219 KiB
219 KiB
In [1]:
import os
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import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt #新增加的两行 from pylab import mpl # 设置显示中文字体 mpl.rcParams["font.sans-serif"] = ["SimHei"] mpl.rcParams["axes.unicode_minus"] = False
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data = pd.read_excel('./data/20240123/煤炭数据.xlsx', header=[1]) data.drop(columns=data.columns[11:], inplace=True)
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object_cols = ['活化剂种类', '混合方式'] data = pd.get_dummies(data, columns=object_cols)
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out_cols = ['比表面积', '总孔体积', '微孔体积'] feature_cols = [x for x in data.columns if x not in out_cols]
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train_data = data.dropna(subset=out_cols).ffill().reset_index(drop=True) train_data
Out[7]:
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11.25 | 17.06 | 3.0 | 800 | 1.0 | 5.0 | 2784.0 | 1.0830 | 0.853 | 1 | 0 | 1 |
| 1 | 8.53 | 13.46 | 3.0 | 800 | 1.0 | 5.0 | 2934.0 | 1.2290 | 1.074 | 1 | 0 | 1 |
| 2 | 18.08 | 13.85 | 3.0 | 800 | 1.0 | 5.0 | 3059.0 | 1.3044 | 1.011 | 1 | 0 | 1 |
| 3 | 11.42 | 12.31 | 3.0 | 800 | 1.0 | 5.0 | 2365.0 | 0.8030 | 0.605 | 1 | 0 | 1 |
| 4 | 11.60 | 8.49 | 3.0 | 800 | 1.0 | 5.0 | 2988.0 | 1.2820 | 0.944 | 1 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 153 | 4.18 | 9.77 | 1.5 | 800 | 1.0 | 5.0 | 1772.0 | 0.7383 | 0.660 | 1 | 0 | 1 |
| 154 | 4.18 | 9.77 | 2.0 | 800 | 1.0 | 5.0 | 2382.0 | 1.0370 | 0.899 | 1 | 0 | 1 |
| 155 | 4.18 | 9.77 | 2.5 | 800 | 1.0 | 5.0 | 2996.0 | 1.3520 | 1.162 | 1 | 0 | 1 |
| 156 | 4.18 | 9.77 | 3.0 | 800 | 1.0 | 5.0 | 3142.0 | 1.6080 | 1.204 | 1 | 0 | 1 |
| 157 | 4.18 | 9.77 | 3.5 | 800 | 1.0 | 5.0 | 3389.0 | 2.0410 | 1.022 | 1 | 0 | 1 |
158 rows × 12 columns
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import seaborn as sns
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train_data['比表面积'] = np.log1p(train_data['比表面积'])
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import tensorflow as tf import keras from keras import layers import keras.backend as K
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
In [13]:
class TransformerBlock(layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1): super().__init__() self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name) self.ffn = keras.Sequential( [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim),] ) self.layernorm1 = layers.LayerNormalization(epsilon=1e-6) self.layernorm2 = layers.LayerNormalization(epsilon=1e-6) self.dropout1 = layers.Dropout(rate) self.dropout2 = layers.Dropout(rate) def call(self, inputs, training): attn_output = self.att(inputs, inputs) attn_output = self.dropout1(attn_output, training=training) out1 = self.layernorm1(inputs + attn_output) ffn_output = self.ffn(out1) ffn_output = self.dropout2(ffn_output, training=training) return self.layernorm2(out1 + ffn_output)
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from keras import Model
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from keras.initializers import Constant
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# Custom loss layer class CustomMultiLossLayer(layers.Layer): def __init__(self, nb_outputs=2, **kwargs): self.nb_outputs = nb_outputs self.is_placeholder = True super(CustomMultiLossLayer, self).__init__(**kwargs) def build(self, input_shape=None): # initialise log_vars self.log_vars = [] for i in range(self.nb_outputs): self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,), initializer=tf.initializers.he_normal(), trainable=True)] super(CustomMultiLossLayer, self).build(input_shape) def multi_loss(self, ys_true, ys_pred): assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs loss = 0 for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars): mse = (y_true - y_pred) ** 2. pre = K.exp(-log_var[0]) loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1)) return K.mean(loss) def call(self, inputs): ys_true = inputs[:self.nb_outputs] ys_pred = inputs[self.nb_outputs:] loss = self.multi_loss(ys_true, ys_pred) self.add_loss(loss, inputs=inputs) # We won't actually use the output. return K.concatenate(inputs, -1)
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num_heads, ff_dim = 3, 12
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def get_prediction_model(): def build_output(out, out_name): # self_block = TransformerBlock(64, num_heads, ff_dim, name=f'{out_name}_attn') # out = self_block(out) # out = layers.GlobalAveragePooling1D()(out) # out = layers.Dropout(0.1)(out) out = layers.Dense(32, activation="relu")(out) return out inputs = layers.Input(shape=(1,len(feature_cols)), name='input') x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs) x = layers.Dropout(rate=0.1)(x) lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x) out = layers.Dense(128, activation='relu')(lstm_out) transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn') out = transformer_block(lstm_out) out = layers.GlobalAveragePooling1D()(out) out = layers.Dropout(0.1)(out) out = layers.Dense(64, activation='relu')(out) # out = K.expand_dims(out, axis=1) bet = build_output(out, 'bet') mesco = build_output(out, 'mesco') micro = build_output(out, 'micro') bet = layers.Dense(1, activation='sigmoid', name='bet2')(bet) mesco = layers.Dense(1, activation='sigmoid', name='mesco2')(mesco) micro = layers.Dense(1, activation='sigmoid', name='micro2')(micro) model = Model(inputs=[inputs], outputs=[bet, mesco, micro]) return model
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def get_trainable_model(prediction_model): inputs = layers.Input(shape=(1,len(feature_cols)), name='input') bet, mesco, micro = prediction_model(inputs) bet_real = layers.Input(shape=(1,), name='bet_real') mesco_real = layers.Input(shape=(1,), name='mesco_real') micro_real = layers.Input(shape=(1,), name='micro_real') out = CustomMultiLossLayer(nb_outputs=3)([bet_real, mesco_real, micro_real, bet, mesco, micro]) return Model([inputs, bet_real, mesco_real, micro_real], out)
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maxs = train_data.max() mins = train_data.min() for col in train_data.columns: train_data[col] = train_data[col].astype(float) if maxs[col] - mins[col] == 0: continue train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])
In [21]:
train_data
Out[21]:
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.265345 | 0.224627 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.916251 | 0.371910 | 0.417894 | 1.0 | 0.0 | 1.0 |
| 1 | 0.201133 | 0.160752 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.929645 | 0.426592 | 0.538462 | 1.0 | 0.0 | 1.0 |
| 2 | 0.426582 | 0.167672 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.940237 | 0.454831 | 0.504092 | 1.0 | 0.0 | 1.0 |
| 3 | 0.269358 | 0.140348 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.874116 | 0.267041 | 0.282597 | 1.0 | 0.0 | 1.0 |
| 4 | 0.273607 | 0.072569 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.934281 | 0.446442 | 0.467540 | 1.0 | 0.0 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 153 | 0.098442 | 0.095280 | 0.237288 | 0.62963 | 0.142857 | 0.0 | 0.797597 | 0.242809 | 0.312602 | 1.0 | 0.0 | 1.0 |
| 154 | 0.098442 | 0.095280 | 0.322034 | 0.62963 | 0.142857 | 0.0 | 0.875983 | 0.354682 | 0.442990 | 1.0 | 0.0 | 1.0 |
| 155 | 0.098442 | 0.095280 | 0.406780 | 0.62963 | 0.142857 | 0.0 | 0.934960 | 0.472659 | 0.586470 | 1.0 | 0.0 | 1.0 |
| 156 | 0.098442 | 0.095280 | 0.491525 | 0.62963 | 0.142857 | 0.0 | 0.947009 | 0.568539 | 0.609384 | 1.0 | 0.0 | 1.0 |
| 157 | 0.098442 | 0.095280 | 0.576271 | 0.62963 | 0.142857 | 0.0 | 0.966042 | 0.730712 | 0.510093 | 1.0 | 0.0 | 1.0 |
158 rows × 12 columns
In [22]:
# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x] feature_cols = [x for x in train_data.columns if x not in out_cols] use_cols = feature_cols + out_cols
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use_data = train_data[use_cols].copy() for col in use_cols: use_data[col] = use_data[col].astype('float32')
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from sklearn.model_selection import KFold kf = KFold(n_splits=10, shuffle=True, random_state=42)
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from keras import optimizers
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from keras.callbacks import ReduceLROnPlateau reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error def print_eva(y_true, y_pred, tp): MSE = mean_squared_error(y_true, y_pred) RMSE = np.sqrt(MSE) MAE = mean_absolute_error(y_true, y_pred) MAPE = mean_absolute_percentage_error(y_true, y_pred) R_2 = r2_score(y_true, y_pred) print(f"COL: {tp}, MSE: {format(MSE, '.2E')}", end=',') print(f'RMSE: {round(RMSE, 4)}', end=',') print(f'MAPE: {round(MAPE, 4) * 100} %', end=',') print(f'MAE: {round(MAE, 4)}', end=',') print(f'R_2: {round(R_2, 4)}') return [MSE, RMSE, MAE, MAPE, R_2]
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total_bet = list() total_micro = list() total_mesco = list() for train_index, test_index in kf.split(use_data): test = use_data.iloc[test_index].copy() train = use_data.iloc[train_index].copy() train, valid = train_test_split(train, test_size=0.2, random_state=42, shuffle=True) prediction_model = get_prediction_model() trainable_model = get_trainable_model(prediction_model) X = np.expand_dims(train[feature_cols].values, axis=1) Y = [x for x in train[out_cols].values.T] Y_valid = [x for x in valid[out_cols].values.T] X_valid = np.expand_dims(valid[feature_cols].values, axis=1) trainable_model.compile(optimizer='adam', loss=None) hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]], callbacks=[reduce_lr] ) rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1)) pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols) real_rst = test[out_cols].copy() for col in out_cols: pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col] real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col] pred_rst['比表面积'] = np.expm1(pred_rst['比表面积']) real_rst['比表面积'] = np.expm1(real_rst['比表面积']) y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,) y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,) y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,) y_true_pm25 = real_rst['比表面积'].values.reshape(-1,) y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,) y_true_so2 = real_rst['微孔体积'].values.reshape(-1,) bet_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积') mesco_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积') micro_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积') total_bet.append(bet_eva) total_mesco.append(mesco_eva) total_micro.append(micro_eva)
2024-04-08 11:13:33.925432: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1 2024-04-08 11:13:33.947575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0 coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s 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 2024-04-08 11:13:33.968875: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11 2024-04-08 11:13:33.968940: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11 2024-04-08 11:13:33.972012: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10 2024-04-08 11:13:33.972302: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10 2024-04-08 11:13:33.972899: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11 2024-04-08 11:13:33.973713: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11 2024-04-08 11:13:33.973880: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8 2024-04-08 11:13:33.976420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 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 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-04-08 11:13:33.986546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:9c:00.0 name: NVIDIA A100-PCIE-40GB computeCapability: 8.0 coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.44GiB deviceMemoryBandwidth: 1.41TiB/s 2024-04-08 11:13:33.989040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 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 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: 2024-04-08 11:13:34.622417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2024-04-08 11:13:34.622422: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: 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) 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) 2024-04-08 11:13:47.994803: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz
Epoch 1/280
2024-04-08 11:14:01.812069: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8 2024-04-08 11:14:02.937519: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8700 2024-04-08 11:14:03.573600: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11 2024-04-08 11:14:03.574110: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11 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.
