Respan Dataset Explorer
Select one behavior. Every returned turn has one binary label: Present or Absent. Source: final dense boolean release.
5,167,182physical rows
86shards
0.00%qualified row coverage
0.00%qualified cell coverage
turns-00016.parquet:51250
a9568dd6487e34d2e015ee25
turn 10/16gpt-4-1106-previewChineseJapan67 words
degenerate_repetitionAbsentFinal dense release
USER
def get_model(x_shape=5, y_shape=40, learning_rate=1e-3):
model = Sequential([
tf.keras.layers.InputLayer(input_shape=(x_shape, y_shape)),
LSTM(128, return_sequences=True, kernel_regularizer=l2(0.001)),
Dropout(0.2),
BatchNormalization(),
LSTM(64, return_sequences=False),
Dropout(0.2),
BatchNormalization(),
Dense(256, activation='relu', kernel_regularizer=l2(0.001)),
Dropout(0.2),
Dense(1, activation='sigmoid'),
])
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
loss='binary_crossentropy',
metrics=['accuracy', 'Precision', 'Recall']
)
early_stopping = EarlyStopping(
monitor='val_loss',
mode='min',
verbose=1,
patience=20,
restore_best_weights=True
)
reduce_lr = ReduceLROnPlateau(
monitor='val_loss',
mode='min',
factor=0.5,
patience=4,
min_lr=1e-6,
verbose=1
)
return model, [early_stopping, reduce_lr]
我手动设置了1e-4的学习率ASSISTANT
根据您提供的代码，您已经定义了一个循环神经网络模型，它包含两层LSTM层，中间穿插了Dropout和BatchNormalization层，以及一个全连接层作为输出。您使用的损失函数是二元交叉熵（binary_crossentropy），这是处理二分类问题的标准选择。
同时，您设定了学习率为`1e-3`（即0.001），这是一个常用的初始学习率值。然而，您评论中提到手动设置了`1e-4`的学习率，这可能是您先前实验中的设置，但在提供的代码段里，传递给优化器（Adam）的是`1e-3`。
另外，您已经添加了两个用于训练中的回调函数（callbacks）：
1. **EarlyStopping**：如果在20个epochs内验证集上的损失没有改善，训练将提前停止（early stopping）。这有助于防止过拟合。
2. **ReduceLROnPlateau**：当验证集上的损失在连续4个epochs内没有改善时，学习率将减半，直到降到`1e-6`。这有助于在训练期间微调学习率，当损失停止下降时找到新的梯度下降路径。
这两种方法配合使用可以有效地管理训练过程，防止过拟合并在必要时调整学习率，以促进模型在验证集上的性能改善。
如果在训练过程中发现模型的性能还有优化空间，并且您确实希望设置一个更小的学习率（如`1e-4`），您只需更新`get_model`函数定义中的`learning_rate`参数即可。此外，请记得在实际调用`get_model`时传入您期望的学习率值。
turns-00016.parquet:51251
e5e9f54920fd1c3052058aa3
turn 11/16gpt-4-1106-previewChineseJapan14 words
degenerate_repetitionAbsentFinal dense release
USER
这是否说明我的数据在一定程度上是收敛的，是存在内部规律的
ASSISTANT
从您的训练日志来看，模型的验证准确率随着时间的推移逐渐提高，这是数据收敛和模型学习内部规律的迹象。具体来说，如果准确率、精确度、召回率等指标在训练过程中稳步提高，并且在验证集上也表现出相同的趋势，那么这通常表明如下几点：
1. **数据一致性**：数据集中的特征与目标变量之间存在一定程度的关联性，使得模型能够通过学习这些特征来预测目标变量。
2. **模型有效性**：使用的模型结构和训练策略对于捕捉数据的内在规律是有效的。
3. **收敛性**：所选择的损失函数、优化算法以及超参数设置使得模型能在每次迭代后逐步减小错误并提高性能指标。
4. **泛化能力**：早停（EarlyStopping）和学习率下降（ReduceLROnPlateau）等策略有助于模型在没有看到的验证集数据上保持好的性能，从而提高模型的泛化能力。
5. **内部规律**：模型不仅仅是记忆训练数据，而是确实学到了数据中的一般规律，可以用来对未见过的数据做出准确的预测。
虽然这些迹象表明模型在训练中表现良好，但需要注意的是，仅凭准确率这一个指标不能完全证明模型的泛化能力。最终评估模型性能时，要考虑各种度量指标（例如准确率、精确度、召回率、F1得分甚至是混淆矩阵）以及在一个独立的测试集上的表现。
此外，即使模型在验证集上表现良好，也仍可能存在一些问题，例如过拟合、数据泄露或类别不平衡。因此，验证集上的性能应结合对模型行为的全面理解，并在必要时对模型或数据进行调整以进一步提高性能。在最后阶段，对于一个独立的测试集（模型从未见过的部分数据）进行评估，这可以提供对模型实际泛化能力的最准确评估。
turns-00016.parquet:51252
6152beb5fa0700ff7dd79541
turn 12/16gpt-4-1106-previewChineseJapan5999 words
degenerate_repetitionAbsentFinal dense release
USER
Epoch 1/1000
7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04
Epoch 2/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04
Epoch 3/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04
Epoch 4/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04
Epoch 5/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04
Epoch 6/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04
Epoch 7/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04
Epoch 8/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04
Epoch 9/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04
Epoch 10/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04
Epoch 11/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04
Epoch 12/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04
Epoch 13/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04
Epoch 14/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04
Epoch 15/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04
Epoch 16/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 17/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04
Epoch 18/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04
Epoch 19/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04
Epoch 20/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04
Epoch 21/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04
Epoch 22/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04
Epoch 23/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04
Epoch 24/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04
Epoch 25/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04
Epoch 26/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04
Epoch 27/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04
Epoch 28/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04
Epoch 29/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04
Epoch 30/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04
Epoch 31/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04
Epoch 32/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04
Epoch 33/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04
Epoch 34/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04
Epoch 35/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04
Epoch 36/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04
Epoch 37/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04
Epoch 38/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04
Epoch 39/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04
Epoch 40/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 41/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04
Epoch 42/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04
Epoch 43/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04
Epoch 44/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04
Epoch 45/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04
Epoch 46/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04
Epoch 47/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04
Epoch 48/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04
Epoch 49/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04
Epoch 50/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04
Epoch 51/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04
Epoch 52/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04
Epoch 53/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04
Epoch 54/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04
Epoch 55/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04
Epoch 56/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04
Epoch 57/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04
