422 KiB
422 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 Epoch 60/280 15/15 [==============================] - 0s 13ms/step - loss: 2.4018 - val_loss: 2.3864 Epoch 61/280 15/15 [==============================] - 0s 13ms/step - loss: 2.3721 - val_loss: 2.3595 Epoch 62/280 15/15 [==============================] - 0s 13ms/step - loss: 2.3478 - val_loss: 2.3276 Epoch 63/280 15/15 [==============================] - 0s 12ms/step - loss: 2.3097 - val_loss: 2.2973 Epoch 64/280 15/15 [==============================] - 0s 14ms/step - loss: 2.2812 - val_loss: 2.2712 Epoch 65/280 15/15 [==============================] - 0s 13ms/step - loss: 2.2538 - val_loss: 2.2394 Epoch 66/280 15/15 [==============================] - 0s 13ms/step - loss: 2.2251 - val_loss: 2.2097 Epoch 67/280 15/15 [==============================] - 0s 12ms/step - loss: 2.1940 - val_loss: 2.1783 Epoch 68/280 15/15 [==============================] - 0s 13ms/step - loss: 2.1662 - val_loss: 2.1537 Epoch 69/280 15/15 [==============================] - 0s 14ms/step - loss: 2.1373 - val_loss: 2.1187 Epoch 70/280 15/15 [==============================] - 0s 13ms/step - loss: 2.1071 - val_loss: 2.0918 Epoch 71/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0699 - val_loss: 2.0571 Epoch 72/280 15/15 [==============================] - 0s 14ms/step - loss: 2.0393 - val_loss: 2.0277 Epoch 73/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0126 - val_loss: 1.9989 Epoch 74/280 15/15 [==============================] - 0s 14ms/step - loss: 1.9778 - val_loss: 1.9675 Epoch 75/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9482 - val_loss: 1.9420 Epoch 76/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9198 - val_loss: 1.9099 Epoch 77/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8941 - val_loss: 1.8831 Epoch 78/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8612 - val_loss: 1.8509 Epoch 79/280 15/15 [==============================] - 0s 15ms/step - loss: 1.8312 - val_loss: 1.8193 Epoch 80/280 15/15 [==============================] - 0s 15ms/step - loss: 1.7970 - val_loss: 1.7916 Epoch 81/280 15/15 [==============================] - 0s 14ms/step - loss: 1.7704 - val_loss: 1.7650 Epoch 82/280 15/15 [==============================] - 0s 14ms/step - loss: 1.7411 - val_loss: 1.7316 Epoch 83/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7098 - val_loss: 1.7020 Epoch 84/280 15/15 [==============================] - 0s 14ms/step - loss: 1.6814 - val_loss: 1.6727 Epoch 85/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6875 - val_loss: 1.6626 Epoch 86/280 15/15 [==============================] - 0s 12ms/step - loss: 1.6399 - val_loss: 1.6227 Epoch 87/280 15/15 [==============================] - 0s 15ms/step - loss: 1.6033 - val_loss: 1.5946 Epoch 88/280 15/15 [==============================] - 0s 12ms/step - loss: 1.5851 - val_loss: 1.5840 Epoch 89/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5594 - val_loss: 1.5417 Epoch 90/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5161 - val_loss: 1.4966 Epoch 91/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4775 - val_loss: 1.4701 Epoch 92/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4455 - val_loss: 1.4386 Epoch 93/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4202 - val_loss: 1.4165 Epoch 94/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3911 - val_loss: 1.3943 Epoch 95/280 15/15 [==============================] - 0s 11ms/step - loss: 1.3817 - val_loss: 1.3952 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3680 - val_loss: 1.3710 Epoch 97/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3466 - val_loss: 1.3535 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3385 - val_loss: 1.3448 Epoch 99/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3236 - val_loss: 1.3263 Epoch 100/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3062 - val_loss: 1.3080 Epoch 101/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2936 - val_loss: 1.2946 Epoch 102/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2701 - val_loss: 1.2771 Epoch 103/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2579 - val_loss: 1.2622 Epoch 104/280 15/15 [==============================] - 0s 16ms/step - loss: 1.2446 - val_loss: 1.2502 Epoch 105/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2254 - val_loss: 1.2349 Epoch 106/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2156 - val_loss: 1.2179 Epoch 107/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1976 - val_loss: 1.2026 Epoch 108/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1824 - val_loss: 1.1901 Epoch 109/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1684 - val_loss: 1.1713 Epoch 110/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1515 - val_loss: 1.1574 Epoch 111/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1371 - val_loss: 1.1436 Epoch 112/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1210 - val_loss: 1.1278 Epoch 113/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1079 - val_loss: 1.1117 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0938 - val_loss: 1.0997 Epoch 115/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0749 - val_loss: 1.0823 Epoch 116/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0648 - val_loss: 1.0685 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0468 - val_loss: 1.0526 Epoch 118/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0304 - val_loss: 1.0357 Epoch 119/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0189 - val_loss: 1.0223 Epoch 120/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0012 - val_loss: 1.0106 Epoch 121/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9899 - val_loss: 0.9892 Epoch 122/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9714 - val_loss: 0.9827 Epoch 123/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9620 - val_loss: 0.9605 Epoch 124/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9377 - val_loss: 0.9512 Epoch 125/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9255 - val_loss: 0.9344 Epoch 126/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9109 - val_loss: 0.9205 Epoch 127/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8942 - val_loss: 0.9064 Epoch 128/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8785 - val_loss: 0.8916 Epoch 129/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8666 - val_loss: 0.8720 Epoch 130/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8547 - val_loss: 0.8645 Epoch 131/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8382 - val_loss: 0.8417 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8257 - val_loss: 0.8341 Epoch 133/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8092 - val_loss: 0.8199 Epoch 134/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7950 - val_loss: 0.7961 Epoch 135/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7771 - val_loss: 0.7816 Epoch 136/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7597 - val_loss: 0.7659 Epoch 137/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7482 - val_loss: 0.7576 Epoch 138/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7291 - val_loss: 0.7387 Epoch 139/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7124 - val_loss: 0.7247 Epoch 140/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7003 - val_loss: 0.7087 Epoch 141/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6847 - val_loss: 0.6923 Epoch 142/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6712 - val_loss: 0.6787 Epoch 143/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6533 - val_loss: 0.6627 Epoch 144/280 15/15 [==============================] - 0s 15ms/step - loss: 0.6407 - val_loss: 0.6493 Epoch 145/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6234 - val_loss: 0.6340 Epoch 146/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6140 - val_loss: 0.6221 Epoch 147/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5983 - val_loss: 0.6026 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5782 - val_loss: 0.5869 Epoch 149/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5630 - val_loss: 0.5720 Epoch 150/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5503 - val_loss: 0.5613 Epoch 151/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5378 - val_loss: 0.5463 Epoch 152/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5210 - val_loss: 0.5323 Epoch 153/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5066 - val_loss: 0.5201 Epoch 154/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4905 - val_loss: 0.5045 Epoch 155/280 15/15 [==============================] - 0s 11ms/step - loss: 0.4781 - val_loss: 0.4834 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4675 - val_loss: 0.4931 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4599 - val_loss: 0.4624 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4379 - val_loss: 0.4451 Epoch 159/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4309 - val_loss: 0.4277 Epoch 160/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4098 - val_loss: 0.4075 Epoch 161/280 15/15 [==============================] - 0s 16ms/step - loss: 0.3912 - val_loss: 0.4006 Epoch 162/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3752 - val_loss: 0.3833 Epoch 163/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3610 - val_loss: 0.3686 Epoch 164/280 15/15 [==============================] - 0s 11ms/step - loss: 0.3427 - val_loss: 0.3496 Epoch 165/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3290 - val_loss: 0.3453 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3412 - val_loss: 0.3276 Epoch 167/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3087 - val_loss: 0.3055 Epoch 168/280 15/15 [==============================] - 0s 16ms/step - loss: 0.2839 - val_loss: 0.2904 Epoch 169/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2721 - val_loss: 0.2758 Epoch 170/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2592 - val_loss: 0.2670 Epoch 171/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2400 - val_loss: 0.2452 Epoch 172/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2272 - val_loss: 0.2401 Epoch 173/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2209 - val_loss: 0.2176 Epoch 174/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1969 - val_loss: 0.2003 Epoch 175/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1867 - val_loss: 0.1853 Epoch 176/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1629 - val_loss: 0.1700 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1446 - val_loss: 0.1559 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1336 - val_loss: 0.1466 Epoch 179/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1169 - val_loss: 0.1309 Epoch 180/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1060 - val_loss: 0.1130 Epoch 181/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0892 - val_loss: 0.0973 Epoch 182/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0694 - val_loss: 0.0806 Epoch 183/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0578 - val_loss: 0.0643 Epoch 184/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0404 - val_loss: 0.0528 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0270 - val_loss: 0.0374 Epoch 186/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0134 - val_loss: 0.0347 Epoch 187/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0132 - val_loss: 0.0325 Epoch 188/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0187 - val_loss: 0.0332 Epoch 189/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0343 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0133 - val_loss: 0.0459 Epoch 191/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0305 - val_loss: 0.0773 Epoch 192/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0612 Epoch 193/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0294 - val_loss: 0.0348 Epoch 194/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0497 Epoch 195/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0454 Epoch 196/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0371 - val_loss: 0.0446 Epoch 197/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0415 Epoch 198/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0225 - val_loss: 0.0405 Epoch 199/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0390 Epoch 200/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0367 Epoch 201/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0162 - val_loss: 0.0360 Epoch 202/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0129 - val_loss: 0.0349 Epoch 203/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0343 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0135 - val_loss: 0.0340 Epoch 205/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0131 - val_loss: 0.0343 Epoch 206/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0342 Epoch 207/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0340 Epoch 208/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0137 - val_loss: 0.0340 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0119 - val_loss: 0.0340 Epoch 210/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0340 Epoch 211/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0110 - val_loss: 0.0340 Epoch 212/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0130 - val_loss: 0.0340 Epoch 213/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0127 - val_loss: 0.0339 Epoch 214/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0339 Epoch 215/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0154 - val_loss: 0.0338 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0338 Epoch 217/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0338 Epoch 218/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0113 - val_loss: 0.0338 Epoch 219/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0141 - val_loss: 0.0338 Epoch 220/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0338 Epoch 221/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0337 Epoch 222/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 223/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0112 - val_loss: 0.0337 Epoch 224/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0145 - val_loss: 0.0337 Epoch 225/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0119 - val_loss: 0.0337 Epoch 226/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0190 - val_loss: 0.0337 Epoch 227/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0103 - val_loss: 0.0337 Epoch 228/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0337 Epoch 229/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0117 - val_loss: 0.0337 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0144 - val_loss: 0.0337 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0337 Epoch 232/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0111 - val_loss: 0.0337 Epoch 233/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0337 Epoch 234/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0337 Epoch 235/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0148 - val_loss: 0.0337 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0337 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0337 Epoch 238/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 