15/15 [==============================] - 18s 101ms/step - loss: 6.0853 - val_loss: 6.0270 Epoch 2/280 15/15 [==============================] - 0s 13ms/step - loss: 6.0146 - val_loss: 5.9848 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 5.9665 - val_loss: 5.9331 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 5.9214 - val_loss: 5.8883 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 5.8715 - val_loss: 5.8414 Epoch 6/280 15/15 [==============================] - 0s 12ms/step - loss: 5.8291 - val_loss: 5.7992 Epoch 7/280 15/15 [==============================] - 0s 14ms/step - loss: 5.7840 - val_loss: 5.7525 Epoch 8/280 15/15 [==============================] - 0s 14ms/step - loss: 5.7362 - val_loss: 5.7064 Epoch 9/280 15/15 [==============================] - 0s 12ms/step - loss: 5.6912 - val_loss: 5.6601 Epoch 10/280 15/15 [==============================] - 0s 16ms/step - loss: 5.6494 - val_loss: 5.6172 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 5.6031 - val_loss: 5.5690 Epoch 12/280 15/15 [==============================] - 0s 12ms/step - loss: 5.5554 - val_loss: 5.5244 Epoch 13/280 15/15 [==============================] - 0s 12ms/step - loss: 5.5094 - val_loss: 5.4814 Epoch 14/280 15/15 [==============================] - 0s 12ms/step - loss: 5.4629 - val_loss: 5.4334 Epoch 15/280 15/15 [==============================] - 0s 13ms/step - loss: 5.4173 - val_loss: 5.3924 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 5.3758 - val_loss: 5.3440 Epoch 17/280 15/15 [==============================] - 0s 14ms/step - loss: 5.3243 - val_loss: 5.2960 Epoch 18/280 15/15 [==============================] - 0s 14ms/step - loss: 5.2835 - val_loss: 5.2535 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 5.2403 - val_loss: 5.2099 Epoch 20/280 15/15 [==============================] - 0s 12ms/step - loss: 5.1915 - val_loss: 5.1600 Epoch 21/280 15/15 [==============================] - 0s 13ms/step - loss: 5.1437 - val_loss: 5.1163 Epoch 22/280 15/15 [==============================] - 0s 13ms/step - loss: 5.1032 - val_loss: 5.0668 Epoch 23/280 15/15 [==============================] - 0s 14ms/step - loss: 5.0552 - val_loss: 5.0426 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 5.0285 - val_loss: 4.9953 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 4.9751 - val_loss: 4.9410 Epoch 26/280 15/15 [==============================] - 0s 14ms/step - loss: 4.9293 - val_loss: 4.9016 Epoch 27/280 15/15 [==============================] - 0s 14ms/step - loss: 4.8772 - val_loss: 4.8457 Epoch 28/280 15/15 [==============================] - 0s 12ms/step - loss: 4.8378 - val_loss: 4.8094 Epoch 29/280 15/15 [==============================] - 0s 13ms/step - loss: 4.7859 - val_loss: 4.7582 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 4.7452 - val_loss: 4.7266 Epoch 31/280 15/15 [==============================] - 0s 14ms/step - loss: 4.7049 - val_loss: 4.6671 Epoch 32/280 15/15 [==============================] - 0s 14ms/step - loss: 4.6538 - val_loss: 4.6273 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 4.6117 - val_loss: 4.5850 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 4.5648 - val_loss: 4.5436 Epoch 35/280 15/15 [==============================] - 0s 14ms/step - loss: 4.5190 - val_loss: 4.4914 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 4.4741 - val_loss: 4.4457 Epoch 37/280 15/15 [==============================] - 0s 12ms/step - loss: 4.4274 - val_loss: 4.4000 Epoch 38/280 15/15 [==============================] - 0s 13ms/step - loss: 4.3819 - val_loss: 4.3606 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 4.3497 - val_loss: 4.3305 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 4.3177 - val_loss: 4.3048 Epoch 41/280 15/15 [==============================] - 0s 14ms/step - loss: 4.2876 - val_loss: 4.2689 Epoch 42/280 15/15 [==============================] - 0s 14ms/step - loss: 4.2592 - val_loss: 4.2384 Epoch 43/280 15/15 [==============================] - 0s 12ms/step - loss: 4.2289 - val_loss: 4.2159 Epoch 44/280 15/15 [==============================] - 0s 15ms/step - loss: 4.1975 - val_loss: 4.1815 Epoch 45/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1707 - val_loss: 4.1547 Epoch 46/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1543 - val_loss: 4.1404 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1142 - val_loss: 4.1112 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0801 - val_loss: 4.0668 Epoch 49/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0530 - val_loss: 4.0336 Epoch 50/280 15/15 [==============================] - 0s 12ms/step - loss: 4.0184 - val_loss: 4.0059 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 3.9920 - val_loss: 3.9744 Epoch 52/280 15/15 [==============================] - 0s 14ms/step - loss: 3.9605 - val_loss: 3.9486 Epoch 53/280 15/15 [==============================] - 0s 12ms/step - loss: 3.9278 - val_loss: 3.9156 Epoch 54/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8994 - val_loss: 3.8840 Epoch 55/280 15/15 [==============================] - 0s 14ms/step - loss: 3.8660 - val_loss: 3.8531 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8381 - val_loss: 3.8223 Epoch 57/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8065 - val_loss: 3.7922 Epoch 58/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7815 - val_loss: 3.7619 Epoch 59/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7489 - val_loss: 3.7333 Epoch 60/280 15/15 [==============================] - 0s 14ms/step - loss: 3.7183 - val_loss: 3.7022 Epoch 61/280 15/15 [==============================] - 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0s 16ms/step - loss: 1.3040 - val_loss: 1.2859 Epoch 142/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2730 - val_loss: 1.2550 Epoch 143/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2509 - val_loss: 1.2333 Epoch 144/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2160 - val_loss: 1.1922 Epoch 145/280 15/15 [==============================] - 0s 11ms/step - loss: 1.1840 - val_loss: 1.1680 Epoch 146/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1514 - val_loss: 1.1352 Epoch 147/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1227 - val_loss: 1.1014 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0908 - val_loss: 1.0708 Epoch 149/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0584 - val_loss: 1.0415 Epoch 150/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0349 - val_loss: 1.0432 Epoch 151/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0144 - val_loss: 0.9994 Epoch 152/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9710 - val_loss: 0.9684 Epoch 153/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9420 - val_loss: 0.9297 Epoch 154/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9124 - val_loss: 0.9023 Epoch 155/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8858 - val_loss: 0.8702 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8488 - val_loss: 0.8434 Epoch 157/280 15/15 [==============================] - 0s 11ms/step - loss: 0.8263 - val_loss: 0.8078 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7939 - val_loss: 0.7735 Epoch 159/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7592 - val_loss: 0.7428 Epoch 160/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7293 - val_loss: 0.7199 Epoch 161/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7086 - val_loss: 0.6938 Epoch 162/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6708 - val_loss: 0.6575 Epoch 163/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6533 - val_loss: 0.6233 Epoch 164/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6125 - val_loss: 0.5980 Epoch 165/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5825 - val_loss: 0.5767 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5598 Epoch 167/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5533 - val_loss: 0.5545 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5384 - val_loss: 0.5321 Epoch 169/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5180 - val_loss: 0.5248 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5119 - val_loss: 0.5090 Epoch 171/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4882 - val_loss: 0.5010 Epoch 172/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4835 - val_loss: 0.4774 Epoch 173/280 15/15 [==============================] - 0s 11ms/step - loss: 0.4757 - val_loss: 0.4660 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4536 - val_loss: 0.4500 Epoch 175/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4341 - val_loss: 0.4243 Epoch 176/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4202 - val_loss: 0.4098 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4045 - val_loss: 0.3962 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3846 - val_loss: 0.3807 Epoch 179/280 15/15 [==============================] - 0s 11ms/step - loss: 0.3694 - val_loss: 0.3647 Epoch 180/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3567 - val_loss: 0.3471 Epoch 181/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3420 - val_loss: 0.3336 Epoch 182/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3252 - val_loss: 0.3194 Epoch 183/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3148 - val_loss: 0.3046 Epoch 184/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2958 - val_loss: 0.2945 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2874 - val_loss: 0.2774 Epoch 186/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2675 - val_loss: 0.2683 Epoch 187/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2531 - val_loss: 0.2504 Epoch 188/280 15/15 [==============================] - 0s 15ms/step - loss: 0.2370 - val_loss: 0.2337 Epoch 189/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2288 - val_loss: 0.2140 Epoch 190/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2054 - val_loss: 0.2039 Epoch 191/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1923 - val_loss: 0.1865 Epoch 192/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1747 - val_loss: 0.1732 Epoch 193/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1657 - val_loss: 0.1587 Epoch 194/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1380 - val_loss: 0.1423 Epoch 195/280 15/15 [==============================] - 0s 15ms/step - loss: 0.1289 - val_loss: 0.1306 Epoch 196/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1186 - val_loss: 0.1169 Epoch 197/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0992 - val_loss: 0.1038 Epoch 198/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0894 - val_loss: 0.0899 Epoch 199/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0703 - val_loss: 0.0739 Epoch 200/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0535 - val_loss: 0.0572 Epoch 201/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0530 - val_loss: 0.0662 Epoch 202/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0280 - val_loss: 0.0423 Epoch 203/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0344 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0353 Epoch 205/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0419 Epoch 206/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0343 Epoch 207/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0331 Epoch 208/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0255 - val_loss: 0.0392 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0245 - val_loss: 0.0384 Epoch 210/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0255 - val_loss: 0.0319 Epoch 211/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0230 - val_loss: 0.0292 Epoch 212/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0255 - val_loss: 0.0305 Epoch 213/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0235 - val_loss: 0.0340 Epoch 214/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0225 - val_loss: 0.0325 Epoch 215/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0210 - val_loss: 0.0295 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0353 Epoch 217/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0366 Epoch 218/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0321 Epoch 219/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0327 Epoch 220/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0379 Epoch 221/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0216 - val_loss: 0.0369 Epoch 222/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0206 - val_loss: 0.0367 Epoch 223/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0264 - val_loss: 0.0361 Epoch 224/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0352 Epoch 225/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0349 Epoch 226/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0348 Epoch 227/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0348 Epoch 228/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0349 Epoch 229/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0350 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0343 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0342 Epoch 232/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0341 Epoch 233/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0339 Epoch 234/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0338 Epoch 235/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0136 - val_loss: 0.0336 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0336 Epoch 237/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0151 - val_loss: 0.0337 Epoch 238/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0164 - val_loss: 0.0338 Epoch 239/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0339 Epoch 240/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0125 - val_loss: 0.0340 Epoch 241/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0342 Epoch 242/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0342 Epoch 243/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0342 Epoch 244/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0127 - val_loss: 0.0342 Epoch 245/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0156 - val_loss: 0.0342 Epoch 