLMxo4eOONN8Ytt9ySWp7Kyso49thj49Zbb8147b777hu9e/dOIRUAQHY0lAGEK2IwDABA9l177bU1epFK165d46mnnoomTZqkkCodBQUFceKJJyauf+qpp+Lcc89NMdF/njm64IILMl635ZZbZvRCawCAXPjVr34VnTp1Slw/ZsyY2HvvvWPRokWp5Pnss88y/qzj+eefn7X9V1999TjssMMS17/zzjtx+OGHR2VlZdYyXHTRRfHSSy9ltObkk0/O2v4AAKuqod5j6mUCAEDtZdA1QAN31llnJa6dN29e7LXXXvHVV19lbf/nn38+rrzyyozWnHDCCVFY6D9hAAD51rNnz/jZz36WuP7FF1+MX/7ylxkPoq5OZWVlXH755bHnnnvW6AHcs846K9q3b5/VTAAA1D89evSIXXbZJXH9888/n1H/NYnjjz8+Jk2alLi+uLg4fv3rX2c1AwAANfPb3/42GjVqlLj+xBNPjPvvvz/rOaZNmxY77bRT3HnnnRmvbdSoUVx11VVZzwQAkC0NaQDhihgMAwCQXa+88kqNzn3btGkTzz77bLRt2zaFVOk67rjjok2bNonr//jHP8YVV1yR9Rxz5syJX/7yl3HJJZfUaP3VV19d519kAwDUPyUlJXHOOedktGbYsGGx9957x9y5c7Oa5euvv44999wzpk+fnnjN5ptvHnvttVdWc5x55pkZPTP+yCOPxAknnJCVnuZf//rXuPzyyzNa07t379hqq61WeW8AgGxpyPeYepkAAFA7mRIK0MAdcMAB0blz58T1kyZNit133z2++eabVd57zJgxsf/++0dFRUXiNWVlZXHMMces8t4AAGTHRRddlFH9U089FVtvvXV88MEHWdl/6tSpsdNOO8WFF14YS5cuzXj9RhttFGeffXZWsgAAUP9l+gDzNddck/FDECty7rnnxgMPPJDRmv322y86duyYlf0BAFg1nTp1iqOOOipxfWVlZRx22GFx6qmnxpIlS7KS4Yknnoju3bvHCy+8UKP1F154Yay33npZyQIAkG0NcQDhihgMAwCQHfPnz4/DDjss4/uk0tLSePLJJ2P99ddPKVm6ysvL47e//W1Gay644IL45S9/mbXBOMOHD4/NNtssHnnkkRqtP/LII2O77bbLShYAgGw7/vjjY+ONN85ozeDBg6NPnz4xbty4rGSYMmVKbL/99jF27NjEawoLC+Mvf/lL1gfwrb/++nHAAQdktObWW2+NAw44IL777rsa7/uHP/whTjjhhIzXXXzxxTXeEwAgLQ31HlMvEwAAaieDrgEauOLi4jj33HMzWvPaa69F37594+OPP67xvoMHD44BAwbEvHnzMlp30kknRevWrWu8LwAA2bXNNtvEwQcfnNGad955J7p37x6nnXZaRm/l/aGPP/44zjzzzOjWrVuNh7I0btw4Hn744WjSpEmN1gMA0PDstNNOsfXWW2e05sILL4yTTjqpRi9miYhYunRp/OY3v4k//vGPGa0rLCyM3/3udzXaEwCAdPz+97+Pli1bZrTmhhtuiA033DDuueeeGt1TVlVVxZAhQ2LAgAGx7777xldffZXxNSIi+vfvHxdeeGGN1gIApK2hDiBcEYNhAACy449//GPGz80UFBTEvffeG9tuu21KqXLjtNNOi3XWWSejNY888kisv/76cf3118eCBQtqtO/IkSNjv/32i/79+8eUKVNqdI0NNtggbrzxxhqtBQDIheLi4rjpppsyHuY3bty42HLLLeO8886Lb7/9tkZ7V1VVxQMPPBA9e/aMd999N6O1J554Ysafn0zqyiuvjNLS0ozWPP7447H55pvH6NGjM1o3bdq02H333Wv0+cq+ffvGbrvtlvE6AIC0NeR7TL1MAACofQqqqqqq8h0CgPxaunRpdO/ePd57772M1jVv3jxuvPHGOOywwxKvWbhwYVx00UVxzTXXZPxQSfPmzePjjz826BoAoJaZNm1abLTRRjFnzpyM1xYVFUXv3r1j5513jk033TTWW2+9WGONNaJVq1ZRVFQUEf+5h5w5c2a899578frrr8fgwYNj5MiRGd9P/q+77747jjjiiFW6BgAAmevSpUtMnTq12rqLL744LrnkkvQDZWjEiBE1eih5q622ivvuuy822GCDxGsmTpwYhx9+eLz22msZ73fIIYfEAw88kPE6AIC6qC7dY958881x0kkn1Whty5YtY6eddortttsuNthgg+jatWu0bds2mjVrtqxm9uzZ8emnn8a4ceNi+PDhMWjQoPjkk09WKfOaa64Zo0aNio4dO67SdQAA0nLRRRfF73//+4zWFBQUxIMPPhi//OUvU0qVXx9//HFsuOGGsWjRoozWrb/++nHfffdF7969E6+ZNm1aHHPMMfHcc89lGjP69u0br7zySsbrAADSNmvWrOjSpUvGLwLp27dvHHPMMSmlqpmmTZvG/vvvn/G6Z555Jvbcc88a7VleXh7bb799DBgwILp16xbrrrturL766j96EeDcuXPj888/j/Hjx8err74agwYNikmTJtVov+81b948hg8fHt27d1+l6wAA5MKZZ54Z1157bY3WNm/ePI444og4+OCDY6uttqp2oOF3330XTzzxRFx33XXxxhtvZLxfr169Yvjw4RkPo87E5ZdfXqOXLxcUFMRBBx0U55xzzkrvAz/55JP4y1/+EjfeeGONXvhXVFQUo0ePji222CLjtQAAudJQ7zH1MgEAoHYx6BqAiIh4+eWXY4cddqjRsMCtt946Lrnkkth5552jsLBwuTXffvttPPDAA3HFFVfE559/XqOM1113XZx22mk1WgsAQLoeeeSRrD4AXFBQEI0aNYqIiMWLF2ftut+75ppr4owzzsj6dQEAqF5dGkK4Iscee2zccccdGa9r1KhRHHfccXHGGWfEOuuss8K69957L/785z/HnXfeGUuXLs14n6ZNm8aECROiU6dOGa8FAKiL6tI9ZlVVVeyxxx7x7LPPZu2aRUVFUVRUFBUVFVFRUZG160ZEtG3bNl5++eXo1q1bVq8LAJAtBhCumMEwAAA1d+WVV8b555+f7xhZ0blz55gyZUqN1p5wwgnx17/+NWtZCgsLo7i4OCorK2t0Fr4yZWVl8fzzz9foxdUAAPmwZMmS6NevX4wePXqVrtOmTZvo3bt3bLDBBtGpU6coLy+PwsLCmDt3bkydOjXGjRsXI0aMiIULF9bo+h06dIhRo0bFWmuttUo5q1NRURH9+vWLkSNH1vgam222WWy33Xax0UYbRcuWLePbb7+NyZMnx/Dhw+PVV1+t0TP03zv99NNrPDQSACBXGvI9pl4mAADUHgZdA7DM2WefHX/6059qvL5Tp06x0047xeabbx6rrbZaLFmyJD777LMYNWpUDBs2rEYPMnxvyy23jNGjR69wkDYAAPl38sknx0033ZTvGCtVUFAQV1xxRZx77rn5jgIA0GDVpSGEKzJ37tzYbLPNYvLkyTVaX1BQENtss03069cv1l133WjevHnMnj07Jk2aFC+99FK88cYbq5Tvz3/+c5xyyimrdA0AgLqkrt1jfvnll9GrV68aD5fJlfbt28fgwYOjR48e+Y4CALBCBhCumMEwAAA1t95668WHH36Y7xhZsSr3mQsXLox+/frF66+/nt1QWdasWbN4/PHHY6eddsp3FACAjHzxxRfRt2/fWnvvudpqq8VLL70UG220UU72mzZtWmy55ZYxffr0nOyX1CabbBKjR4+OsrKyfEcBAKhWQ73H1MsEAIDaw6BrAJZZunRp7LrrrvHvf/8731F+pLy8PMaMGRMbbrhhvqMAALASlZWV8Ytf/CIee+yxfEdZriZNmsS9994bBxxwQL6jAAA0aHVtCOGKvPvuu9GnT5+YN29evqP8yE477RTPP/+8lwYCAA1KXbzH/OCDD2LbbbeNL774It9RlmuzzTaLp556Kjp16pTvKAAAK2UA4coZDAMAkLlJkyZFt27d8h0ja1b1PnPWrFnRt2/f+OCDD7IXKou6dOkSTz/9dGyyySb5jgIAUCOTJ0+OAQMGJDrzzqWOHTvGc889F5tuumlO933jjTdiwIAB8e233+Z03xVp1qxZvP7667H++uvnOwoAQGIN9R5TLxMAAGoHT7gDsExxcXH84x//yPmhY3Vuv/12Q64BAOqAwsLC+Nvf/ha/+MUv8h3lJ7p16xavvPKKIdcAAGTNpptuGg899FA0atQo31GW6dixY/z973835BoAoA5Yb731YujQobHmmmvmO8pPHHnkkfHKK68Ycg0A1HqTJk2qN0Ou07LGGmvE008/Hc2bN893lGWaNWsWjz32mCHXAECtNXz48HxHqFXatm0bw4YNq5XDVwYOHBivvfZarcwGAJDUOuusE6+++mqtuqfZbLPNYvTo0Xl53nyLLbaI559/vlb0NEtKSuKxxx4z5BoAqHMa6j2mXiYAANQOnnIH4Edat24dw4YNi4033jjfUSIi4tJLL42DDjoo3zEAAEiotLQ0HnroobjggguioKAg33GisLAwTj/99HjrrbeiZ8+e+Y4DAEA9M3DgwHjkkUdqxbDrli1bxrPPPhtt27bNdxQAABLadNNNY/To0dGrV698R4mIiA4dOsTTTz8dd911V5SXl+c7DgBAtQwgTMZgGACAzIwbNy7fEWqdTp06xYgRI2L33XfPd5SIiGjevHnceeed8cwzzzgjBwDqhTXWWCNGjBgRBxxwQL6jxK9//esYOXJkdOzYMW8Ztt566xg+fHheXxxdXFwcf//732PnnXfOWwYAgFXRUO8x9TIBACD/DLoG4Cfatm0bw4cPj+222y6vOU4//fS46KKL8poBAIDMFRQUxB/+8IcYMmRIXj/YtvPOO8ebb74Z1157bTRu3DhvOQAAqN/23nvvGDx4cLRs2TJ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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scatter_out_1(df['预测值'].values/100, df['真实值'].values/100)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6e95d4f4-eb89-4263-8f81-f1694ad7e97f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples = np.random.choice(df.index.values, 50, replace=False)\n", + "plt.figure(figsize=(12, 9))\n", + "plt.plot(range(len(samples)), df.iloc[samples]['预测值'].values, 'o-', label='预测值')\n", + "plt.plot(range(len(samples)), df.iloc[samples]['真实值'].values, '*-', label='真实值')\n", + "plt.xlabel('预测值 $(10^2 cm^3/g)$', fontdict={\"fontsize\":16})\n", + "plt.ylabel('真实值 $(10^2 cm^3/g)$', fontdict={\"fontsize\":16})\n", + "plt.title('氮气吸附量拟合结果')\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37c6c196-acb0-43c3-a234-c05a2f44dda4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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