239/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0141 - val_loss: 0.0337 Epoch 240/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0111 - val_loss: 0.0337 Epoch 241/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337 Epoch 242/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0121 - val_loss: 0.0337 Epoch 243/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0120 - val_loss: 0.0337 Epoch 244/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0118 - val_loss: 0.0337 Epoch 245/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0101 - val_loss: 0.0337 Epoch 246/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0337 Epoch 247/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0337 Epoch 248/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0111 - val_loss: 0.0337 Epoch 249/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0111 - val_loss: 0.0337 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 251/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0126 - val_loss: 0.0337 Epoch 252/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337 Epoch 253/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0121 - val_loss: 0.0337 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0113 - val_loss: 0.0337 Epoch 255/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0118 - val_loss: 0.0337 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0116 - val_loss: 0.0337 Epoch 257/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0133 - val_loss: 0.0337 Epoch 258/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0114 - val_loss: 0.0337 Epoch 259/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0149 - val_loss: 0.0337 Epoch 260/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0120 - val_loss: 0.0337 Epoch 261/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0107 - val_loss: 0.0337 Epoch 262/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0118 - val_loss: 0.0337 Epoch 263/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0135 - val_loss: 0.0337 Epoch 264/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0337 Epoch 265/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0160 - val_loss: 0.0337 Epoch 266/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0105 - val_loss: 0.0337 Epoch 267/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0144 - val_loss: 0.0337 Epoch 268/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 269/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0337 Epoch 270/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0105 - val_loss: 0.0337 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0337 Epoch 272/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0337 Epoch 274/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0141 - val_loss: 0.0337 Epoch 275/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0116 - val_loss: 0.0337 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0337 Epoch 277/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0114 - val_loss: 0.0337 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0337 Epoch 279/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0151 - val_loss: 0.0337 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0337 WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f925b0ad5f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details. COL: 比表面积, MSE: 5.17E-01,RMSE: 0.7191,MAPE: 6.8500000000000005 %,MAE: 0.4346,R_2: -0.097 COL: 总孔体积, MSE: 4.80E-02,RMSE: 0.2191,MAPE: 26.93 %,MAE: 0.1605,R_2: 0.7029 COL: 微孔体积, MSE: 2.84E-02,RMSE: 0.1685,MAPE: 25.85 %,MAE: 0.1176,R_2: -0.2583 Epoch 1/280 15/15 [==============================] - 6s 93ms/step - loss: 2.8437 - val_loss: 2.7256 Epoch 2/280 15/15 [==============================] - 0s 14ms/step - loss: 2.2702 - val_loss: 2.2922 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 2.1285 - val_loss: 2.2275 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0685 - val_loss: 2.2036 Epoch 5/280 15/15 [==============================] - 0s 14ms/step - loss: 2.1385 - val_loss: 2.1282 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 2.2805 - val_loss: 2.0756 Epoch 7/280 15/15 [==============================] - 0s 13ms/step - loss: 2.0905 - val_loss: 2.0699 Epoch 8/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9892 - val_loss: 1.9419 Epoch 9/280 15/15 [==============================] - 0s 14ms/step - loss: 1.9954 - val_loss: 1.9155 Epoch 10/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9310 - val_loss: 1.9522 Epoch 11/280 15/15 [==============================] - 0s 15ms/step - loss: 1.8701 - val_loss: 1.7135 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7483 - val_loss: 1.7967 Epoch 13/280 15/15 [==============================] - 0s 12ms/step - loss: 1.8521 - val_loss: 1.7321 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 1.9484 - val_loss: 1.6460 Epoch 15/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6651 - val_loss: 1.5944 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7173 - val_loss: 1.7414 Epoch 17/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7019 - val_loss: 1.6067 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5441 - val_loss: 1.4960 Epoch 19/280 15/15 [==============================] - 0s 11ms/step - loss: 1.6313 - val_loss: 1.5536 Epoch 20/280 15/15 [==============================] - 0s 15ms/step - loss: 1.5156 - val_loss: 1.5032 Epoch 21/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3779 - val_loss: 1.4505 Epoch 22/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3311 - val_loss: 1.5608 Epoch 23/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3291 - val_loss: 1.4962 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6878 - val_loss: 1.5311 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 1.4834 - val_loss: 1.6844 Epoch 26/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5190 - val_loss: 1.4949 Epoch 27/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4538 - val_loss: 1.5663 Epoch 28/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3375 - val_loss: 1.4548 Epoch 29/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2507 - val_loss: 1.3878 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3738 - val_loss: 1.5167 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4214 - val_loss: 1.3964 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3014 - val_loss: 1.3950 Epoch 33/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4419 - val_loss: 1.4415 Epoch 34/280 15/15 [==============================] - 0s 11ms/step - loss: 1.3593 - val_loss: 1.5711 Epoch 35/280 15/15 [==============================] - 0s 12ms/step - loss: 1.5495 - val_loss: 1.3657 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4482 - val_loss: 1.3499 Epoch 37/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1884 - val_loss: 1.3848 Epoch 38/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2024 - val_loss: 1.3474 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2285 - val_loss: 1.3377 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1912 - val_loss: 1.2759 Epoch 41/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1634 - val_loss: 1.2572 Epoch 42/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1176 - val_loss: 1.2016 Epoch 43/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1759 - val_loss: 1.2444 Epoch 44/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2093 - val_loss: 1.1987 Epoch 45/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1634 - val_loss: 1.2866 Epoch 46/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1392 - val_loss: 1.2245 Epoch 47/280 15/15 [==============================] - 0s 15ms/step - loss: 1.1493 - val_loss: 1.1568 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - 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0s 13ms/step - loss: 0.8230 - val_loss: 0.8792 Epoch 269/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7199 - val_loss: 0.8792 Epoch 270/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7807 - val_loss: 0.8792 Epoch 271/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6880 - val_loss: 0.8792 Epoch 272/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7277 - val_loss: 0.8792 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6574 - val_loss: 0.8792 Epoch 274/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7361 - val_loss: 0.8792 Epoch 275/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7203 - val_loss: 0.8792 Epoch 276/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6350 - val_loss: 0.8792 Epoch 277/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6775 - val_loss: 0.8792 Epoch 278/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8293 - val_loss: 0.8792 Epoch 279/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6803 - val_loss: 0.8792 Epoch 280/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8161 - val_loss: 0.8792 COL: 比表面积, MSE: 3.15E+00,RMSE: 1.7749,MAPE: 23.9 %,MAE: 1.745,R_2: -9.6787 COL: 总孔体积, MSE: 2.57E+00,RMSE: 1.6022,MAPE: 267.31 %,MAE: 1.5898,R_2: -15.1375 COL: 微孔体积, MSE: 9.74E-01,RMSE: 0.9872,MAPE: 211.20000000000002 %,MAE: 0.9771,R_2: -7.2983 Epoch 1/280 15/15 [==============================] - 6s 87ms/step - loss: 1.8668 - val_loss: 1.1992 Epoch 2/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4484 - val_loss: 1.4379 Epoch 3/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2950 - val_loss: 1.2383 Epoch 4/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3274 - val_loss: 1.3438 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4247 - val_loss: 1.1749 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3966 - val_loss: 1.1068 Epoch 7/280 15/15 [==============================] - 0s 14ms/step - loss: 1.2165 - val_loss: 1.1996 Epoch 8/280 15/15 [==============================] - 0s 13ms/step - loss: 1.3044 - val_loss: 1.0921 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1830 - val_loss: 1.0342 Epoch 10/280 15/15 [==============================] - 0s 12ms/step - loss: 1.1450 - val_loss: 1.1071 Epoch 11/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0652 - val_loss: 0.8831 Epoch 12/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0268 - val_loss: 0.8447 Epoch 13/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9613 - val_loss: 1.0174 Epoch 14/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0751 - val_loss: 0.8795 Epoch 15/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9335 - val_loss: 0.8544 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8992 - val_loss: 0.8532 Epoch 17/280 15/15 [==============================] - 0s 14ms/step - loss: 1.0178 - val_loss: 0.9018 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0000 - val_loss: 0.7768 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9628 - val_loss: 0.7968 Epoch 20/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8164 - val_loss: 0.8871 Epoch 21/280 15/15 [==============================] - 0s 15ms/step - loss: 0.9036 - val_loss: 0.7119 Epoch 22/280 15/15 [==============================] - 0s 15ms/step - loss: 0.7970 - val_loss: 0.8270 Epoch 23/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9493 - val_loss: 0.7180 Epoch 24/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8372 - val_loss: 0.7380 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8457 - val_loss: 0.7112 Epoch 26/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7195 - val_loss: 0.6870 Epoch 27/280 15/15 [==============================] - 0s 10ms/step - loss: 0.7943 - val_loss: 0.6575 Epoch 28/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7529 - val_loss: 0.7097 Epoch 29/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6944 - val_loss: 0.6601 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6873 - val_loss: 0.7422 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6731 - val_loss: 0.6164 Epoch 32/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6947 - val_loss: 0.6660 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6314 - val_loss: 0.7325 Epoch 34/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7560 - val_loss: 0.6896 Epoch 35/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7288 - val_loss: 0.6792 Epoch 36/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7292 - val_loss: 0.6967 Epoch 37/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6105 - val_loss: 0.6262 Epoch 38/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7251 - val_loss: 0.5785 Epoch 39/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6971 - val_loss: 0.5742 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6764 - val_loss: 0.6914 Epoch 41/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7370 - val_loss: 0.7835 Epoch 42/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6001 - val_loss: 0.6084 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5410 - val_loss: 0.6046 Epoch 44/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6696 - val_loss: 0.5749 Epoch 45/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6586 - val_loss: 0.5534 Epoch 46/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5560 - val_loss: 0.6006 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6033 - val_loss: 0.5684 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5913 - val_loss: 0.6091 Epoch 49/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6363 - val_loss: 0.5119 Epoch 50/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5681 - val_loss: 0.5731 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5341 - val_loss: 0.5061 Epoch 52/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5671 - val_loss: 0.5192 Epoch 53/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5239 - val_loss: 0.6527 Epoch 54/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5469 - val_loss: 0.5055 Epoch 55/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5339 - val_loss: 0.5661 Epoch 56/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5102 - val_loss: 0.5130 Epoch 57/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4892 - val_loss: 0.5354 Epoch 58/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5005 - val_loss: 0.4813 Epoch 59/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5486 - val_loss: 0.4677 Epoch 60/280 15/15 [==============================] - 0s 11ms/step - loss: 0.4578 - val_loss: 0.5658 Epoch 61/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5319 - val_loss: 0.4716 Epoch 62/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5149 - val_loss: 0.4338 Epoch 63/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4531 - val_loss: 0.4298 Epoch 64/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4922 - val_loss: 0.4973 Epoch 65/280 15/15 [==============================] - 0s 17ms/step - loss: 0.5464 - val_loss: 0.5038 Epoch 66/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4528 - val_loss: 0.4625 Epoch 67/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4555 - val_loss: 0.4653 Epoch 68/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4918 - val_loss: 0.4619 Epoch 69/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4516 - val_loss: 0.4529 Epoch 70/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4390 - val_loss: 0.4423 Epoch 71/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4243 - val_loss: 0.4243 Epoch 72/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4119 - val_loss: 