246/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0342 Epoch 247/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0139 - val_loss: 0.0341 Epoch 248/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0172 - val_loss: 0.0341 Epoch 249/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0198 - val_loss: 0.0341 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341 Epoch 251/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0162 - val_loss: 0.0341 Epoch 252/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0342 Epoch 253/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0143 - val_loss: 0.0342 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0157 - val_loss: 0.0342 Epoch 255/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0341 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0342 Epoch 257/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0341 Epoch 258/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0234 - val_loss: 0.0341 Epoch 259/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0341 Epoch 260/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0154 - val_loss: 0.0341 Epoch 261/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0209 - val_loss: 0.0341 Epoch 262/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0341 Epoch 263/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0341 Epoch 264/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0341 Epoch 265/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0134 - val_loss: 0.0341 Epoch 266/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0341 Epoch 267/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0129 - val_loss: 0.0341 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0341 Epoch 269/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0341 Epoch 270/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0193 - val_loss: 0.0341 Epoch 271/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0197 - val_loss: 0.0341 Epoch 272/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0152 - val_loss: 0.0341 Epoch 273/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0341 Epoch 274/280 15/15 [==============================] - 0s 10ms/step - loss: 0.0105 - val_loss: 0.0341 Epoch 275/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0147 - val_loss: 0.0341 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0125 - val_loss: 0.0341 Epoch 277/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0176 - val_loss: 0.0341 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0341 Epoch 279/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0341 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0341 COL: 比表面积, MSE: 1.32E-01,RMSE: 0.3629,MAPE: 3.6700000000000004 %,MAE: 0.2619,R_2: 0.2356 COL: 总孔体积, MSE: 7.52E-02,RMSE: 0.2742,MAPE: 27.810000000000002 %,MAE: 0.1978,R_2: 0.5771 COL: 微孔体积, MSE: 3.16E-02,RMSE: 0.1779,MAPE: 27.389999999999997 %,MAE: 0.1412,R_2: 0.3639 Epoch 1/280 15/15 [==============================] - 6s 93ms/step - loss: 1.8382 - val_loss: 1.6969 Epoch 2/280 15/15 [==============================] - 0s 11ms/step - loss: 1.6804 - val_loss: 1.6477 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6384 - val_loss: 1.6156 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6002 - val_loss: 1.5794 Epoch 5/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5757 - val_loss: 1.5500 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5482 - val_loss: 1.5252 Epoch 7/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5112 - val_loss: 1.4768 Epoch 8/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4720 - val_loss: 1.4442 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4436 - val_loss: 1.4154 Epoch 10/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4106 - val_loss: 1.3848 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3779 - val_loss: 1.3495 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3496 - val_loss: 1.3266 Epoch 13/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3157 - val_loss: 1.2910 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2770 - val_loss: 1.2666 Epoch 15/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2492 - val_loss: 1.2274 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2195 - val_loss: 1.1976 Epoch 17/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1825 - val_loss: 1.1668 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1551 - val_loss: 1.1331 Epoch 19/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1302 - val_loss: 1.1090 Epoch 20/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1015 - val_loss: 1.0747 Epoch 21/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0671 - val_loss: 1.0434 Epoch 22/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0422 - val_loss: 1.0214 Epoch 23/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0175 - val_loss: 0.9870 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9689 - val_loss: 0.9571 Epoch 25/280 15/15 [==============================] - 0s 16ms/step - loss: 0.9638 - val_loss: 0.9219 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9229 - val_loss: 0.8921 Epoch 27/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8851 - val_loss: 0.8686 Epoch 28/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8600 - val_loss: 0.8591 Epoch 29/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8503 - val_loss: 0.8390 Epoch 30/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8302 - val_loss: 0.8242 Epoch 31/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8137 - val_loss: 0.8139 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7981 - val_loss: 0.7926 Epoch 33/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7801 - val_loss: 0.7753 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7702 - val_loss: 0.7752 Epoch 35/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7537 - val_loss: 0.7512 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7407 - val_loss: 0.7492 Epoch 37/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7295 - val_loss: 0.7353 Epoch 38/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7083 - val_loss: 0.7060 Epoch 39/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6915 - val_loss: 0.6916 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6770 - val_loss: 0.6797 Epoch 41/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6640 - val_loss: 0.6657 Epoch 42/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6501 - val_loss: 0.6506 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6339 - val_loss: 0.6335 Epoch 44/280 15/15 [==============================] - 0s 11ms/step - loss: 0.6152 - val_loss: 0.6280 Epoch 45/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6174 - val_loss: 0.6014 Epoch 46/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5801 - val_loss: 0.5853 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5731 - val_loss: 0.5724 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5564 - val_loss: 0.5611 Epoch 49/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5450 - val_loss: 0.5549 Epoch 50/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5287 - val_loss: 0.5268 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5126 - val_loss: 0.5114 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5044 - val_loss: 0.4975 Epoch 53/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4773 - val_loss: 0.4839 Epoch 54/280 15/15 [==============================] - 0s 11ms/step - loss: 0.4700 - val_loss: 0.4683 Epoch 55/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4451 - val_loss: 0.4576 Epoch 56/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4326 - val_loss: 0.4369 Epoch 57/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4194 - val_loss: 0.4308 Epoch 58/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4123 - val_loss: 0.4054 Epoch 59/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3949 - val_loss: 0.4015 Epoch 60/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3771 - val_loss: 0.3864 Epoch 61/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3633 - val_loss: 0.3728 Epoch 62/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3432 - val_loss: 0.3576 Epoch 63/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3261 - val_loss: 0.3386 Epoch 64/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3135 - val_loss: 0.3321 Epoch 65/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3005 - val_loss: 0.3138 Epoch 66/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2829 - val_loss: 0.2991 Epoch 67/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2684 - val_loss: 0.2838 Epoch 68/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2604 - val_loss: 0.2645 Epoch 69/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2434 - val_loss: 0.2493 Epoch 70/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2266 - val_loss: 0.2490 Epoch 71/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2219 - val_loss: 0.2254 Epoch 72/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1932 - val_loss: 0.2080 Epoch 73/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.1971 Epoch 74/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1630 - val_loss: 0.1745 Epoch 75/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1646 - val_loss: 0.1629 Epoch 76/280 15/15 [==============================] - 0s 16ms/step - loss: 0.1398 - val_loss: 0.1502 Epoch 77/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1169 - val_loss: 0.1299 Epoch 78/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1055 - val_loss: 0.1099 Epoch 79/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0943 - val_loss: 0.0993 Epoch 80/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0771 - val_loss: 0.0771 Epoch 81/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0663 - val_loss: 0.0752 Epoch 82/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0607 Epoch 83/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0305 - val_loss: 0.0504 Epoch 84/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0391 - val_loss: 0.0555 Epoch 85/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0289 - val_loss: 0.0447 Epoch 86/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0503 Epoch 87/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0326 - val_loss: 0.0529 Epoch 88/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0402 - val_loss: 0.0522 Epoch 89/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0297 - val_loss: 0.0481 Epoch 90/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0336 - val_loss: 0.0483 Epoch 91/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0504 Epoch 92/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0344 - val_loss: 0.0517 Epoch 93/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0526 Epoch 94/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0534 Epoch 95/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0334 - val_loss: 0.0468 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0389 - val_loss: 0.0455 Epoch 97/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0284 - val_loss: 0.0468 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0488 Epoch 99/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0493 Epoch 100/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0494 Epoch 101/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0492 Epoch 102/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0484 Epoch 103/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0183 - val_loss: 0.0486 Epoch 104/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0498 Epoch 105/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499 Epoch 106/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0500 Epoch 107/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0222 - val_loss: 0.0499 Epoch 108/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0292 - val_loss: 0.0498 Epoch 109/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0498 Epoch 110/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499 Epoch 111/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0500 Epoch 112/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0298 - val_loss: 0.0500 Epoch 113/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0323 - val_loss: 0.0499 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0500 Epoch 115/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0245 - val_loss: 0.0499 Epoch 116/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0499 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0499 Epoch 118/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0499 Epoch 119/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0499 Epoch 120/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0270 - val_loss: 0.0499 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0499 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0499 Epoch 123/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0499 Epoch 124/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499 Epoch 125/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0499 Epoch 126/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0327 - val_loss: 0.0499 Epoch 127/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0328 - val_loss: 0.0499 Epoch 128/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0499 Epoch 129/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0301 - val_loss: 0.0499 Epoch 130/280 15/15 [==============================] - 0s 17ms/step - loss: 0.0311 - val_loss: 0.0499 Epoch 131/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0202 - val_loss: 0.0499 Epoch 132/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0499 Epoch 133/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0499 Epoch 134/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0499 Epoch 135/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0499 Epoch 136/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0312 - val_loss: 0.0499 Epoch 137/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0258 - val_loss: 0.0499 Epoch 138/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0499 Epoch 139/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0223 - val_loss: 0.0499 Epoch 140/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0243 - val_loss: 0.0499 Epoch 141/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0214 - val_loss: 0.0499 Epoch 142/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499 Epoch 143/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0251 - val_loss: 0.0499 Epoch 144/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0221 - val_loss: 0.0499 Epoch 145/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0294 - val_loss: 0.0499 Epoch 146/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0243 - val_loss: 0.0499 Epoch 147/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0499 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499 Epoch 149/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0499 Epoch 150/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0499 Epoch 151/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0207 - val_loss: 0.0499 Epoch 152/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0241 - val_loss: 0.0499 Epoch 153/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0192 - val_loss: 0.0499 Epoch 154/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499 Epoch 155/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0295 - val_loss: 0.0499 Epoch 156/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0499 Epoch 157/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0499 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0499 Epoch 159/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0213 - val_loss: 0.0499 Epoch 160/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0246 - val_loss: 0.0499 Epoch 161/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499 Epoch 162/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0499 Epoch 163/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0242 - val_loss: 0.0499 Epoch 164/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0499 Epoch 165/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0277 - val_loss: 0.0499 Epoch 166/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0177 - val_loss: 0.0499 Epoch 167/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0499 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499 Epoch 169/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0499 Epoch 170/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0499 Epoch 171/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0191 - val_loss: 0.0499 Epoch 172/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0266 - val_loss: 0.0499 Epoch 173/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0499 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0499 Epoch 175/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0499 Epoch 176/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0256 - val_loss: 0.0499 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0499 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0499 Epoch 179/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0271 - val_loss: 0.0499 Epoch 180/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0233 - val_loss: 0.0499 Epoch 181/280 15/15 [==============================] - 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0s 14ms/step - loss: 0.0203 - val_loss: 0.0499 Epoch 262/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499 Epoch 263/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0271 - val_loss: 0.0499 Epoch 264/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0499 Epoch 265/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0499 Epoch 266/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0499 Epoch 267/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0499 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0499 Epoch 269/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0287 - val_loss: 0.0499 Epoch 270/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0499 Epoch 271/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0499 Epoch 272/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0232 - val_loss: 0.0499 Epoch 273/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0179 - val_loss: 0.0499 Epoch 274/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0298 - val_loss: 0.0499 Epoch 275/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0499 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0499 Epoch 277/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0300 - val_loss: 0.0499 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0499 Epoch 279/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0168 - val_loss: 0.0499 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0499 COL: 比表面积, MSE: 1.83E-01,RMSE: 0.4274,MAPE: 3.84 %,MAE: 0.2752,R_2: 0.4599 COL: 总孔体积, MSE: 1.35E-01,RMSE: 0.368,MAPE: 29.43 %,MAE: 0.251,R_2: 0.582 COL: 微孔体积, MSE: 1.75E-01,RMSE: 0.4187,MAPE: 32.84 %,MAE: 0.2536,R_2: 0.2184 Epoch 1/280 15/15 [==============================] - 6s 86ms/step - loss: 2.5093 - val_loss: 2.2921 Epoch 2/280 15/15 [==============================] - 0s 12ms/step - loss: 2.2438 - val_loss: 2.2513 Epoch 3/280 15/15 [==============================] - 0s 12ms/step - loss: 2.2354 - val_loss: 2.1929 Epoch 4/280 15/15 [==============================] - 0s 15ms/step - loss: 2.1850 - val_loss: 2.2179 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 2.1398 - val_loss: 2.1617 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 2.1034 - val_loss: 2.1344 Epoch 7/280 15/15 [==============================] - 0s 14ms/step - loss: 2.1028 - val_loss: 2.0470 Epoch 8/280 15/15 [==============================] - 0s 14ms/step - loss: 2.0489 - val_loss: 2.0199 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0085 - val_loss: 2.0242 Epoch 10/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9942 - val_loss: 1.9674 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9434 - val_loss: 1.9257 Epoch 12/280 15/15 [==============================] - 0s 14ms/step - loss: 1.9492 - val_loss: 1.8951 Epoch 13/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9382 - val_loss: 1.8804 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9204 - val_loss: 1.8696 Epoch 15/280 15/15 [==============================] - 0s 12ms/step - loss: 1.8749 - val_loss: 1.8513 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8301 - val_loss: 1.7955 Epoch 17/280 15/15 [==============================] - 0s 12ms/step - loss: 1.8124 - val_loss: 1.7826 Epoch 18/280 15/15 [==============================] - 0s 14ms/step - loss: 1.8018 - val_loss: 1.7692 Epoch 19/280 15/15 [==============================] - 0s 14ms/step - loss: 1.7991 - val_loss: 1.7415 Epoch 20/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7584 - val_loss: 1.7389 Epoch 21/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7315 - val_loss: 1.7086 Epoch 22/280 15/15 [==============================] - 0s 14ms/step - loss: 1.7082 - val_loss: 1.6742 Epoch 23/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6990 - val_loss: 1.7330 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7054 - val_loss: 1.6404 Epoch 25/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6263 - val_loss: 1.6385 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6006 - val_loss: 1.6112 Epoch 27/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5858 - val_loss: 1.5762 Epoch 28/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.6333 Epoch 29/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5803 - val_loss: 1.5680 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5278 - val_loss: 1.5337 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5164 - val_loss: 1.5049 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5023 - val_loss: 1.4911 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4772 - val_loss: 1.4716 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4323 - val_loss: 1.4509 Epoch 35/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4409 - val_loss: 1.4576 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4506 - val_loss: 1.4298 Epoch 37/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4344 - val_loss: 1.4233 Epoch 38/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3917 - val_loss: 1.3796 Epoch 39/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3798 - val_loss: 1.3587 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3458 - val_loss: 1.3256 Epoch 41/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3357 - val_loss: 1.3232 Epoch 42/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2982 - val_loss: 1.2909 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2903 - val_loss: 1.2806 Epoch 44/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2797 - val_loss: 1.2819 Epoch 45/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2652 - val_loss: 1.2418 Epoch 46/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2416 - val_loss: 1.2181 Epoch 47/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1992 - val_loss: 1.1981 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1843 - val_loss: 1.1742 Epoch 49/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1733 - val_loss: 1.1714 Epoch 50/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1635 - val_loss: 1.1432 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1295 - val_loss: 1.1217 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1078 - val_loss: 1.0999 Epoch 53/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0884 - val_loss: 1.0842 Epoch 54/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0660 - val_loss: 1.0670 Epoch 55/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0480 - val_loss: 1.0427 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0226 - val_loss: 1.0279 Epoch 57/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0134 - val_loss: 1.0064 Epoch 58/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9996 - val_loss: 0.9869 Epoch 59/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9771 - val_loss: 0.9727 Epoch 60/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9675 - val_loss: 0.9556 Epoch 61/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9433 - val_loss: 0.9389 Epoch 62/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9366 - val_loss: 0.9140 Epoch 63/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9133 - val_loss: 0.9077 Epoch 64/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8954 - val_loss: 0.8816 Epoch 65/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8708 - val_loss: 0.8689 Epoch 66/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8601 - val_loss: 0.8506 Epoch 67/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8415 - val_loss: 0.8326 Epoch 68/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8209 - val_loss: 0.8159 Epoch 69/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8046 - val_loss: 0.8116 Epoch 70/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7990 - val_loss: 0.7809 Epoch 71/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7824 - val_loss: 0.7674 Epoch 72/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7634 - val_loss: 0.7477 Epoch 73/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7432 - val_loss: 0.7355 Epoch 74/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7313 - val_loss: 0.7165 Epoch 75/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7137 - val_loss: 0.6973 Epoch 76/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6910 - val_loss: 0.6775 Epoch 77/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6671 - val_loss: 0.6567 Epoch 78/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6503 - val_loss: 0.6431 Epoch 79/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6419 - val_loss: 0.6281 Epoch 80/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6325 - val_loss: 0.6109 Epoch 81/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6073 - val_loss: 0.5969 Epoch 82/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5921 - val_loss: 0.5814 Epoch 83/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5766 - val_loss: 0.5767 Epoch 84/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5650 - val_loss: 0.5570 Epoch 85/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5517 - val_loss: 0.5424 Epoch 86/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5356 - val_loss: 0.5201 Epoch 87/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5284 - val_loss: 0.5093 Epoch 88/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4967 - val_loss: 0.4913 Epoch 89/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4909 - val_loss: 0.4805 Epoch 90/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4778 - val_loss: 0.4694 Epoch 91/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4641 - val_loss: 0.4434 Epoch 92/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4429 - val_loss: 0.4431 Epoch 93/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4261 - val_loss: 0.4184 Epoch 94/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4118 - val_loss: 0.4070 Epoch 95/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4017 - val_loss: 0.3975 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3843 - val_loss: 0.3721 Epoch 97/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3733 - val_loss: 0.3661 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3501 - val_loss: 0.3501 Epoch 99/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3431 - val_loss: 0.3297 Epoch 100/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3337 - val_loss: 0.3141 Epoch 101/280 15/15 [==============================] - 0s 15ms/step - loss: 0.3057 - val_loss: 0.3023 Epoch 102/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2870 - val_loss: 0.3007 Epoch 103/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2794 - val_loss: 0.2787 Epoch 104/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2690 - val_loss: 0.2698 Epoch 105/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2539 - val_loss: 0.2383 Epoch 106/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2290 - val_loss: 0.2458 Epoch 107/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2229 - val_loss: 0.2211 Epoch 108/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2076 - val_loss: 0.1950 Epoch 109/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1842 - val_loss: 0.1891 Epoch 110/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1702 - val_loss: 0.1710 Epoch 111/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1541 - val_loss: 0.1655 Epoch 112/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1383 - val_loss: 0.1385 Epoch 113/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1276 - val_loss: 0.1214 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1092 - val_loss: 0.1268 Epoch 115/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1004 - val_loss: 0.1230 Epoch 116/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0888 - val_loss: 0.0899 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0756 - val_loss: 0.0718 Epoch 118/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0530 - val_loss: 0.0633 Epoch 119/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0413 - val_loss: 0.0487 Epoch 120/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0276 - val_loss: 0.0440 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0337 - val_loss: 0.0697 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0481 Epoch 123/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0335 - val_loss: 0.0503 Epoch 124/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0281 - val_loss: 0.0584 Epoch 125/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0400 - val_loss: 0.0570 Epoch 126/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0367 - val_loss: 0.0713 Epoch 127/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0343 - val_loss: 0.0378 Epoch 128/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0355 - val_loss: 0.0768 Epoch 129/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0532 Epoch 130/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0310 - val_loss: 0.0867 Epoch 131/280 15/15 [==============================] - 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0s 13ms/step - loss: 0.0228 - val_loss: 0.0527 Epoch 242/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0527 Epoch 243/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0527 Epoch 244/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0527 Epoch 245/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0213 - val_loss: 0.0527 Epoch 246/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0527 Epoch 247/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0527 Epoch 248/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0527 Epoch 249/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0527 Epoch 250/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0527 Epoch 251/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0527 Epoch 252/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0527 Epoch 253/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0158 - val_loss: 0.0527 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0302 - val_loss: 0.0527 Epoch 255/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0206 - val_loss: 0.0527 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0527 Epoch 257/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0194 - val_loss: 0.0527 Epoch 258/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0527 Epoch 259/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0527 Epoch 260/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0527 Epoch 261/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527 Epoch 262/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0527 Epoch 263/280 15/15 [==============================] - 0s 17ms/step - loss: 0.0200 - val_loss: 0.0527 Epoch 264/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0527 Epoch 265/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0190 - val_loss: 0.0527 Epoch 266/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0217 - val_loss: 0.0527 Epoch 267/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0527 Epoch 268/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0527 Epoch 269/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0263 - val_loss: 0.0527 Epoch 270/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0527 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0527 Epoch 272/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0306 - val_loss: 0.0527 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0527 Epoch 274/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0210 - val_loss: 0.0527 Epoch 275/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0527 Epoch 276/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0527 Epoch 277/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0251 - val_loss: 0.0527 Epoch 278/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0527 Epoch 279/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0527 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0527 COL: 比表面积, MSE: 1.91E-01,RMSE: 0.4372,MAPE: 4.16 %,MAE: 0.289,R_2: 0.5448 COL: 总孔体积, MSE: 8.81E-02,RMSE: 0.2969,MAPE: 25.869999999999997 %,MAE: 0.178,R_2: 0.5039 COL: 微孔体积, MSE: 2.92E-02,RMSE: 0.1709,MAPE: 31.97 %,MAE: 0.1435,R_2: 0.6463 Epoch 1/280 15/15 [==============================] - 6s 87ms/step - loss: 5.1081 - val_loss: 4.9102 Epoch 2/280 15/15 [==============================] - 0s 13ms/step - loss: 4.8975 - val_loss: 4.9026 Epoch 3/280 15/15 [==============================] - 0s 12ms/step - loss: 4.8714 - val_loss: 4.8965 Epoch 4/280 15/15 [==============================] - 0s 14ms/step - loss: 4.8855 - val_loss: 4.8015 Epoch 5/280 15/15 [==============================] - 0s 14ms/step - loss: 4.7490 - val_loss: 4.8035 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 4.7785 - val_loss: 4.7613 Epoch 7/280 15/15 [==============================] - 0s 13ms/step - loss: 4.7197 - val_loss: 4.7055 Epoch 8/280 15/15 [==============================] - 0s 12ms/step - loss: 4.7034 - val_loss: 4.6356 Epoch 9/280 15/15 [==============================] - 0s 12ms/step - loss: 4.6134 - val_loss: 4.6040 Epoch 10/280 15/15 [==============================] - 0s 12ms/step - loss: 4.5380 - val_loss: 4.5892 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 4.5485 - val_loss: 4.5479 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 4.4834 - val_loss: 4.5093 Epoch 13/280 15/15 [==============================] - 0s 12ms/step - loss: 4.4568 - val_loss: 4.5328 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 4.4267 - val_loss: 4.4700 Epoch 15/280 15/15 [==============================] - 0s 14ms/step - loss: 4.4915 - val_loss: 4.3953 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 4.4139 - val_loss: 4.3778 Epoch 17/280 15/15 [==============================] - 0s 13ms/step - loss: 4.3356 - val_loss: 4.3524 Epoch 18/280 15/15 [==============================] - 0s 14ms/step - loss: 4.2885 - val_loss: 4.3275 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 4.2311 - val_loss: 4.2670 Epoch 20/280 15/15 [==============================] - 0s 14ms/step - loss: 4.1935 - val_loss: 4.2596 Epoch 21/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1738 - val_loss: 4.1836 Epoch 22/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1310 - val_loss: 4.1656 Epoch 23/280 15/15 [==============================] - 0s 12ms/step - loss: 4.1215 - val_loss: 4.2036 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0710 - val_loss: 4.1054 Epoch 25/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0426 - val_loss: 4.0336 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 3.9955 - val_loss: 4.0605 Epoch 27/280 15/15 [==============================] - 0s 14ms/step - loss: 3.9581 - val_loss: 3.9995 Epoch 28/280 15/15 [==============================] - 0s 13ms/step - loss: 3.9062 - val_loss: 3.9595 Epoch 29/280 15/15 [==============================] - 0s 14ms/step - loss: 3.9252 - val_loss: 3.9659 Epoch 30/280 15/15 [==============================] - 0s 14ms/step - loss: 3.8707 - val_loss: 3.8585 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8102 - val_loss: 3.8593 Epoch 32/280 15/15 [==============================] - 0s 14ms/step - loss: 3.7872 - val_loss: 3.7926 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8039 - val_loss: 3.8059 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7222 - val_loss: 3.7581 Epoch 35/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7013 - val_loss: 3.7203 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6839 - val_loss: 3.6715 Epoch 37/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6679 Epoch 38/280 15/15 [==============================] - 0s 13ms/step - loss: 3.5974 - val_loss: 3.6234 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 3.5549 - val_loss: 3.6106 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 3.5219 - val_loss: 3.5373 Epoch 41/280 15/15 [==============================] - 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0s 13ms/step - loss: 3.1809 - val_loss: 3.1922 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 3.1383 - val_loss: 3.1311 Epoch 53/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0813 - val_loss: 3.1212 Epoch 54/280 15/15 [==============================] - 0s 14ms/step - loss: 3.0824 - val_loss: 3.0604 Epoch 55/280 15/15 [==============================] - 0s 17ms/step - loss: 3.0412 - val_loss: 3.0347 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0030 - val_loss: 3.0090 Epoch 57/280 15/15 [==============================] - 0s 13ms/step - loss: 2.9850 - val_loss: 2.9901 Epoch 58/280 15/15 [==============================] - 0s 13ms/step - loss: 2.9486 - val_loss: 2.9865 Epoch 59/280 15/15 [==============================] - 0s 14ms/step - loss: 2.9330 - val_loss: 2.9117 Epoch 60/280 15/15 [==============================] - 0s 13ms/step - loss: 2.8916 - val_loss: 2.8976 Epoch 61/280 15/15 [==============================] - 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0s 14ms/step - loss: 1.9129 - val_loss: 1.8740 Epoch 92/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8763 - val_loss: 1.8452 Epoch 93/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8298 - val_loss: 1.8235 Epoch 94/280 15/15 [==============================] - 0s 11ms/step - loss: 1.7952 - val_loss: 1.7809 Epoch 95/280 15/15 [==============================] - 0s 12ms/step - loss: 1.7741 - val_loss: 1.7611 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7307 - val_loss: 1.7357 Epoch 97/280 15/15 [==============================] - 0s 12ms/step - loss: 1.7168 - val_loss: 1.7038 Epoch 98/280 15/15 [==============================] - 0s 15ms/step - loss: 1.6875 - val_loss: 1.6567 Epoch 99/280 15/15 [==============================] - 0s 14ms/step - loss: 1.6440 - val_loss: 1.6213 Epoch 100/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6178 - val_loss: 1.5933 Epoch 101/280 15/15 [==============================] - 0s 12ms/step - loss: 1.5765 - val_loss: 1.5616 Epoch 102/280 15/15 [==============================] - 0s 12ms/step - loss: 1.5462 - val_loss: 1.5271 Epoch 103/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5152 - val_loss: 1.4967 Epoch 104/280 15/15 [==============================] - 0s 11ms/step - loss: 1.4833 - val_loss: 1.4654 Epoch 105/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4444 - val_loss: 1.4358 Epoch 106/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4152 - val_loss: 1.4042 Epoch 107/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3893 - val_loss: 1.3689 Epoch 108/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3534 - val_loss: 1.3381 Epoch 109/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3189 - val_loss: 1.3094 Epoch 110/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2893 - val_loss: 1.2775 Epoch 111/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2663 - val_loss: 1.2617 Epoch 112/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2497 - val_loss: 1.2412 Epoch 113/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2280 - val_loss: 1.2307 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2131 - val_loss: 1.2178 Epoch 115/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1963 - val_loss: 1.2042 Epoch 116/280 15/15 [==============================] - 0s 15ms/step - loss: 1.1870 - val_loss: 1.1858 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1782 - val_loss: 1.1721 Epoch 118/280 15/15 [==============================] - 0s 11ms/step - loss: 1.1521 - val_loss: 1.1573 Epoch 119/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1387 - val_loss: 1.1391 Epoch 120/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1245 - val_loss: 1.1252 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1089 - val_loss: 1.1109 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0948 - val_loss: 1.0973 Epoch 123/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0807 - val_loss: 1.0832 Epoch 124/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0651 - val_loss: 1.0653 Epoch 125/280 15/15 [==============================] - 0s 16ms/step - loss: 1.0550 - val_loss: 1.0520 Epoch 126/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0281 - val_loss: 1.0342 Epoch 127/280 15/15 [==============================] - 0s 11ms/step - loss: 1.0174 - val_loss: 1.0233 Epoch 128/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0004 - val_loss: 1.0092 Epoch 129/280 15/15 [==============================] - 0s 15ms/step - loss: 0.9905 - val_loss: 0.9870 Epoch 130/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9755 - val_loss: 0.9740 Epoch 131/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9609 - val_loss: 0.9636 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9390 - val_loss: 0.9474 Epoch 133/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9285 - val_loss: 0.9306 Epoch 134/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9186 - val_loss: 0.9154 Epoch 135/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8964 - val_loss: 0.9008 Epoch 136/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8825 - val_loss: 0.8857 Epoch 137/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8665 - val_loss: 0.8731 Epoch 138/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8525 - val_loss: 0.8578 Epoch 139/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8374 - val_loss: 0.8368 Epoch 140/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8190 - val_loss: 0.8262 Epoch 141/280 15/15 [==============================] - 0s 16ms/step - loss: 0.8082 - val_loss: 0.8144 Epoch 142/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7900 - val_loss: 0.7967 Epoch 143/280 15/15 [==============================] - 0s 11ms/step - loss: 0.7780 - val_loss: 0.7780 Epoch 144/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7609 - val_loss: 0.7624 Epoch 145/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7445 - val_loss: 0.7526 Epoch 146/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7292 - val_loss: 0.7464 Epoch 147/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7150 - val_loss: 0.7205 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7076 - val_loss: 0.7118 Epoch 149/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6928 - val_loss: 0.6972 Epoch 150/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.6844 Epoch 151/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6611 - val_loss: 0.6654 Epoch 152/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6423 - val_loss: 0.6477 Epoch 153/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6313 - val_loss: 0.6356 Epoch 154/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6113 - val_loss: 0.6156 Epoch 155/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5967 - val_loss: 0.6031 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5812 - val_loss: 0.5875 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5702 - val_loss: 0.5728 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5558 - val_loss: 0.5591 Epoch 159/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5361 - val_loss: 0.5422 Epoch 160/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5235 - val_loss: 0.5270 Epoch 161/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5084 - val_loss: 0.5119 Epoch 162/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4930 - val_loss: 0.4952 Epoch 163/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4763 - val_loss: 0.4804 Epoch 164/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4594 - val_loss: 0.4669 Epoch 165/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4478 - val_loss: 0.4494 Epoch 166/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4318 - val_loss: 0.4370 Epoch 167/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4136 - val_loss: 0.4334 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4057 - val_loss: 0.4085 Epoch 169/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3844 - val_loss: 0.3921 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3693 - val_loss: 0.3802 Epoch 171/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3657 - val_loss: 0.3635 Epoch 172/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3474 - val_loss: 0.3528 Epoch 173/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3396 - val_loss: 0.3365 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3189 - val_loss: 0.3179 Epoch 175/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3041 - val_loss: 0.3039 Epoch 176/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2797 - val_loss: 0.2888 Epoch 177/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2678 - val_loss: 0.2714 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2664 - val_loss: 0.2648 Epoch 179/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2430 - val_loss: 0.2442 Epoch 180/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2275 - val_loss: 0.2299 Epoch 181/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2174 - val_loss: 0.2133 Epoch 182/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1952 - val_loss: 0.2043 Epoch 183/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1787 - val_loss: 0.1842 Epoch 184/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1641 - val_loss: 0.1706 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1502 - val_loss: 0.1541 Epoch 186/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1363 - val_loss: 0.1366 Epoch 187/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1143 - val_loss: 0.1261 Epoch 188/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1053 - val_loss: 0.1179 Epoch 189/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0897 - val_loss: 0.0969 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0726 - val_loss: 0.0757 Epoch 191/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0586 - val_loss: 0.0688 Epoch 192/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0413 - val_loss: 0.0489 Epoch 193/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0226 - val_loss: 0.0385 Epoch 194/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0374 Epoch 195/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0381 Epoch 196/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0407 Epoch 197/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0204 - val_loss: 0.0405 Epoch 198/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0247 - val_loss: 0.0350 Epoch 199/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0282 - val_loss: 0.0399 Epoch 200/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0259 - val_loss: 0.0431 Epoch 201/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0397 Epoch 202/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0377 Epoch 203/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0354 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0355 Epoch 205/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0168 - val_loss: 0.0316 Epoch 206/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0378 Epoch 207/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0337 Epoch 208/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0377 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0264 - val_loss: 0.0426 Epoch 210/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0451 Epoch 211/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0412 Epoch 212/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0425 Epoch 213/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0359 Epoch 214/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0429 Epoch 215/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0378 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0386 Epoch 217/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0387 Epoch 218/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0384 Epoch 219/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0378 Epoch 220/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0224 - val_loss: 0.0372 Epoch 221/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0166 - val_loss: 0.0369 Epoch 222/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0373 Epoch 223/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0160 - val_loss: 0.0372 Epoch 224/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0374 Epoch 225/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0148 - val_loss: 0.0366 Epoch 226/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0366 Epoch 227/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0367 Epoch 228/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0368 Epoch 229/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0368 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0141 - val_loss: 0.0368 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0366 Epoch 232/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0364 Epoch 233/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0112 - val_loss: 0.0363 Epoch 234/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0163 - val_loss: 0.0363 Epoch 235/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0252 - val_loss: 0.0364 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0364 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0364 Epoch 238/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0364 Epoch 239/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0364 Epoch 240/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0237 - val_loss: 0.0364 Epoch 241/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365 Epoch 242/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0147 - val_loss: 0.0365 Epoch 243/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0365 Epoch 244/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0170 - val_loss: 0.0365 Epoch 245/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0189 - val_loss: 0.0365 Epoch 246/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0365 Epoch 247/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0365 Epoch 248/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0365 Epoch 249/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0365 Epoch 250/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0187 - val_loss: 0.0365 Epoch 251/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0365 Epoch 252/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0137 - val_loss: 0.0365 Epoch 253/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0113 - val_loss: 0.0365 Epoch 255/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0109 - val_loss: 0.0365 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365 Epoch 257/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0124 - val_loss: 0.0365 Epoch 258/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0168 - val_loss: 0.0365 Epoch 259/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0123 - val_loss: 0.0365 Epoch 260/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0365 Epoch 261/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0173 - val_loss: 0.0365 Epoch 262/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0136 - val_loss: 0.0365 Epoch 263/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0206 - val_loss: 0.0365 Epoch 264/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0170 - val_loss: 0.0365 Epoch 265/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0136 - val_loss: 0.0365 Epoch 266/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0135 - val_loss: 0.0365 Epoch 267/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0365 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0365 Epoch 269/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0186 - val_loss: 0.0365 Epoch 270/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0365 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0365 Epoch 272/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0365 Epoch 273/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0149 - val_loss: 0.0365 Epoch 274/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0365 Epoch 275/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0142 - val_loss: 0.0365 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0365 Epoch 277/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0138 - val_loss: 0.0365 Epoch 278/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0150 - val_loss: 0.0365 Epoch 279/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0365 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0365 COL: 比表面积, MSE: 2.48E-01,RMSE: 0.4978,MAPE: 4.0 %,MAE: 0.302,R_2: 0.2379 COL: 总孔体积, MSE: 3.02E-01,RMSE: 0.5491,MAPE: 28.02 %,MAE: 0.3058,R_2: 0.1327 COL: 微孔体积, MSE: 2.86E-02,RMSE: 0.169,MAPE: 28.199999999999996 %,MAE: 0.119,R_2: 0.7352 Epoch 1/280 15/15 [==============================] - 6s 92ms/step - loss: 3.8534 - val_loss: 3.7567 Epoch 2/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6472 - val_loss: 3.6221 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4827 - val_loss: 3.5718 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4439 - val_loss: 3.7812 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4902 - val_loss: 3.4990 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4199 - val_loss: 3.5033 Epoch 7/280 15/15 [==============================] - 0s 14ms/step - loss: 3.3520 - val_loss: 3.4131 Epoch 8/280 15/15 [==============================] - 0s 13ms/step - loss: 3.2274 - val_loss: 3.4744 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 3.3141 - val_loss: 3.3652 Epoch 10/280 15/15 [==============================] - 0s 14ms/step - loss: 3.2422 - val_loss: 3.3192 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 3.1901 - val_loss: 3.2861 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 3.1546 - val_loss: 3.2805 Epoch 13/280 15/15 [==============================] - 0s 8ms/step - loss: 3.1568 - val_loss: 3.1997 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0975 - val_loss: 3.1395 Epoch 15/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0909 - val_loss: 3.1347 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0395 - val_loss: 3.0548 Epoch 17/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0171 - val_loss: 3.0526 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 2.8964 - val_loss: 3.0192 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 2.9154 - val_loss: 3.0198 Epoch 20/280 15/15 [==============================] - 0s 13ms/step - loss: 2.8078 - val_loss: 2.9941 Epoch 21/280 15/15 [==============================] - 0s 13ms/step - loss: 2.7513 - val_loss: 2.9358 Epoch 22/280 15/15 [==============================] - 0s 14ms/step - loss: 2.8136 - val_loss: 2.8785 Epoch 23/280 15/15 [==============================] - 0s 13ms/step - loss: 2.8047 - val_loss: 2.8693 Epoch 24/280 15/15 [==============================] - 0s 12ms/step - loss: 2.7416 - val_loss: 2.8484 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 2.6788 - val_loss: 2.8676 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 2.6403 - val_loss: 2.7848 Epoch 27/280 15/15 [==============================] - 0s 16ms/step - loss: 2.5648 - val_loss: 2.6941 Epoch 28/280 15/15 [==============================] - 0s 12ms/step - loss: 2.5826 - val_loss: 2.6609 Epoch 29/280 15/15 [==============================] - 0s 13ms/step - loss: 2.6405 - val_loss: 2.6603 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 2.4518 - val_loss: 2.5825 Epoch 31/280 15/15 [==============================] - 0s 14ms/step - loss: 2.4556 - val_loss: 2.5640 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 2.3909 - val_loss: 2.5145 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 2.4314 - val_loss: 2.5188 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 2.3933 - val_loss: 2.4724 Epoch 35/280 15/15 [==============================] - 0s 14ms/step - loss: 2.3968 - val_loss: 2.4456 Epoch 36/280 