0.5160 Epoch 73/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4005 - val_loss: 0.4408 Epoch 74/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4297 - val_loss: 0.4306 Epoch 75/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4293 - val_loss: 0.4610 Epoch 76/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4115 - val_loss: 0.3962 Epoch 77/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3773 - val_loss: 0.4735 Epoch 78/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3768 - val_loss: 0.4152 Epoch 79/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4012 - val_loss: 0.4378 Epoch 80/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4226 - val_loss: 0.4112 Epoch 81/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4643 - val_loss: 0.4040 Epoch 82/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3945 - val_loss: 0.3504 Epoch 83/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3447 - val_loss: 0.3626 Epoch 84/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4264 - val_loss: 0.4764 Epoch 85/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5110 - val_loss: 0.3595 Epoch 86/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3792 - val_loss: 0.4096 Epoch 87/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3393 - val_loss: 0.3632 Epoch 88/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3819 - val_loss: 0.3798 Epoch 89/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3427 - val_loss: 0.3594 Epoch 90/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3622 - val_loss: 0.3543 Epoch 91/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3277 - val_loss: 0.3369 Epoch 92/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3397 - val_loss: 0.3200 Epoch 93/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3350 - val_loss: 0.3255 Epoch 94/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2814 - val_loss: 0.3326 Epoch 95/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2897 - val_loss: 0.3047 Epoch 96/280 15/15 [==============================] - 0s 12ms/step - loss: 0.3236 - val_loss: 0.3356 Epoch 97/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2907 - val_loss: 0.3490 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2919 - val_loss: 0.3174 Epoch 99/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3346 - val_loss: 0.3385 Epoch 100/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2826 - val_loss: 0.2934 Epoch 101/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2927 - val_loss: 0.3279 Epoch 102/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2928 - val_loss: 0.3109 Epoch 103/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2679 - val_loss: 0.2795 Epoch 104/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2976 - val_loss: 0.3162 Epoch 105/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3202 - val_loss: 0.2745 Epoch 106/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2536 - val_loss: 0.3388 Epoch 107/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3343 - val_loss: 0.2917 Epoch 108/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2965 - val_loss: 0.3187 Epoch 109/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2604 - val_loss: 0.2665 Epoch 110/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2710 - val_loss: 0.2802 Epoch 111/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2739 - val_loss: 0.2383 Epoch 112/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2721 - val_loss: 0.2425 Epoch 113/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2272 - val_loss: 0.3003 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2751 - val_loss: 0.2282 Epoch 115/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2650 - val_loss: 0.2294 Epoch 116/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2643 - val_loss: 0.2636 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3353 - val_loss: 0.2320 Epoch 118/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2664 - val_loss: 0.2094 Epoch 119/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2427 - val_loss: 0.2413 Epoch 120/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2390 - val_loss: 0.2177 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2449 - val_loss: 0.2402 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2631 - val_loss: 0.2137 Epoch 123/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2387 - val_loss: 0.2068 Epoch 124/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2433 - val_loss: 0.2341 Epoch 125/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1834 - val_loss: 0.2072 Epoch 126/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1973 - val_loss: 0.2419 Epoch 127/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1934 - val_loss: 0.2124 Epoch 128/280 15/15 [==============================] - 0s 15ms/step - loss: 0.2440 - val_loss: 0.2692 Epoch 129/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2326 - val_loss: 0.2106 Epoch 130/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1797 - val_loss: 0.1862 Epoch 131/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2054 - val_loss: 0.2030 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1795 - val_loss: 0.2119 Epoch 133/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2056 - val_loss: 0.2118 Epoch 134/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2077 - val_loss: 0.2097 Epoch 135/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2364 - val_loss: 0.2304 Epoch 136/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1959 - val_loss: 0.2032 Epoch 137/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1983 - val_loss: 0.2122 Epoch 138/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2184 - val_loss: 0.2047 Epoch 139/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1817 - val_loss: 0.2111 Epoch 140/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1568 - val_loss: 0.2090 Epoch 141/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1812 - val_loss: 0.2002 Epoch 142/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1744 - val_loss: 0.1984 Epoch 143/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1886 - val_loss: 0.1954 Epoch 144/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1793 - val_loss: 0.1943 Epoch 145/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1885 - val_loss: 0.1946 Epoch 146/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2055 - val_loss: 0.1927 Epoch 147/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2046 - val_loss: 0.1919 Epoch 148/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2020 - val_loss: 0.1875 Epoch 149/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1720 - val_loss: 0.1847 Epoch 150/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1480 - val_loss: 0.1880 Epoch 151/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1829 - val_loss: 0.1865 Epoch 152/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1643 - val_loss: 0.1906 Epoch 153/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1688 - val_loss: 0.1954 Epoch 154/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1584 - val_loss: 0.1961 Epoch 155/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1657 - val_loss: 0.1927 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1860 - val_loss: 0.1914 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1627 - val_loss: 0.1891 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1599 - val_loss: 0.1923 Epoch 159/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1583 - val_loss: 0.1945 Epoch 160/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1653 - val_loss: 0.1946 Epoch 161/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1630 - val_loss: 0.1938 Epoch 162/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1849 - val_loss: 0.1935 Epoch 163/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1816 - val_loss: 0.1934 Epoch 164/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1787 - val_loss: 0.1929 Epoch 165/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1755 - val_loss: 0.1932 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1952 - val_loss: 0.1934 Epoch 167/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1885 - val_loss: 0.1932 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1582 - val_loss: 0.1931 Epoch 169/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1774 - val_loss: 0.1935 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1551 - val_loss: 0.1935 Epoch 171/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1984 - val_loss: 0.1935 Epoch 172/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1613 - val_loss: 0.1936 Epoch 173/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1437 - val_loss: 0.1936 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1804 - val_loss: 0.1936 Epoch 175/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1749 - val_loss: 0.1936 Epoch 176/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1621 - val_loss: 0.1936 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1619 - val_loss: 0.1937 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1722 - val_loss: 0.1936 Epoch 179/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1642 - val_loss: 0.1936 Epoch 180/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1880 - val_loss: 0.1936 Epoch 181/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1713 - val_loss: 0.1936 Epoch 182/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2098 - val_loss: 0.1936 Epoch 183/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1343 - val_loss: 0.1936 Epoch 184/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1974 - val_loss: 0.1936 Epoch 185/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1773 - val_loss: 0.1936 Epoch 186/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1614 - val_loss: 0.1936 Epoch 187/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1606 - val_loss: 0.1936 Epoch 188/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1926 - val_loss: 0.1936 Epoch 189/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1581 - val_loss: 0.1936 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1727 - val_loss: 0.1936 Epoch 191/280 15/15 [==============================] - 0s 15ms/step - loss: 0.1736 - val_loss: 0.1936 Epoch 192/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1535 - val_loss: 0.1936 Epoch 193/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1823 - val_loss: 0.1936 Epoch 194/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1547 - val_loss: 0.1936 Epoch 195/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1697 - val_loss: 0.1936 Epoch 196/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1593 - val_loss: 0.1936 Epoch 197/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1424 - val_loss: 0.1936 Epoch 198/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1490 - val_loss: 0.1936 Epoch 199/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1651 - val_loss: 0.1936 Epoch 200/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1833 - val_loss: 0.1936 Epoch 201/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1759 - val_loss: 0.1936 Epoch 202/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1895 - val_loss: 0.1936 Epoch 203/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1532 - val_loss: 0.1936 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1492 - val_loss: 0.1936 Epoch 205/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1820 - val_loss: 0.1936 Epoch 206/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1774 - val_loss: 0.1936 Epoch 207/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1547 - val_loss: 0.1936 Epoch 208/280 15/15 [==============================] - 0s 16ms/step - loss: 0.1821 - val_loss: 0.1936 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1591 - val_loss: 0.1936 Epoch 210/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1618 - val_loss: 0.1936 Epoch 211/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1773 - val_loss: 0.1936 Epoch 212/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1511 - val_loss: 0.1936 Epoch 213/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1963 - val_loss: 0.1936 Epoch 214/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1926 - val_loss: 0.1936 Epoch 215/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1658 - val_loss: 0.1936 Epoch 216/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2016 - val_loss: 0.1936 Epoch 217/280 15/15 [==============================] - 0s 8ms/step - loss: 0.1397 - val_loss: 0.1936 Epoch 218/280 15/15 [==============================] - 0s 11ms/step - loss: 0.1688 - val_loss: 0.1936 Epoch 219/280 15/15 [==============================] - 0s 9ms/step - loss: 0.1774 - val_loss: 0.1936 Epoch 220/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1549 - val_loss: 0.1936 Epoch 221/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1472 - val_loss: 0.1936 Epoch 222/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1730 - val_loss: 0.1936 Epoch 223/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1665 - val_loss: 0.1936 Epoch 224/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1703 - val_loss: 0.1936 Epoch 225/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1613 - val_loss: 0.1936 Epoch 226/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1654 - val_loss: 0.1936 Epoch 227/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1493 - val_loss: 0.1936 Epoch 228/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1761 - val_loss: 0.1936 Epoch 229/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1988 - val_loss: 0.1936 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1459 - val_loss: 0.1936 Epoch 231/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1515 - val_loss: 0.1936 Epoch 232/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1596 - val_loss: 0.1936 Epoch 233/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1751 - val_loss: 0.1936 Epoch 234/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1762 - val_loss: 0.1936 Epoch 235/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1753 - val_loss: 0.1936 Epoch 236/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1923 - val_loss: 0.1936 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1639 - val_loss: 0.1936 Epoch 238/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1644 - val_loss: 0.1936 Epoch 239/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1670 - val_loss: 0.1936 Epoch 240/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1719 - val_loss: 0.1936 Epoch 241/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1763 - val_loss: 0.1936 Epoch 242/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1647 - val_loss: 0.1936 Epoch 243/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1762 - val_loss: 0.1936 Epoch 244/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1605 - val_loss: 0.1936 Epoch 245/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1380 - val_loss: 0.1936 Epoch 246/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1861 - val_loss: 0.1936 Epoch 247/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936 Epoch 248/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1579 - val_loss: 0.1936 Epoch 249/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1840 - val_loss: 0.1936 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1631 - val_loss: 0.1936 Epoch 251/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1912 - val_loss: 0.1936 Epoch 252/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1586 - val_loss: 0.1936 Epoch 253/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1560 - val_loss: 0.1936 Epoch 255/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1587 - val_loss: 0.1936 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1489 - val_loss: 0.1936 Epoch 257/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1425 - val_loss: 0.1936 Epoch 258/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1698 - val_loss: 0.1936 Epoch 259/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1693 - val_loss: 0.1936 Epoch 260/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1612 - val_loss: 0.1936 Epoch 261/280 15/15 [==============================] - 0s 16ms/step - loss: 0.1393 - val_loss: 0.1936 Epoch 262/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1759 - val_loss: 0.1936 Epoch 263/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1346 - val_loss: 0.1936 Epoch 264/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1715 - val_loss: 0.1936 Epoch 265/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1747 - val_loss: 0.1936 Epoch 266/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1783 - val_loss: 0.1936 Epoch 267/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2145 - val_loss: 0.1936 Epoch 268/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1618 - val_loss: 0.1936 Epoch 269/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1594 - val_loss: 0.1936 Epoch 270/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1733 - val_loss: 0.1936 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1540 - val_loss: 0.1936 Epoch 272/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1798 - val_loss: 0.1936 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1628 - val_loss: 0.1936 Epoch 274/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1702 - val_loss: 0.1936 Epoch 275/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1807 - val_loss: 0.1936 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1776 - val_loss: 0.1936 Epoch 277/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1753 - val_loss: 0.1936 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1638 - val_loss: 0.1936 Epoch 279/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1876 - val_loss: 0.1936 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1817 - val_loss: 0.1936 COL: 比表面积, MSE: 3.31E+00,RMSE: 1.8196,MAPE: 23.23 %,MAE: 1.7385,R_2: -5.7583 COL: 总孔体积, MSE: 2.00E-01,RMSE: 0.4469,MAPE: 73.2 %,MAE: 0.3261,R_2: 0.383 COL: 微孔体积, MSE: 8.91E-02,RMSE: 0.2985,MAPE: 67.27 %,MAE: 0.2204,R_2: -0.409 Epoch 1/280 15/15 [==============================] - 6s 87ms/step - loss: 1.9754 - val_loss: 1.9920 Epoch 2/280 15/15 [==============================] - 0s 14ms/step - loss: 1.8934 - val_loss: 1.8108 Epoch 3/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8678 - val_loss: 1.7804 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8099 - val_loss: 1.7808 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7852 - val_loss: 1.7365 Epoch 6/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8005 - val_loss: 1.7159 Epoch 7/280 15/15 [==============================] - 0s 13ms/step - loss: 1.8002 - val_loss: 1.7278 Epoch 8/280 15/15 [==============================] - 0s 15ms/step - loss: 1.7150 - val_loss: 1.6841 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 1.7070 - val_loss: 1.6588 Epoch 10/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6771 - val_loss: 1.6260 Epoch 11/280 15/15 [==============================] - 0s 14ms/step - loss: 1.6522 - val_loss: 1.6379 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 1.6243 - val_loss: 1.5841 Epoch 13/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5996 - val_loss: 1.5591 Epoch 14/280 15/15 [==============================] - 0s 14ms/step - loss: 1.6091 - val_loss: 1.5598 Epoch 15/280 15/15 [==============================] - 0s 14ms/step - loss: 1.5881 - val_loss: 1.5620 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5255 - val_loss: 1.5440 Epoch 17/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5364 - val_loss: 1.5459 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 1.5452 - val_loss: 1.5002 Epoch 19/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4664 - val_loss: 1.4460 Epoch 20/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4699 - val_loss: 1.4584 Epoch 21/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4165 - val_loss: 1.4341 Epoch 22/280 15/15 [==============================] - 0s 12ms/step - loss: 1.4157 - val_loss: 1.4142 Epoch 23/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4067 - val_loss: 1.4117 Epoch 24/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3716 - val_loss: 1.3767 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3796 - val_loss: 1.3504 Epoch 26/280 15/15 [==============================] - 0s 14ms/step - loss: 1.3564 - val_loss: 1.3289 Epoch 27/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2984 - val_loss: 1.3355 Epoch 28/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2968 - val_loss: 1.3116 Epoch 29/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2898 - val_loss: 1.2716 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2706 - val_loss: 1.2734 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2616 - val_loss: 1.2720 Epoch 32/280 15/15 [==============================] - 0s 12ms/step - loss: 1.2315 - val_loss: 1.2208 Epoch 33/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2314 - val_loss: 1.2156 Epoch 34/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2090 - val_loss: 1.1900 Epoch 35/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1728 - val_loss: 1.1776 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1574 - val_loss: 1.1586 Epoch 37/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1426 - val_loss: 1.1430 Epoch 38/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1540 - val_loss: 1.1104 Epoch 39/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1075 - val_loss: 1.1120 Epoch 40/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0754 - val_loss: 1.0676 Epoch 41/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0708 - val_loss: 1.0648 Epoch 42/280 15/15 [==============================] - 0s 15ms/step - loss: 1.0629 - val_loss: 1.0402 Epoch 43/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0278 - val_loss: 1.0344 Epoch 44/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0377 - val_loss: 1.0089 Epoch 45/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0029 - val_loss: 0.9939 Epoch 46/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9828 - val_loss: 0.9852 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9729 - val_loss: 0.9580 Epoch 48/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9498 - val_loss: 0.9360 Epoch 49/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9313 - val_loss: 0.9178 Epoch 50/280 15/15 [==============================] - 0s 13ms/step - loss: 0.9114 - val_loss: 0.9116 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8911 - val_loss: 0.8786 Epoch 52/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8728 - val_loss: 0.8649 Epoch 53/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8607 - val_loss: 0.8530 Epoch 54/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8456 - val_loss: 0.8272 Epoch 55/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8353 - val_loss: 0.8141 Epoch 56/280 15/15 [==============================] - 0s 11ms/step - loss: 0.8056 - val_loss: 0.8010 Epoch 57/280 15/15 [==============================] - 0s 12ms/step - loss: 0.7975 - val_loss: 0.7785 Epoch 58/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7706 - val_loss: 0.7631 Epoch 59/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7487 - val_loss: 0.7437 Epoch 60/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7475 - val_loss: 0.7329 Epoch 61/280 15/15 [==============================] - 0s 15ms/step - loss: 0.7218 - val_loss: 0.7051 Epoch 62/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6961 - val_loss: 0.7014 Epoch 63/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6748 - val_loss: 0.6711 Epoch 64/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6677 - val_loss: 0.6656 Epoch 65/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6512 - val_loss: 0.6350 Epoch 66/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6440 - val_loss: 0.6271 Epoch 67/280 15/15 [==============================] - 0s 13ms/step - loss: 0.6210 - val_loss: 0.6103 Epoch 68/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5943 - val_loss: 0.5891 Epoch 69/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5832 - val_loss: 0.5770 Epoch 70/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5636 - val_loss: 0.5581 Epoch 71/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5530 - val_loss: 0.5416 Epoch 72/280 15/15 [==============================] - 0s 11ms/step - loss: 0.5373 - val_loss: 0.5298 Epoch 73/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5210 - val_loss: 0.5089 Epoch 74/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5073 - val_loss: 0.5021 Epoch 75/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4912 - val_loss: 0.4890 Epoch 76/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4719 - val_loss: 0.4673 Epoch 77/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4608 - val_loss: 0.4523 Epoch 78/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4363 - val_loss: 0.4391 Epoch 79/280 15/15 [==============================] - 0s 13ms/step - loss: 0.4274 - val_loss: 0.4227 Epoch 80/280 15/15 [==============================] - 0s 12ms/step - loss: 0.4084 - val_loss: 0.4116 Epoch 81/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3988 - val_loss: 0.3912 Epoch 82/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3783 - val_loss: 0.3845 Epoch 83/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3693 - val_loss: 0.3615 Epoch 84/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3551 - val_loss: 0.3554 Epoch 85/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3354 - val_loss: 0.3415 Epoch 86/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3240 - val_loss: 0.3161 Epoch 87/280 15/15 [==============================] - 0s 14ms/step - loss: 0.3123 - val_loss: 0.3055 Epoch 88/280 15/15 [==============================] - 0s 17ms/step - loss: 0.2883 - val_loss: 0.2887 Epoch 89/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2689 - val_loss: 0.2721 Epoch 90/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2605 - val_loss: 0.2620 Epoch 91/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2468 - val_loss: 0.2441 Epoch 92/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2334 - val_loss: 0.2355 Epoch 93/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2148 - val_loss: 0.2202 Epoch 94/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2101 - val_loss: 0.1989 Epoch 95/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1962 - val_loss: 0.1927 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1746 - val_loss: 0.1719 Epoch 97/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1619 - val_loss: 0.1531 Epoch 98/280 15/15 [==============================] - 0s 15ms/step - loss: 0.1445 - val_loss: 0.1398 Epoch 99/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1297 - val_loss: 0.1240 Epoch 100/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1157 - val_loss: 0.1125 Epoch 101/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0919 - val_loss: 0.0965 Epoch 102/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0815 - val_loss: 0.0808 Epoch 103/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0625 - val_loss: 0.0682 Epoch 104/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0499 - val_loss: 0.0650 Epoch 105/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0425 - val_loss: 0.0400 Epoch 106/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0262 - val_loss: 0.0429 Epoch 107/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0288 - val_loss: 0.0370 Epoch 108/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0235 - val_loss: 0.0440 Epoch 109/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0243 - val_loss: 0.0438 Epoch 110/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0374 Epoch 111/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0298 - val_loss: 0.0396 Epoch 112/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0444 Epoch 113/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0318 - val_loss: 0.0488 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0515 Epoch 115/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0436 Epoch 116/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0380 - val_loss: 0.0419 Epoch 117/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0430 Epoch 118/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0451 Epoch 119/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0216 - val_loss: 0.0450 Epoch 120/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0427 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0263 - val_loss: 0.0422 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0261 - val_loss: 0.0422 Epoch 123/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0268 - val_loss: 0.0432 Epoch 124/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0220 - val_loss: 0.0437 Epoch 125/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0433 Epoch 126/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0434 Epoch 127/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0327 - val_loss: 0.0443 Epoch 128/280 15/15 [==============================] - 0s 17ms/step - loss: 0.0255 - val_loss: 0.0442 Epoch 129/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0443 Epoch 130/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0244 - val_loss: 0.0444 Epoch 131/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0443 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0443 Epoch 133/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0442 Epoch 134/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0444 Epoch 135/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0215 - val_loss: 0.0444 Epoch 136/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0444 Epoch 137/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0445 Epoch 138/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0210 - val_loss: 0.0445 Epoch 139/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0182 - val_loss: 0.0445 Epoch 140/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0291 - val_loss: 0.0445 Epoch 141/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0183 - val_loss: 0.0445 Epoch 142/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0445 Epoch 143/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0252 - val_loss: 0.0445 Epoch 144/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0446 Epoch 145/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0446 Epoch 146/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0446 Epoch 147/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0174 - val_loss: 0.0446 Epoch 148/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0446 Epoch 149/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0233 - val_loss: 0.0446 Epoch 150/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0201 - val_loss: 0.0446 Epoch 151/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0195 - val_loss: 0.0446 Epoch 152/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0260 - val_loss: 0.0446 Epoch 153/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0446 Epoch 154/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0446 Epoch 155/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0446 Epoch 156/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0446 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0446 Epoch 158/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0190 - val_loss: 0.0446 Epoch 159/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0165 - val_loss: 0.0446 Epoch 160/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0188 - val_loss: 0.0446 Epoch 161/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0446 Epoch 162/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0234 - val_loss: 0.0446 Epoch 163/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0319 - val_loss: 0.0446 Epoch 164/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0446 Epoch 165/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0244 - val_loss: 0.0446 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0283 - val_loss: 0.0446 Epoch 167/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0183 - val_loss: 0.0446 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0446 Epoch 169/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0320 - val_loss: 0.0446 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446 Epoch 171/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0446 Epoch 172/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446 Epoch 173/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0446 Epoch 174/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0446 Epoch 175/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0191 - val_loss: 0.0446 Epoch 176/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446 Epoch 177/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0237 - val_loss: 0.0446 Epoch 178/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0446 Epoch 179/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0191 - val_loss: 0.0446 Epoch 180/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0278 - val_loss: 0.0446 Epoch 181/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0446 Epoch 182/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0446 Epoch 183/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0204 - val_loss: 0.0446 Epoch 184/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0446 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446 Epoch 186/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446 Epoch 187/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0446 Epoch 188/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0446 Epoch 189/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0446 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0446 Epoch 191/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0198 - val_loss: 0.0446 Epoch 192/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0446 Epoch 193/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446 Epoch 194/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0446 Epoch 195/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0446 Epoch 196/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446 Epoch 197/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0446 Epoch 198/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0446 Epoch 199/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0446 Epoch 200/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0446 Epoch 201/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0250 - val_loss: 0.0446 Epoch 202/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0446 Epoch 203/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0177 - val_loss: 0.0446 Epoch 204/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0446 Epoch 205/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0446 Epoch 206/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0274 - val_loss: 0.0446 Epoch 207/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446 Epoch 208/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0446 Epoch 209/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0256 - val_loss: 0.0446 Epoch 210/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0223 - val_loss: 0.0446 Epoch 211/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0236 - val_loss: 0.0446 Epoch 212/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0159 - val_loss: 0.0446 Epoch 213/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0226 - val_loss: 0.0446 Epoch 214/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0212 - val_loss: 0.0446 Epoch 215/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0221 - val_loss: 0.0446 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0186 - val_loss: 0.0446 Epoch 217/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0180 - val_loss: 0.0446 Epoch 218/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0255 - val_loss: 0.0446 Epoch 219/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0446 Epoch 220/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0446 Epoch 221/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0221 - val_loss: 0.0446 Epoch 222/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0256 - val_loss: 0.0446 Epoch 223/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0446 Epoch 224/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0206 - val_loss: 0.0446 Epoch 225/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446 Epoch 226/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446 Epoch 227/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0446 Epoch 228/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0446 Epoch 229/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0208 - val_loss: 0.0446 Epoch 230/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0239 - val_loss: 0.0446 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0446 Epoch 232/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0446 Epoch 233/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0253 - val_loss: 0.0446 Epoch 234/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0446 Epoch 235/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0218 - val_loss: 0.0446 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0446 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0203 - val_loss: 0.0446 Epoch 238/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0257 - val_loss: 0.0446 Epoch 239/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0446 Epoch 240/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446 Epoch 241/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0254 - val_loss: 0.0446 Epoch 242/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0446 Epoch 243/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0260 - val_loss: 0.0446 Epoch 244/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0446 Epoch 245/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0184 - val_loss: 0.0446 Epoch 246/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0446 Epoch 247/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0446 Epoch 248/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0446 Epoch 249/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0446 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0446 Epoch 251/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0446 Epoch 252/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0237 - val_loss: 0.0446 Epoch 253/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0225 - val_loss: 0.0446 Epoch 254/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0246 - val_loss: 0.0446 Epoch 255/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0346 - val_loss: 0.0446 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0446 Epoch 257/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0212 - val_loss: 0.0446 Epoch 258/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0218 - val_loss: 0.0446 Epoch 259/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0446 Epoch 260/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0196 - val_loss: 0.0446 Epoch 261/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0446 Epoch 262/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0446 Epoch 263/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0446 Epoch 264/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0446 Epoch 265/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0215 - val_loss: 0.0446 Epoch 266/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0222 - val_loss: 0.0446 Epoch 267/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0218 - val_loss: 0.0446 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0446 Epoch 269/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0446 Epoch 270/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0446 Epoch 271/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0446 Epoch 272/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0259 - val_loss: 0.0446 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0239 - val_loss: 0.0446 Epoch 274/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0251 - val_loss: 0.0446 Epoch 275/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0205 - val_loss: 0.0446 Epoch 276/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0446 Epoch 277/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0446 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0446 Epoch 279/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0446 Epoch 280/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0304 - val_loss: 0.0446 COL: 比表面积, MSE: 8.57E-02,RMSE: 0.2928,MAPE: 3.18 %,MAE: 0.2321,R_2: 0.6612 COL: 总孔体积, MSE: 1.47E-01,RMSE: 0.3828,MAPE: 23.830000000000002 %,MAE: 0.2373,R_2: 0.5847 COL: 微孔体积, MSE: 3.54E-02,RMSE: 0.1882,MAPE: 25.540000000000003 %,MAE: 0.1482,R_2: 0.6431 Epoch 1/280 15/15 [==============================] - 6s 87ms/step - loss: 1.7915 - val_loss: 1.5831 Epoch 2/280 15/15 [==============================] - 0s 14ms/step - loss: 1.4854 - val_loss: 1.5479 Epoch 3/280 15/15 [==============================] - 0s 14ms/step - loss: 1.4388 - val_loss: 1.5548 Epoch 4/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4186 - val_loss: 1.5243 Epoch 5/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4387 - val_loss: 1.4333 Epoch 6/280 15/15 [==============================] - 0s 12ms/step - loss: 1.3341 - val_loss: 1.4208 Epoch 7/280 15/15 [==============================] - 0s 13ms/step - loss: 1.4012 - val_loss: 1.3857 Epoch 8/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2815 - val_loss: 1.3240 Epoch 9/280 15/15 [==============================] - 0s 13ms/step - loss: 1.2213 - val_loss: 1.2801 Epoch 10/280 15/15 [==============================] - 0s 14ms/step - loss: 1.1352 - val_loss: 1.2286 Epoch 11/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1376 - val_loss: 1.1920 Epoch 12/280 15/15 [==============================] - 0s 13ms/step - loss: 1.1759 - val_loss: 1.1531 Epoch 13/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0811 - val_loss: 1.0861 Epoch 14/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0537 - val_loss: 1.0637 Epoch 15/280 15/15 [==============================] - 0s 12ms/step - loss: 1.0311 - val_loss: 1.0354 Epoch 16/280 15/15 [==============================] - 0s 13ms/step - loss: 1.0069 - val_loss: 0.9676 Epoch 17/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9223 - val_loss: 0.9667 Epoch 18/280 15/15 [==============================] - 0s 13ms/step - loss: 0.8913 - val_loss: 0.9186 Epoch 19/280 15/15 [==============================] - 0s 14ms/step - loss: 0.9217 - val_loss: 0.9701 Epoch 20/280 15/15 [==============================] - 0s 12ms/step - loss: 0.9211 - val_loss: 0.8864 Epoch 21/280 15/15 [==============================] - 0s 12ms/step - loss: 0.8601 - val_loss: 0.8302 Epoch 22/280 15/15 [==============================] - 0s 14ms/step - loss: 0.8128 - val_loss: 0.8375 Epoch 23/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7730 - val_loss: 0.7847 Epoch 24/280 15/15 [==============================] - 0s 13ms/step - loss: 0.7332 - val_loss: 0.7959 Epoch 25/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6908 - val_loss: 0.7689 Epoch 26/280 15/15 [==============================] - 0s 14ms/step - loss: 0.7353 - val_loss: 0.7359 Epoch 27/280 15/15 [==============================] - 0s 12ms/step - loss: 0.6959 - val_loss: 0.6988 Epoch 28/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6375 - val_loss: 0.6795 Epoch 29/280 15/15 [==============================] - 0s 14ms/step - loss: 0.6234 - val_loss: 0.6861 Epoch 30/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5952 - val_loss: 0.6636 Epoch 31/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5796 - val_loss: 0.6411 Epoch 32/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5588 - val_loss: 0.5939 Epoch 33/280 15/15 [==============================] - 0s 14ms/step - loss: 0.5059 - val_loss: 0.5869 Epoch 34/280 15/15 [==============================] - 0s 12ms/step - loss: 0.5317 - val_loss: 0.5622 Epoch 35/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5272 - val_loss: 0.5156 Epoch 36/280 15/15 [==============================] - 0s 13ms/step - loss: 0.5096 - val_loss: 0.5028 Epoch 37/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4625 - val_loss: 0.4561 Epoch 38/280 15/15 [==============================] - 0s 14ms/step - loss: 0.4435 - val_loss: 0.4643 Epoch 39/280 15/15 [==============================] - 0s 16ms/step - loss: 0.4074 - val_loss: 0.3901 Epoch 40/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3852 - val_loss: 0.3962 Epoch 41/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3540 - val_loss: 0.3499 Epoch 42/280 15/15 [==============================] - 0s 13ms/step - loss: 0.3305 - val_loss: 0.3387 Epoch 43/280 15/15 [==============================] - 0s 15ms/step - loss: 0.2808 - val_loss: 0.3249 Epoch 44/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2860 - val_loss: 0.2871 Epoch 45/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2449 - val_loss: 0.2715 Epoch 46/280 15/15 [==============================] - 0s 14ms/step - loss: 0.2613 - val_loss: 0.2669 Epoch 47/280 15/15 [==============================] - 0s 13ms/step - loss: 0.2432 - val_loss: 0.2790 Epoch 48/280 15/15 [==============================] - 0s 12ms/step - loss: 0.2318 - val_loss: 0.2070 Epoch 49/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1715 - val_loss: 0.2061 Epoch 50/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1673 - val_loss: 0.1978 Epoch 51/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1722 - val_loss: 0.2138 Epoch 52/280 15/15 [==============================] - 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0s 14ms/step - loss: 0.1373 - val_loss: 0.1609 Epoch 63/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1333 - val_loss: 0.1626 Epoch 64/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1308 - val_loss: 0.1722 Epoch 