15/15 [==============================] - 0s 14ms/step - loss: 2.2659 - val_loss: 2.3750 Epoch 37/280 15/15 [==============================] - 0s 12ms/step - loss: 2.3021 - val_loss: 2.3642 Epoch 38/280 15/15 [==============================] - 0s 15ms/step - loss: 2.2431 - val_loss: 2.3579 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 2.2053 - val_loss: 2.2751 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 2.2433 - val_loss: 2.2323 Epoch 41/280 15/15 [==============================] - 0s 12ms/step - loss: 2.0799 - val_loss: 2.2380 Epoch 42/280 15/15 [==============================] - 0s 12ms/step - loss: 2.1074 - val_loss: 2.2114 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0792 - val_loss: 2.1425 Epoch 44/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0379 - val_loss: 2.0959 Epoch 45/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0600 - val_loss: 2.0612 Epoch 46/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9412 - val_loss: 2.0333 Epoch 47/280 15/15 [==============================] - 0s 12ms/step - loss: 1.9411 - val_loss: 2.0240 Epoch 48/280 15/15 [==============================] - 0s 15ms/step - loss: 1.9220 - val_loss: 1.9449 Epoch 49/280 15/15 [==============================] - 0s 14ms/step - loss: 1.8270 - val_loss: 1.9371 Epoch 50/280 15/15 [==============================] - 0s 14ms/step - loss: 1.8160 - val_loss: 1.9008 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7859 - val_loss: 1.9029 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8180 - val_loss: 1.8543 Epoch 53/280 15/15 [==============================] - 0s 12ms/step - loss: 1.6860 - val_loss: 1.8061 Epoch 54/280 15/15 [==============================] - 0s 12ms/step - loss: 1.6871 - val_loss: 1.7867 Epoch 55/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6814 - val_loss: 1.7409 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6678 - val_loss: 1.7037 Epoch 57/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5697 - val_loss: 1.6629 Epoch 58/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5739 - val_loss: 1.6251 Epoch 59/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5689 - val_loss: 1.5888 Epoch 60/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5003 - val_loss: 1.5611 Epoch 61/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4374 - val_loss: 1.5485 Epoch 62/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4609 - val_loss: 1.5217 Epoch 63/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3757 - val_loss: 1.4869 Epoch 64/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3607 - val_loss: 1.4766 Epoch 65/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3568 - val_loss: 1.4298 Epoch 66/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3402 - val_loss: 1.4201 Epoch 67/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2598 - val_loss: 1.3883 Epoch 68/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2835 - val_loss: 1.4037 Epoch 69/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3104 - val_loss: 1.3474 Epoch 70/280 15/15 [==============================] - 0s 11ms/step - loss: 1.2975 - val_loss: 1.3322 Epoch 71/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2389 - val_loss: 1.3066 Epoch 72/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2247 - val_loss: 1.2726 Epoch 73/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2057 - val_loss: 1.2699 Epoch 74/280 15/15 [==============================] - 0s 16ms/step - loss: 1.1751 - val_loss: 1.2541 Epoch 75/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1926 - val_loss: 1.2535 Epoch 76/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1240 - val_loss: 1.2125 Epoch 77/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1475 - val_loss: 1.1964 Epoch 78/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1142 - val_loss: 1.1723 Epoch 79/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0919 - val_loss: 1.1463 Epoch 80/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0487 - val_loss: 1.1511 Epoch 81/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0812 - val_loss: 1.1103 Epoch 82/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0616 - val_loss: 1.1049 Epoch 83/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0060 - val_loss: 1.0763 Epoch 84/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9815 - val_loss: 1.0593 Epoch 85/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9701 - val_loss: 1.0384 Epoch 86/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0118 - val_loss: 1.0158 Epoch 87/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9694 - val_loss: 0.9976 Epoch 88/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8923 - val_loss: 0.9689 Epoch 89/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8620 - val_loss: 0.9573 Epoch 90/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8262 - val_loss: 0.9274 Epoch 91/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8643 - val_loss: 0.9043 Epoch 92/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7931 - val_loss: 0.8905 Epoch 93/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7984 - val_loss: 0.8760 Epoch 94/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7855 - val_loss: 0.8485 Epoch 95/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7722 - val_loss: 0.8250 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7325 - val_loss: 0.8051 Epoch 97/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7136 - val_loss: 0.7794 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6683 - val_loss: 0.7647 Epoch 99/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7094 - val_loss: 0.7334 Epoch 100/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6586 - val_loss: 0.7116 Epoch 101/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6167 - val_loss: 0.6856 Epoch 102/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6134 - val_loss: 0.6801 Epoch 103/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6155 - val_loss: 0.6491 Epoch 104/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5715 - val_loss: 0.6357 Epoch 105/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5260 - val_loss: 0.6052 Epoch 106/280 15/15 [==============================] - 0s 16ms/step - loss: 0.5269 - val_loss: 0.5999 Epoch 107/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5412 - val_loss: 0.5573 Epoch 108/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5057 - val_loss: 0.5422 Epoch 109/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4933 - val_loss: 0.5037 Epoch 110/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4462 - val_loss: 0.4962 Epoch 111/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4479 - val_loss: 0.4797 Epoch 112/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4036 - val_loss: 0.4708 Epoch 113/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4004 - val_loss: 0.4384 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3423 - val_loss: 0.4223 Epoch 115/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3605 - val_loss: 0.3956 Epoch 116/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3069 - val_loss: 0.3667 Epoch 117/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3148 - val_loss: 0.3675 Epoch 118/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3581 - val_loss: 0.3360 Epoch 119/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2954 - val_loss: 0.2939 Epoch 120/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2562 - val_loss: 0.2863 Epoch 121/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2189 - val_loss: 0.2752 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1909 - val_loss: 0.2655 Epoch 123/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2091 - val_loss: 0.2517 Epoch 124/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1874 - val_loss: 0.2449 Epoch 125/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2034 - val_loss: 0.2362 Epoch 126/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1790 - val_loss: 0.2391 Epoch 127/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1783 - val_loss: 0.2272 Epoch 128/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1812 - val_loss: 0.2234 Epoch 129/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1731 - val_loss: 0.2219 Epoch 130/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1789 - val_loss: 0.2142 Epoch 131/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1547 - val_loss: 0.2143 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1717 - val_loss: 0.1890 Epoch 133/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1656 - val_loss: 0.1934 Epoch 134/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1378 - val_loss: 0.1874 Epoch 135/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1595 - val_loss: 0.1803 Epoch 136/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1520 - val_loss: 0.1690 Epoch 137/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1372 - val_loss: 0.1707 Epoch 138/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1398 - val_loss: 0.1716 Epoch 139/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1318 - val_loss: 0.1650 Epoch 140/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1382 - val_loss: 0.1633 Epoch 141/280 15/15 [==============================] - 0s 15ms/step - loss: 0.1195 - val_loss: 0.1586 Epoch 142/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1226 - val_loss: 0.1533 Epoch 143/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1163 - val_loss: 0.1529 Epoch 144/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1119 - val_loss: 0.1457 Epoch 145/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1016 - val_loss: 0.1407 Epoch 146/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1107 - val_loss: 0.1242 Epoch 147/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1032 - val_loss: 0.1262 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0993 - val_loss: 0.1167 Epoch 149/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0960 - val_loss: 0.1096 Epoch 150/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0914 - val_loss: 0.1052 Epoch 151/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1001 - val_loss: 0.1168 Epoch 152/280 15/15 [==============================] - 0s 15ms/step - loss: 0.1094 - val_loss: 0.1104 Epoch 153/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1035 - val_loss: 0.0997 Epoch 154/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1001 - val_loss: 0.0935 Epoch 155/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0838 - val_loss: 0.0931 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0794 - val_loss: 0.0897 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0782 - val_loss: 0.0855 Epoch 158/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0685 - val_loss: 0.0867 Epoch 159/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0778 - val_loss: 0.0852 Epoch 160/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0702 - val_loss: 0.0724 Epoch 161/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0626 - val_loss: 0.0764 Epoch 162/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0671 - val_loss: 0.0755 Epoch 163/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0522 - val_loss: 0.0720 Epoch 164/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0624 - val_loss: 0.0724 Epoch 165/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0587 - val_loss: 0.0723 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0588 - val_loss: 0.0745 Epoch 167/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0559 - val_loss: 0.0627 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0501 - val_loss: 0.0605 Epoch 169/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0459 - val_loss: 0.0581 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0480 - val_loss: 0.0530 Epoch 171/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0467 - val_loss: 0.0543 Epoch 172/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0437 - val_loss: 0.0489 Epoch 173/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0441 - val_loss: 0.0496 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0452 - val_loss: 0.0614 Epoch 175/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0494 - val_loss: 0.0520 Epoch 176/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0361 - val_loss: 0.0529 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0324 - val_loss: 0.0539 Epoch 178/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0284 - val_loss: 0.0513 Epoch 179/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0346 - val_loss: 0.0491 Epoch 180/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0309 - val_loss: 0.0530 Epoch 181/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0473 Epoch 182/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0479 Epoch 183/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0412 - val_loss: 0.0524 Epoch 184/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0367 - val_loss: 0.0464 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0316 - val_loss: 0.0448 Epoch 186/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0311 - val_loss: 0.0472 Epoch 187/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0261 - val_loss: 0.0464 Epoch 188/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0293 - val_loss: 0.0466 Epoch 189/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0493 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0475 Epoch 191/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0446 Epoch 192/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0341 - val_loss: 0.0459 Epoch 193/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0325 - val_loss: 0.0448 Epoch 194/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0351 - val_loss: 0.0464 Epoch 195/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0269 - val_loss: 0.0424 Epoch 196/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0315 - val_loss: 0.0465 Epoch 197/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0274 - val_loss: 0.0448 Epoch 198/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0428 Epoch 199/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0313 - val_loss: 0.0495 Epoch 200/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0304 - val_loss: 