65/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1480 - val_loss: 0.1668 Epoch 66/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1385 - val_loss: 0.1745 Epoch 67/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1379 - val_loss: 0.1651 Epoch 68/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1351 - val_loss: 0.1787 Epoch 69/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1341 - val_loss: 0.1736 Epoch 70/280 15/15 [==============================] - 0s 13ms/step - loss: 0.1363 - val_loss: 0.1608 Epoch 71/280 15/15 [==============================] - 0s 14ms/step - loss: 0.1263 - val_loss: 0.1555 Epoch 72/280 15/15 [==============================] - 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0s 13ms/step - loss: 0.1106 - val_loss: 0.1252 Epoch 83/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0947 - val_loss: 0.1318 Epoch 84/280 15/15 [==============================] - 0s 12ms/step - loss: 0.1007 - val_loss: 0.1268 Epoch 85/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0989 - val_loss: 0.1345 Epoch 86/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0691 - val_loss: 0.1216 Epoch 87/280 15/15 [==============================] - 0s 9ms/step - loss: 0.0807 - val_loss: 0.1239 Epoch 88/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0750 - val_loss: 0.1082 Epoch 89/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0807 - val_loss: 0.1143 Epoch 90/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0740 - val_loss: 0.1164 Epoch 91/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0715 - val_loss: 0.1135 Epoch 92/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0710 - val_loss: 0.1208 Epoch 93/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0686 - val_loss: 0.1155 Epoch 94/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0684 - val_loss: 0.0992 Epoch 95/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0770 - val_loss: 0.1185 Epoch 96/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0633 - val_loss: 0.0914 Epoch 97/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0657 - val_loss: 0.0985 Epoch 98/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0635 - val_loss: 0.0949 Epoch 99/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0546 - val_loss: 0.0930 Epoch 100/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0536 - val_loss: 0.0830 Epoch 101/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0486 - val_loss: 0.0842 Epoch 102/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0479 - val_loss: 0.0841 Epoch 103/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0545 - val_loss: 0.0764 Epoch 104/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0476 - val_loss: 0.0760 Epoch 105/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0482 - val_loss: 0.0715 Epoch 106/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0476 - val_loss: 0.0705 Epoch 107/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0411 - val_loss: 0.0652 Epoch 108/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0474 - val_loss: 0.0638 Epoch 109/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0408 - val_loss: 0.0665 Epoch 110/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0408 - val_loss: 0.0645 Epoch 111/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0369 - val_loss: 0.0660 Epoch 112/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0345 - val_loss: 0.0544 Epoch 113/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0384 - val_loss: 0.0657 Epoch 114/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0342 - val_loss: 0.0542 Epoch 115/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0341 - val_loss: 0.0580 Epoch 116/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0588 Epoch 117/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0237 - val_loss: 0.0608 Epoch 118/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0236 - val_loss: 0.0521 Epoch 119/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0333 - val_loss: 0.0608 Epoch 120/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0267 - val_loss: 0.0640 Epoch 121/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0703 Epoch 122/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0278 - val_loss: 0.0619 Epoch 123/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0253 - val_loss: 0.0610 Epoch 124/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0299 - val_loss: 0.0641 Epoch 125/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0281 - val_loss: 0.0571 Epoch 126/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0259 - val_loss: 0.0676 Epoch 127/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0286 - val_loss: 0.0611 Epoch 128/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0323 - val_loss: 0.0576 Epoch 129/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0573 Epoch 130/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0234 - val_loss: 0.0563 Epoch 131/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0201 - val_loss: 0.0559 Epoch 132/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0204 - val_loss: 0.0563 Epoch 133/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0227 - val_loss: 0.0564 Epoch 134/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0286 - val_loss: 0.0567 Epoch 135/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0176 - val_loss: 0.0560 Epoch 136/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0300 - val_loss: 0.0556 Epoch 137/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0544 Epoch 138/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0538 Epoch 139/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0538 Epoch 140/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0539 Epoch 141/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0540 Epoch 142/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0224 - val_loss: 0.0541 Epoch 143/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0542 Epoch 144/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0228 - val_loss: 0.0542 Epoch 145/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0259 - val_loss: 0.0542 Epoch 146/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0543 Epoch 147/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0258 - val_loss: 0.0544 Epoch 148/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0275 - val_loss: 0.0544 Epoch 149/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0224 - val_loss: 0.0544 Epoch 150/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0544 Epoch 151/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0544 Epoch 152/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0213 - val_loss: 0.0544 Epoch 153/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0183 - val_loss: 0.0544 Epoch 154/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0260 - val_loss: 0.0544 Epoch 155/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0164 - val_loss: 0.0544 Epoch 156/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0195 - val_loss: 0.0544 Epoch 157/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0544 Epoch 158/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0203 - val_loss: 0.0544 Epoch 159/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0181 - val_loss: 0.0544 Epoch 160/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0544 Epoch 161/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544 Epoch 162/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0544 Epoch 163/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0151 - val_loss: 0.0544 Epoch 164/280 15/15 [==============================] - 0s 17ms/step - loss: 0.0239 - val_loss: 0.0544 Epoch 165/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544 Epoch 166/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0222 - val_loss: 0.0544 Epoch 167/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544 Epoch 168/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0544 Epoch 169/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0235 - val_loss: 0.0544 Epoch 170/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0544 Epoch 171/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544 Epoch 172/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0544 Epoch 173/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544 Epoch 174/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0313 - val_loss: 0.0544 Epoch 175/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0544 Epoch 176/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0317 - val_loss: 0.0544 Epoch 177/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0544 Epoch 178/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0544 Epoch 179/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0231 - val_loss: 0.0544 Epoch 180/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544 Epoch 181/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0215 - val_loss: 0.0544 Epoch 182/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0232 - val_loss: 0.0544 Epoch 183/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544 Epoch 184/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0206 - val_loss: 0.0544 Epoch 185/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0241 - val_loss: 0.0544 Epoch 186/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0544 Epoch 187/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0544 Epoch 188/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0544 Epoch 189/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0243 - val_loss: 0.0544 Epoch 190/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0544 Epoch 191/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0544 Epoch 192/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0204 - val_loss: 0.0544 Epoch 193/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0544 Epoch 194/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0227 - val_loss: 0.0544 Epoch 195/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0544 Epoch 196/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0544 Epoch 197/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0246 - val_loss: 0.0544 Epoch 198/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0188 - val_loss: 0.0544 Epoch 199/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0207 - val_loss: 0.0544 Epoch 200/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0544 Epoch 201/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0225 - val_loss: 0.0544 Epoch 202/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0283 - val_loss: 0.0544 Epoch 203/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0205 - val_loss: 0.0544 Epoch 204/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0544 Epoch 205/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0175 - val_loss: 0.0544 Epoch 206/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0544 Epoch 207/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0201 - val_loss: 0.0544 Epoch 208/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544 Epoch 209/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0544 Epoch 210/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544 Epoch 211/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0544 Epoch 212/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0544 Epoch 213/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0544 Epoch 214/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0247 - val_loss: 0.0544 Epoch 215/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0226 - val_loss: 0.0544 Epoch 216/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0231 - val_loss: 0.0544 Epoch 217/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0202 - val_loss: 0.0544 Epoch 218/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544 Epoch 219/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544 Epoch 220/280 15/15 [==============================] - 0s 16ms/step - loss: 0.0200 - val_loss: 0.0544 Epoch 221/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0544 Epoch 222/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0183 - val_loss: 0.0544 Epoch 223/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0229 - val_loss: 0.0544 Epoch 224/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0143 - val_loss: 0.0544 Epoch 225/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0265 - val_loss: 0.0544 Epoch 226/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544 Epoch 227/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0248 - val_loss: 0.0544 Epoch 228/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0238 - val_loss: 0.0544 Epoch 229/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0214 - val_loss: 0.0544 Epoch 230/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0181 - val_loss: 0.0544 Epoch 231/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0544 Epoch 232/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544 Epoch 233/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544 Epoch 234/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0544 Epoch 235/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0262 - val_loss: 0.0544 Epoch 236/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0544 Epoch 237/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0218 - val_loss: 0.0544 Epoch 238/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0227 - val_loss: 0.0544 Epoch 239/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0211 - val_loss: 0.0544 Epoch 240/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0263 - val_loss: 0.0544 Epoch 241/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0193 - val_loss: 0.0544 Epoch 242/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0240 - val_loss: 0.0544 Epoch 243/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0544 Epoch 244/280 15/15 [==============================] - 0s 17ms/step - loss: 0.0166 - val_loss: 0.0544 Epoch 245/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0544 Epoch 246/280 15/15 [==============================] - 0s 10ms/step - loss: 0.0247 - val_loss: 0.0544 Epoch 247/280 15/15 [==============================] - 0s 11ms/step - loss: 0.0236 - val_loss: 0.0544 Epoch 248/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0217 - val_loss: 0.0544 Epoch 249/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0226 - val_loss: 0.0544 Epoch 250/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0183 - val_loss: 0.0544 Epoch 251/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0544 Epoch 252/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0228 - val_loss: 0.0544 Epoch 253/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0265 - val_loss: 0.0544 Epoch 254/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0253 - val_loss: 0.0544 Epoch 255/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0211 - val_loss: 0.0544 Epoch 256/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0255 - val_loss: 0.0544 Epoch 257/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0544 Epoch 258/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0273 - val_loss: 0.0544 Epoch 259/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0248 - val_loss: 0.0544 Epoch 260/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0544 Epoch 261/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0230 - val_loss: 0.0544 Epoch 262/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0210 - val_loss: 0.0544 Epoch 263/280 15/15 [==============================] - 0s 12ms/step - loss: 0.0240 - val_loss: 0.0544 Epoch 264/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0544 Epoch 265/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0163 - val_loss: 0.0544 Epoch 266/280 15/15 [==============================] - 0s 15ms/step - loss: 0.0201 - val_loss: 0.0544 Epoch 267/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0216 - val_loss: 0.0544 Epoch 268/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0544 Epoch 269/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0265 - val_loss: 0.0544 Epoch 270/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0193 - val_loss: 0.0544 Epoch 271/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0209 - val_loss: 0.0544 Epoch 272/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0199 - val_loss: 0.0544 Epoch 273/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0544 Epoch 274/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0182 - val_loss: 0.0544 Epoch 275/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0258 - val_loss: 0.0544 Epoch 276/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0150 - val_loss: 0.0544 Epoch 277/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0544 Epoch 278/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0210 - val_loss: 0.0544 Epoch 279/280 15/15 [==============================] - 0s 14ms/step - loss: 0.0220 - val_loss: 0.0544 Epoch 280/280 15/15 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0544 COL: 比表面积, MSE: 5.96E-02,RMSE: 0.244,MAPE: 2.69 %,MAE: 0.2008,R_2: 0.6614 COL: 总孔体积, MSE: 1.18E-01,RMSE: 0.3435,MAPE: 23.05 %,MAE: 0.2474,R_2: 0.5394 COL: 微孔体积, MSE: 1.40E-02,RMSE: 0.1182,MAPE: 12.839999999999998 %,MAE: 0.0836,R_2: 0.6906
In [30]:
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)
In [31]:
prediction_model = get_prediction_model() trainable_model = get_trainable_model(prediction_model)
In [32]:
prediction_model.summary()
Model: "model_20" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input (InputLayer) [(None, 1, 9)] 0 __________________________________________________________________________________________________ conv1d_10 (Conv1D) (None, 1, 64) 640 input[0][0] __________________________________________________________________________________________________ dropout_40 (Dropout) (None, 1, 64) 0 conv1d_10[0][0] __________________________________________________________________________________________________ bidirectional_10 (Bidirectional (None, 1, 128) 66048 dropout_40[0][0] __________________________________________________________________________________________________ transformer_block_10 (Transform (None, 1, 128) 201612 bidirectional_10[0][0] __________________________________________________________________________________________________ global_average_pooling1d_10 (Gl (None, 128) 0 transformer_block_10[0][0] __________________________________________________________________________________________________ dropout_43 (Dropout) (None, 128) 0 global_average_pooling1d_10[0][0] __________________________________________________________________________________________________ dense_73 (Dense) (None, 64) 8256 dropout_43[0][0] __________________________________________________________________________________________________ dense_74 (Dense) (None, 32) 2080 dense_73[0][0] __________________________________________________________________________________________________ dense_75 (Dense) (None, 32) 2080 dense_73[0][0] __________________________________________________________________________________________________ dense_76 (Dense) (None, 32) 2080 dense_73[0][0] __________________________________________________________________________________________________ bet2 (Dense) (None, 1) 33 dense_74[0][0] __________________________________________________________________________________________________ mesco2 (Dense) (None, 1) 33 dense_75[0][0] __________________________________________________________________________________________________ micro2 (Dense) (None, 1) 33 dense_76[0][0] ================================================================================================== Total params: 282,895 Trainable params: 282,895 Non-trainable params: 0 __________________________________________________________________________________________________
In [33]:
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]
In [34]:
X_valid = np.expand_dims(valid[feature_cols].values, axis=1)
In [35]:
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] )
Epoch 1/280 14/14 [==============================] - 6s 102ms/step - loss: 1.9726 - val_loss: 1.9315 Epoch 2/280 14/14 [==============================] - 0s 14ms/step - loss: 1.9151 - val_loss: 1.8694 Epoch 3/280 14/14 [==============================] - 0s 12ms/step - loss: 1.8556 - val_loss: 1.8408 Epoch 4/280 14/14 [==============================] - 0s 13ms/step - loss: 1.8443 - val_loss: 1.8135 Epoch 5/280 14/14 [==============================] - 0s 13ms/step - loss: 1.7872 - val_loss: 1.7786 Epoch 6/280 14/14 [==============================] - 0s 14ms/step - loss: 1.7575 - val_loss: 1.7491 Epoch 7/280 14/14 [==============================] - 0s 14ms/step - loss: 1.7200 - val_loss: 1.7100 Epoch 8/280 14/14 [==============================] - 0s 14ms/step - loss: 1.7015 - val_loss: 1.6676 Epoch 9/280 14/14 [==============================] - 0s 14ms/step - loss: 1.6513 - val_loss: 1.6266 Epoch 10/280 14/14 [==============================] - 0s 14ms/step - loss: 1.6336 - val_loss: 1.5949 Epoch 11/280 14/14 [==============================] - 0s 14ms/step - loss: 1.5841 - val_loss: 1.5725 Epoch 12/280 14/14 [==============================] - 0s 13ms/step - loss: 1.5839 - val_loss: 1.5405 Epoch 13/280 14/14 [==============================] - 0s 14ms/step - loss: 1.5170 - val_loss: 1.5139 Epoch 14/280 14/14 [==============================] - 0s 14ms/step - loss: 1.5071 - val_loss: 1.4670 Epoch 15/280 14/14 [==============================] - 0s 15ms/step - loss: 1.4613 - val_loss: 1.4348 Epoch 16/280 14/14 [==============================] - 0s 14ms/step - loss: 1.4164 - val_loss: 1.4158 Epoch 17/280 14/14 [==============================] - 0s 16ms/step - loss: 1.4005 - val_loss: 1.3993 Epoch 18/280 14/14 [==============================] - 0s 13ms/step - loss: 1.3717 - val_loss: 1.3793 Epoch 19/280 14/14 [==============================] - 0s 14ms/step - loss: 1.3516 - val_loss: 1.3384 Epoch 20/280 14/14 [==============================] - 0s 14ms/step - loss: 1.3167 - val_loss: 1.2922 Epoch 21/280 14/14 [==============================] - 0s 14ms/step - loss: 1.2712 - val_loss: 1.2535 Epoch 22/280 14/14 [==============================] - 0s 13ms/step - loss: 1.2406 - val_loss: 1.2342 Epoch 23/280 14/14 [==============================] - 0s 14ms/step - loss: 1.2112 - val_loss: 1.2048 Epoch 24/280 14/14 [==============================] - 0s 13ms/step - loss: 1.1871 - val_loss: 1.1615 Epoch 25/280 14/14 [==============================] - 0s 17ms/step - loss: 1.1634 - val_loss: 1.1365 Epoch 26/280 14/14 [==============================] - 0s 14ms/step - loss: 1.1384 - val_loss: 1.1151 Epoch 27/280 14/14 [==============================] - 0s 14ms/step - loss: 1.1062 - val_loss: 1.1013 Epoch 28/280 14/14 [==============================] - 0s 12ms/step - loss: 1.0915 - val_loss: 1.0817 Epoch 29/280 14/14 [==============================] - 0s 13ms/step - loss: 1.0771 - val_loss: 1.0612 Epoch 30/280 14/14 [==============================] - 0s 12ms/step - loss: 1.0636 - val_loss: 1.0381 Epoch 31/280 14/14 [==============================] - 0s 16ms/step - loss: 1.0393 - val_loss: 1.0242 Epoch 32/280 14/14 [==============================] - 0s 14ms/step - loss: 1.0218 - val_loss: 1.0148 Epoch 33/280 14/14 [==============================] - 0s 12ms/step - loss: 1.0062 - val_loss: 0.9933 Epoch 34/280 14/14 [==============================] - 0s 14ms/step - loss: 0.9939 - val_loss: 0.9830 Epoch 35/280 14/14 [==============================] - 0s 13ms/step - loss: 0.9961 - val_loss: 0.9677 Epoch 36/280 14/14 [==============================] - 0s 13ms/step - loss: 0.9708 - val_loss: 0.9525 Epoch 37/280 14/14 [==============================] - 0s 15ms/step - loss: 0.9551 - val_loss: 0.9321 Epoch 38/280 14/14 [==============================] - 0s 13ms/step - loss: 0.9344 - val_loss: 0.9344 Epoch 39/280 14/14 [==============================] - 0s 13ms/step - loss: 0.9231 - val_loss: 0.9070 Epoch 40/280 14/14 [==============================] - 0s 14ms/step - loss: 0.9001 - val_loss: 0.8912 Epoch 41/280 14/14 [==============================] - 0s 13ms/step - loss: 0.9015 - val_loss: 0.8871 Epoch 42/280 14/14 [==============================] - 0s 14ms/step - loss: 0.8841 - val_loss: 0.8634 Epoch 43/280 14/14 [==============================] - 0s 14ms/step - loss: 0.8646 - val_loss: 0.8536 Epoch 44/280 14/14 [==============================] - 0s 13ms/step - loss: 0.8481 - val_loss: 0.8452 Epoch 45/280 14/14 [==============================] - 0s 13ms/step - loss: 0.8417 - val_loss: 0.8216 Epoch 46/280 14/14 [==============================] - 0s 13ms/step - loss: 0.8287 - val_loss: 0.8122 Epoch 47/280 14/14 [==============================] - 0s 14ms/step - loss: 0.8082 - val_loss: 0.7948 Epoch 48/280 14/14 [==============================] - 0s 14ms/step - loss: 0.7929 - val_loss: 0.7807 Epoch 49/280 14/14 [==============================] - 0s 13ms/step - loss: 0.7867 - val_loss: 0.7734 Epoch 50/280 14/14 [==============================] - 0s 14ms/step - loss: 0.7649 - val_loss: 0.7528 Epoch 51/280 14/14 [==============================] - 0s 14ms/step - loss: 0.7520 - val_loss: 0.7400 Epoch 52/280 14/14 [==============================] - 0s 14ms/step - loss: 0.7381 - val_loss: 0.7289 Epoch 53/280 14/14 [==============================] - 0s 12ms/step - loss: 0.7259 - val_loss: 0.7111 Epoch 54/280 14/14 [==============================] - 0s 12ms/step - loss: 0.7023 - val_loss: 0.7003 Epoch 55/280 14/14 [==============================] - 0s 13ms/step - loss: 0.6926 - val_loss: 0.6845 Epoch 56/280 14/14 [==============================] - 0s 15ms/step - loss: 0.6750 - val_loss: 0.6706 Epoch 57/280 14/14 [==============================] - 0s 13ms/step - loss: 0.6691 - val_loss: 0.6609 Epoch 58/280 14/14 [==============================] - 0s 12ms/step - loss: 0.6442 - val_loss: 0.6402 Epoch 59/280 14/14 [==============================] - 0s 13ms/step - loss: 0.6329 - val_loss: 0.6283 Epoch 60/280 14/14 [==============================] - 0s 14ms/step - loss: 0.6259 - val_loss: 0.6103 Epoch 61/280 14/14 [==============================] - 0s 14ms/step - loss: 0.6193 - val_loss: 0.6077 Epoch 62/280 14/14 [==============================] - 0s 14ms/step - loss: 0.5972 - val_loss: 0.5861 Epoch 63/280 14/14 [==============================] - 0s 15ms/step - loss: 0.5805 - val_loss: 0.5720 Epoch 64/280 14/14 [==============================] - 0s 15ms/step - loss: 0.5700 - val_loss: 0.5619 Epoch 65/280 14/14 [==============================] - 0s 15ms/step - loss: 0.5630 - val_loss: 0.5535 Epoch 66/280 14/14 [==============================] - 0s 13ms/step - loss: 0.5327 - val_loss: 0.5283 Epoch 67/280 14/14 [==============================] - 0s 13ms/step - loss: 0.5211 - val_loss: 0.5135 Epoch 68/280 14/14 [==============================] - 0s 14ms/step - loss: 0.5094 - val_loss: 0.5006 Epoch 69/280 14/14 [==============================] - 0s 14ms/step - loss: 0.4960 - val_loss: 0.4898 Epoch 70/280 14/14 [==============================] - 0s 13ms/step - loss: 0.4774 - val_loss: 0.4719 Epoch 71/280 14/14 [==============================] - 0s 14ms/step - loss: 0.4671 - val_loss: 0.4640 Epoch 72/280 14/14 [==============================] - 0s 14ms/step - loss: 0.4524 - val_loss: 0.4413 Epoch 73/280 14/14 [==============================] - 0s 13ms/step - loss: 0.4415 - val_loss: 0.4306 Epoch 74/280 14/14 [==============================] - 0s 14ms/step - loss: 0.4202 - val_loss: 0.4134 Epoch 75/280 14/14 [==============================] - 0s 13ms/step - loss: 0.4092 - val_loss: 0.4068 Epoch 76/280 14/14 [==============================] - 0s 17ms/step - loss: 0.4043 - val_loss: 0.3906 Epoch 77/280 14/14 [==============================] - 0s 14ms/step - loss: 0.3894 - val_loss: 0.3789 Epoch 78/280 14/14 [==============================] - 0s 13ms/step - loss: 0.3753 - val_loss: 0.3564 Epoch 79/280 14/14 [==============================] - 0s 14ms/step - loss: 0.3551 - val_loss: 0.3447 Epoch 80/280 14/14 [==============================] - 0s 15ms/step - loss: 0.3397 - val_loss: 0.3312 Epoch 81/280 14/14 [==============================] - 0s 14ms/step - loss: 0.3253 - val_loss: 0.3141 Epoch 82/280 14/14 [==============================] - 0s 14ms/step - loss: 0.3186 - val_loss: 0.3010 Epoch 83/280 14/14 [==============================] - 0s 13ms/step - loss: 0.2883 - val_loss: 0.2862 Epoch 84/280 14/14 [==============================] - 0s 14ms/step - loss: 0.2842 - val_loss: 0.2716 Epoch 85/280 14/14 [==============================] - 0s 14ms/step - loss: 0.2776 - val_loss: 0.2592 Epoch 86/280 14/14 [==============================] - 0s 14ms/step - loss: 0.2546 - val_loss: 0.2622 Epoch 87/280 14/14 [==============================] - 0s 12ms/step - loss: 0.2422 - val_loss: 0.2374 Epoch 88/280 14/14 [==============================] - 0s 13ms/step - loss: 0.2283 - val_loss: 0.2267 Epoch 89/280 14/14 [==============================] - 0s 13ms/step - loss: 0.2138 - val_loss: 0.2042 Epoch 90/280 14/14 [==============================] - 0s 13ms/step - loss: 0.1944 - val_loss: 0.1909 Epoch 91/280 14/14 [==============================] - 0s 14ms/step - loss: 0.1891 - val_loss: 0.1766 Epoch 92/280 14/14 [==============================] - 0s 12ms/step - loss: 0.1744 - val_loss: 0.1632 Epoch 93/280 14/14 [==============================] - 0s 14ms/step - loss: 0.1597 - val_loss: 0.1546 Epoch 94/280 14/14 [==============================] - 0s 13ms/step - loss: 0.1430 - val_loss: 0.1371 Epoch 95/280 14/14 [==============================] - 0s 13ms/step - loss: 0.1227 - val_loss: 0.1261 Epoch 96/280 14/14 [==============================] - 0s 15ms/step - loss: 0.1142 - val_loss: 0.1068 Epoch 97/280 14/14 [==============================] - 0s 13ms/step - loss: 0.1003 - val_loss: 0.1063 Epoch 98/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0965 - val_loss: 0.0777 Epoch 99/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0717 - val_loss: 0.0723 Epoch 100/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0618 - val_loss: 0.0501 Epoch 101/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0442 - val_loss: 0.0546 Epoch 102/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0280 - val_loss: 0.0273 Epoch 103/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0245 - val_loss: 0.0294 Epoch 104/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0245 - val_loss: 0.0293 Epoch 105/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0178 - val_loss: 0.0306 Epoch 106/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0260 - val_loss: 0.0284 Epoch 107/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0279 - val_loss: 0.0327 Epoch 108/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0304 Epoch 