0.0429 Epoch 201/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0332 - val_loss: 0.0407 Epoch 202/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0270 - val_loss: 0.0431 Epoch 203/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0314 - val_loss: 0.0446 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0421 Epoch 205/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0318 - val_loss: 0.0435 Epoch 206/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0471 Epoch 207/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0309 - val_loss: 0.0431 Epoch 208/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0367 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0233 - val_loss: 0.0426 Epoch 210/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0265 - val_loss: 0.0549 Epoch 211/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0372 - val_loss: 0.0536 Epoch 212/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0376 - val_loss: 0.0454 Epoch 213/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0472 Epoch 214/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0424 Epoch 215/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0386 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0353 - val_loss: 0.0409 Epoch 217/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0335 - val_loss: 0.0425 Epoch 218/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0253 - val_loss: 0.0384 Epoch 219/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0238 - val_loss: 0.0379 Epoch 220/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0278 - val_loss: 0.0385 Epoch 221/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0204 - val_loss: 0.0389 Epoch 222/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0386 Epoch 223/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0260 - val_loss: 0.0388 Epoch 224/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0219 - val_loss: 0.0376 Epoch 225/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0382 Epoch 226/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0313 - val_loss: 0.0377 Epoch 227/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0376 Epoch 228/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0369 Epoch 229/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0368 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0368 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0368 Epoch 232/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0368 Epoch 233/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0191 - val_loss: 0.0368 Epoch 234/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0227 - val_loss: 0.0368 Epoch 235/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0369 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0369 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0248 - val_loss: 0.0369 Epoch 238/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0240 - val_loss: 0.0369 Epoch 239/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0369 Epoch 240/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0369 Epoch 241/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0206 - val_loss: 0.0369 Epoch 242/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0274 - val_loss: 0.0368 Epoch 243/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0368 Epoch 244/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0242 - val_loss: 0.0368 Epoch 245/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0250 - val_loss: 0.0368 Epoch 246/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0191 - val_loss: 0.0368 Epoch 247/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0368 Epoch 248/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0205 - val_loss: 0.0368 Epoch 249/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0368 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0368 Epoch 251/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0368 Epoch 252/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0203 - val_loss: 0.0368 Epoch 253/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0368 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0368 Epoch 255/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0368 Epoch 256/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0368 Epoch 257/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0229 - val_loss: 0.0368 Epoch 258/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0215 - val_loss: 0.0368 Epoch 259/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0368 Epoch 260/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0196 - val_loss: 0.0368 Epoch 261/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0258 - val_loss: 0.0368 Epoch 262/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0253 - val_loss: 0.0368 Epoch 263/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0189 - val_loss: 0.0368 Epoch 264/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0368 Epoch 265/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0368 Epoch 266/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0368 Epoch 267/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0368 Epoch 269/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0368 Epoch 270/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0368 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0368 Epoch 272/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0368 Epoch 273/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0171 - val_loss: 0.0368 Epoch 274/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0368 Epoch 275/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0368 Epoch 276/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0186 - val_loss: 0.0368 Epoch 277/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0236 - val_loss: 0.0368 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0368 Epoch 279/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0231 - val_loss: 0.0368 Epoch 280/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0368 WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.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. COL: 比表面积, MSE: 4.64E-02,RMSE: 0.2154,MAPE: 1.9900000000000002 %,MAE: 0.1412,R_2: 0.8076 COL: 总孔体积, MSE: 7.08E-02,RMSE: 0.2661,MAPE: 26.39 %,MAE: 0.2135,R_2: 0.7685 COL: 微孔体积, MSE: 6.68E-02,RMSE: 0.2585,MAPE: 32.42 %,MAE: 0.1907,R_2: 0.5484 Epoch 1/280 15/15 [==============================] - 6s 129ms/step - loss: 4.2350 - val_loss: 4.1776 Epoch 2/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1771 - val_loss: 4.1449 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 4.1223 - val_loss: 4.1071 Epoch 4/280 15/15 [==============================] - 0s 14ms/step - loss: 4.0915 - val_loss: 4.0765 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0640 - val_loss: 4.0501 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 4.0344 - val_loss: 4.0214 Epoch 7/280 15/15 [==============================] - 0s 13ms/step - loss: 3.9994 - val_loss: 3.9829 Epoch 8/280 15/15 [==============================] - 0s 12ms/step - loss: 3.9724 - val_loss: 3.9461 Epoch 9/280 15/15 [==============================] - 0s 14ms/step - loss: 3.9340 - val_loss: 3.9392 Epoch 10/280 15/15 [==============================] - 0s 13ms/step - loss: 3.9182 - val_loss: 3.8950 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8824 - val_loss: 3.8600 Epoch 12/280 15/15 [==============================] - 0s 12ms/step - loss: 3.8552 - val_loss: 3.8238 Epoch 13/280 15/15 [==============================] - 0s 13ms/step - loss: 3.8206 - val_loss: 3.8020 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7942 - val_loss: 3.7723 Epoch 15/280 15/15 [==============================] - 0s 14ms/step - loss: 3.7578 - val_loss: 3.7352 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 3.7251 - val_loss: 3.7036 Epoch 17/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6941 - val_loss: 3.6750 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6685 - val_loss: 3.6430 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6334 - val_loss: 3.6137 Epoch 20/280 15/15 [==============================] - 0s 13ms/step - loss: 3.6061 - val_loss: 3.5869 Epoch 21/280 15/15 [==============================] - 0s 14ms/step - loss: 3.5691 - val_loss: 3.5518 Epoch 22/280 15/15 [==============================] - 0s 12ms/step - loss: 3.5421 - val_loss: 3.5220 Epoch 23/280 15/15 [==============================] - 0s 13ms/step - loss: 3.5133 - val_loss: 3.4925 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4835 - val_loss: 3.4649 Epoch 25/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4501 - val_loss: 3.4398 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 3.4284 - val_loss: 3.4076 Epoch 27/280 15/15 [==============================] - 0s 15ms/step - loss: 3.3958 - val_loss: 3.3724 Epoch 28/280 15/15 [==============================] - 0s 14ms/step - loss: 3.3619 - val_loss: 3.3404 Epoch 29/280 15/15 [==============================] - 0s 13ms/step - loss: 3.3317 - val_loss: 3.3143 Epoch 30/280 15/15 [==============================] - 0s 14ms/step - loss: 3.3014 - val_loss: 3.2824 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 3.2694 - val_loss: 3.2526 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 3.2441 - val_loss: 3.2212 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 3.2127 - val_loss: 3.1935 Epoch 34/280 15/15 [==============================] - 0s 14ms/step - loss: 3.1830 - val_loss: 3.1647 Epoch 35/280 15/15 [==============================] - 0s 13ms/step - loss: 3.1553 - val_loss: 3.1322 Epoch 36/280 15/15 [==============================] - 0s 14ms/step - loss: 3.1277 - val_loss: 3.1093 Epoch 37/280 15/15 [==============================] - 0s 12ms/step - loss: 3.1034 - val_loss: 3.0807 Epoch 38/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0715 - val_loss: 3.0497 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0388 - val_loss: 3.0153 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 3.0073 - val_loss: 2.9840 Epoch 41/280 15/15 [==============================] - 0s 14ms/step - loss: 2.9731 - val_loss: 2.9659 Epoch 42/280 15/15 [==============================] - 0s 14ms/step - loss: 2.9443 - val_loss: 2.9278 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 2.9167 - val_loss: 2.8959 Epoch 44/280 15/15 [==============================] - 0s 14ms/step - loss: 2.8854 - val_loss: 2.8698 Epoch 45/280 15/15 [==============================] - 0s 14ms/step - loss: 2.8597 - val_loss: 2.8404 Epoch 46/280 15/15 [==============================] - 0s 14ms/step - loss: 2.8206 - val_loss: 2.8021 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 2.7924 - val_loss: 2.7780 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 2.7626 - val_loss: 2.7478 Epoch 49/280 15/15 [==============================] - 0s 13ms/step - loss: 2.7273 - val_loss: 2.7211 Epoch 50/280 15/15 [==============================] - 0s 13ms/step - loss: 2.7038 - val_loss: 2.6861 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 2.6716 - val_loss: 2.6589 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 2.6406 - val_loss: 2.6293 Epoch 53/280 15/15 [==============================] - 0s 13ms/step - loss: 2.6130 - val_loss: 2.5971 Epoch 54/280 15/15 [==============================] - 0s 14ms/step - loss: 2.5839 - val_loss: 2.5632 Epoch 55/280 15/15 [==============================] - 0s 12ms/step - loss: 2.5519 - val_loss: 2.5352 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 2.5227 - val_loss: 2.5058 Epoch 57/280 15/15 [==============================] - 0s 13ms/step - loss: 2.4997 - val_loss: 2.4834 Epoch 58/280 15/15 [==============================] - 0s 15ms/step - loss: 2.4671 - val_loss: 2.4525 Epoch 59/280 15/15 [==============================] - 0s 14ms/step - loss: 2.4349 - val_loss: 2.4193
In [ ]:
train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True) valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)
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prediction_model = get_prediction_model() trainable_model = get_trainable_model(prediction_model)
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prediction_model.summary()
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X = np.expand_dims(train[feature_cols].values, axis=1) Y = [x for x in train[out_cols].values.T] Y_valid = [x for x in valid[out_cols].values.T]
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X_valid = np.expand_dims(valid[feature_cols].values, axis=1)
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trainable_model.compile(optimizer='adam', loss=None) hist = trainable_model.fit([X, Y[0], Y[1], Y[2]], epochs=280, batch_size=8, verbose=1, validation_data=[X_valid, Y_valid[0], Y_valid[1], Y_valid[2]], callbacks=[reduce_lr] )
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rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))
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[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]
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pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)
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real_rst = test[out_cols].copy()
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for col in out_cols: pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col] real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]
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pred_rst['比表面积'] = np.expm1(pred_rst['比表面积']) real_rst['比表面积'] = np.expm1(real_rst['比表面积'])
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real_rst.columns
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y_pred_pm25 = pred_rst['比表面积'].values.reshape(-1,) y_pred_pm10 = pred_rst['总孔体积'].values.reshape(-1,) y_pred_so2 = pred_rst['微孔体积'].values.reshape(-1,) y_true_pm25 = real_rst['比表面积'].values.reshape(-1,) y_true_pm10 = real_rst['总孔体积'].values.reshape(-1,) y_true_so2 = real_rst['微孔体积'].values.reshape(-1,)
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pm25_eva = print_eva(y_true_pm25, y_pred_pm25, tp='比表面积') pm10_eva = print_eva(y_true_pm10, y_pred_pm10, tp='总孔体积') so2_eva = print_eva(y_true_so2, y_pred_so2, tp='微孔体积')
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