109/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0247 - val_loss: 0.0290 Epoch 110/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0223 - val_loss: 0.0399 Epoch 111/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0211 - val_loss: 0.0323 Epoch 112/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0295 Epoch 113/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0178 - val_loss: 0.0283 Epoch 114/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0212 - val_loss: 0.0283 Epoch 115/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0166 - val_loss: 0.0282 Epoch 116/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0282 Epoch 117/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0228 - val_loss: 0.0281 Epoch 118/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0173 - val_loss: 0.0280 Epoch 119/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0209 - val_loss: 0.0271 Epoch 120/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0162 - val_loss: 0.0280 Epoch 121/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0176 - val_loss: 0.0268 Epoch 122/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0171 - val_loss: 0.0270 Epoch 123/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0154 - val_loss: 0.0276 Epoch 124/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0270 Epoch 125/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0164 - val_loss: 0.0265 Epoch 126/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0200 - val_loss: 0.0276 Epoch 127/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0151 - val_loss: 0.0285 Epoch 128/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0254 - val_loss: 0.0280 Epoch 129/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0214 - val_loss: 0.0275 Epoch 130/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0189 - val_loss: 0.0267 Epoch 131/280 14/14 [==============================] - 0s 17ms/step - loss: 0.0224 - val_loss: 0.0264 Epoch 132/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0200 - val_loss: 0.0270 Epoch 133/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0146 - val_loss: 0.0270 Epoch 134/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0172 - val_loss: 0.0274 Epoch 135/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0130 - val_loss: 0.0279 Epoch 136/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0238 - val_loss: 0.0275 Epoch 137/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0135 - val_loss: 0.0274 Epoch 138/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0153 - val_loss: 0.0272 Epoch 139/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0141 - val_loss: 0.0271 Epoch 140/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0165 - val_loss: 0.0269 Epoch 141/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0269 Epoch 142/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0269 Epoch 143/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0233 - val_loss: 0.0269 Epoch 144/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0269 Epoch 145/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0148 - val_loss: 0.0268 Epoch 146/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0230 - val_loss: 0.0268 Epoch 147/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0144 - val_loss: 0.0268 Epoch 148/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 149/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0144 - val_loss: 0.0268 Epoch 150/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0177 - val_loss: 0.0268 Epoch 151/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0195 - val_loss: 0.0268 Epoch 152/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0146 - val_loss: 0.0268 Epoch 153/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0156 - val_loss: 0.0268 Epoch 154/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0144 - val_loss: 0.0268 Epoch 155/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0162 - val_loss: 0.0268 Epoch 156/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0173 - val_loss: 0.0268 Epoch 157/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0202 - val_loss: 0.0268 Epoch 158/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0167 - val_loss: 0.0268 Epoch 159/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0134 - val_loss: 0.0268 Epoch 160/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0165 - val_loss: 0.0268 Epoch 161/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0268 Epoch 162/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0185 - val_loss: 0.0268 Epoch 163/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0120 - val_loss: 0.0268 Epoch 164/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0282 - val_loss: 0.0268 Epoch 165/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0268 Epoch 166/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 167/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0152 - val_loss: 0.0268 Epoch 168/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0172 - val_loss: 0.0268 Epoch 169/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268 Epoch 170/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0148 - val_loss: 0.0268 Epoch 171/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 172/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0197 - val_loss: 0.0268 Epoch 173/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0194 - val_loss: 0.0268 Epoch 174/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0157 - val_loss: 0.0268 Epoch 175/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0148 - val_loss: 0.0268 Epoch 176/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0173 - val_loss: 0.0268 Epoch 177/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 178/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 179/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0124 - val_loss: 0.0268 Epoch 180/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0136 - val_loss: 0.0268 Epoch 181/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0186 - val_loss: 0.0268 Epoch 182/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0166 - val_loss: 0.0268 Epoch 183/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0184 - val_loss: 0.0268 Epoch 184/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0268 Epoch 185/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 186/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0191 - val_loss: 0.0268 Epoch 187/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0121 - val_loss: 0.0268 Epoch 188/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0142 - val_loss: 0.0268 Epoch 189/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0169 - val_loss: 0.0268 Epoch 190/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 191/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0194 - val_loss: 0.0268 Epoch 192/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0157 - val_loss: 0.0268 Epoch 193/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268 Epoch 194/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 195/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0306 - val_loss: 0.0268 Epoch 196/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 197/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0268 Epoch 198/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0185 - val_loss: 0.0268 Epoch 199/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268 Epoch 200/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0154 - val_loss: 0.0268 Epoch 201/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0188 - val_loss: 0.0268 Epoch 202/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0145 - val_loss: 0.0268 Epoch 203/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0207 - val_loss: 0.0268 Epoch 204/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 205/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0156 - val_loss: 0.0268 Epoch 206/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0145 - val_loss: 0.0268 Epoch 207/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 208/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0249 - val_loss: 0.0268 Epoch 209/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0219 - val_loss: 0.0268 Epoch 210/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0163 - val_loss: 0.0268 Epoch 211/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0196 - val_loss: 0.0268 Epoch 212/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0131 - val_loss: 0.0268 Epoch 213/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0161 - val_loss: 0.0268 Epoch 214/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0150 - val_loss: 0.0268 Epoch 215/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0203 - val_loss: 0.0268 Epoch 216/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0169 - val_loss: 0.0268 Epoch 217/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0139 - val_loss: 0.0268 Epoch 218/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0167 - val_loss: 0.0268 Epoch 219/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0224 - val_loss: 0.0268 Epoch 220/280 14/14 [==============================] - 0s 12ms/step - loss: 0.0218 - val_loss: 0.0268 Epoch 221/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0197 - val_loss: 0.0268 Epoch 222/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0184 - val_loss: 0.0268 Epoch 223/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0175 - val_loss: 0.0268 Epoch 224/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0168 - val_loss: 0.0268 Epoch 225/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 226/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0227 - val_loss: 0.0268 Epoch 227/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0210 - val_loss: 0.0268 Epoch 228/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0146 - val_loss: 0.0268 Epoch 229/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0146 - val_loss: 0.0268 Epoch 230/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0268 Epoch 231/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0211 - val_loss: 0.0268 Epoch 232/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0143 - val_loss: 0.0268 Epoch 233/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0161 - val_loss: 0.0268 Epoch 234/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0138 - val_loss: 0.0268 Epoch 235/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0192 - val_loss: 0.0268 Epoch 236/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0180 - val_loss: 0.0268 Epoch 237/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0134 - val_loss: 0.0268 Epoch 238/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0137 - val_loss: 0.0268 Epoch 239/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0193 - val_loss: 0.0268 Epoch 240/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0224 - val_loss: 0.0268 Epoch 241/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0130 - val_loss: 0.0268 Epoch 242/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0129 - val_loss: 0.0268 Epoch 243/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0219 - val_loss: 0.0268 Epoch 244/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0126 - val_loss: 0.0268 Epoch 245/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0190 - val_loss: 0.0268 Epoch 246/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0220 - val_loss: 0.0268 Epoch 247/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0268 Epoch 248/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 249/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0170 - val_loss: 0.0268 Epoch 250/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0188 - val_loss: 0.0268 Epoch 251/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0163 - val_loss: 0.0268 Epoch 252/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0131 - val_loss: 0.0268 Epoch 253/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0202 - val_loss: 0.0268 Epoch 254/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0187 - val_loss: 0.0268 Epoch 255/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0172 - val_loss: 0.0268 Epoch 256/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0143 - val_loss: 0.0268 Epoch 257/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0124 - val_loss: 0.0268 Epoch 258/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0128 - val_loss: 0.0268 Epoch 259/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0121 - val_loss: 0.0268 Epoch 260/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0262 - val_loss: 0.0268 Epoch 261/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0185 - val_loss: 0.0268 Epoch 262/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268 Epoch 263/280 14/14 [==============================] - 0s 11ms/step - loss: 0.0155 - val_loss: 0.0268 Epoch 264/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0158 - val_loss: 0.0268 Epoch 265/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0268 Epoch 266/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0194 - val_loss: 0.0268 Epoch 267/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0168 - val_loss: 0.0268 Epoch 268/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0198 - val_loss: 0.0268 Epoch 269/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0171 - val_loss: 0.0268 Epoch 270/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0206 - val_loss: 0.0268 Epoch 271/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0159 - val_loss: 0.0268 Epoch 272/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0140 - val_loss: 0.0268 Epoch 273/280 14/14 [==============================] - 0s 15ms/step - loss: 0.0156 - val_loss: 0.0268 Epoch 274/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0187 - val_loss: 0.0268 Epoch 275/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0223 - val_loss: 0.0268 Epoch 276/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0179 - val_loss: 0.0268 Epoch 277/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0160 - val_loss: 0.0268 Epoch 278/280 14/14 [==============================] - 0s 14ms/step - loss: 0.0174 - val_loss: 0.0268 Epoch 279/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0180 - val_loss: 0.0268 Epoch 280/280 14/14 [==============================] - 0s 13ms/step - loss: 0.0147 - val_loss: 0.0268
In [36]:
rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))
In [37]:
[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]
Out[37]:
[0.9991102134329165, 0.9990587871483716, 0.9990097447684705]
In [38]:
pred_rst = pd.DataFrame.from_records(np.squeeze(np.asarray(rst), axis=2).T, columns=out_cols)
In [39]:
real_rst = test[out_cols].copy()
In [40]:
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]
In [41]:
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'])
In [42]:
real_rst.columns
Out[42]:
Index(['比表面积', '总孔体积', '微孔体积'], dtype='object')
In [43]:
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,)
In [44]:
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')
COL: 比表面积, MSE: 1.41E-01,RMSE: 0.3751,MAPE: 4.15 %,MAE: 0.2805,R_2: 0.5069 COL: 总孔体积, MSE: 3.31E-02,RMSE: 0.1819,MAPE: 21.86 %,MAE: 0.1473,R_2: 0.7538 COL: 微孔体积, MSE: 2.67E-02,RMSE: 0.1635,MAPE: 25.290000000000003 %,MAE: 0.1261,R_2: 0.6851
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