Epoch 58/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04
Epoch 59/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04
Epoch 60/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04
Epoch 61/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 62/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04
Epoch 63/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04
Epoch 64/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 65/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04
Epoch 66/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04
Epoch 67/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04
Epoch 68/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04
Epoch 69/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04
Epoch 70/1000
7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04
Epoch 71/1000
7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 72/1000
7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04
Epoch 73/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 74/1000
7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04
Epoch 75/1000
7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 76/1000
7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04
Epoch 77/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04
Epoch 78/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04
Epoch 79/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04
Epoch 80/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04
Epoch 81/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04
Epoch 82/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 83/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 84/1000
7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04
Epoch 85/1000
7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04
Epoch 86/1000
7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04
Epoch 87/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04
Epoch 88/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04
Epoch 89/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04
Epoch 90/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04
Epoch 91/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04
Epoch 92/1000
7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04
Epoch 93/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 94/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04
Epoch 95/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04
Epoch 96/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04
Epoch 97/1000
7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04
Epoch 98/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 99/1000
7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04
Epoch 100/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04
Epoch 101/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 102/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04
Epoch 103/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04
Epoch 104/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04
Epoch 105/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04
Epoch 106/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04
Epoch 107/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04
Epoch 108/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04
Epoch 109/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04
Epoch 110/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 111/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04
Epoch 112/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04
Epoch 113/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04
Epoch 114/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04
Epoch 115/1000
7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04
Epoch 116/1000
7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04
Epoch 117/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04
Epoch 118/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04
Epoch 119/1000
7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04
Epoch 120/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04
Epoch 121/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04
Epoch 122/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 123/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04
Epoch 124/1000
7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04
Epoch 125/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04
Epoch 126/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 127/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04
Epoch 128/1000
7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04
Epoch 129/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04
Epoch 130/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04
Epoch 131/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04
Epoch 132/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04
Epoch 133/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04
Epoch 134/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04
Epoch 135/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6172 - accuracy: 0.6562 - precision: 0.6593 - recall: 0.6463 - val_loss: 0.5896 - val_accuracy: 0.6841 - val_precision: 0.6874 - val_recall: 0.6752 - lr: 1.0000e-04
Epoch 136/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6167 - accuracy: 0.6561 - precision: 0.6587 - recall: 0.6479 - val_loss: 0.5908 - val_accuracy: 0.6823 - val_precision: 0.6815 - val_recall: 0.6846 - lr: 1.0000e-04
Epoch 137/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6158 - accuracy: 0.6578 - precision: 0.6600 - recall: 0.6511 - val_loss: 0.5901 - val_accuracy: 0.6834 - val_precision: 0.6805 - val_recall: 0.6913 - lr: 1.0000e-04
Epoch 138/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6585 - precision: 0.6601 - recall: 0.6537 - val_loss: 0.5887 - val_accuracy: 0.6850 - val_precision: 0.6860 - val_recall: 0.6824 - lr: 1.0000e-04
Epoch 139/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6592 - precision: 0.6616 - recall: 0.6517 - val_loss: 0.5871 - val_accuracy: 0.6859 - val_precision: 0.6851 - val_recall: 0.6881 - lr: 1.0000e-04
Epoch 140/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6151 - accuracy: 0.6579 - precision: 0.6602 - recall: 0.6507 - val_loss: 0.5879 - val_accuracy: 0.6862 - val_precision: 0.6877 - val_recall: 0.6823 - lr: 1.0000e-04
Epoch 141/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6151 - accuracy: 0.6575 - precision: 0.6602 - recall: 0.6489 - val_loss: 0.5878 - val_accuracy: 0.6861 - val_precision: 0.6831 - val_recall: 0.6944 - lr: 1.0000e-04
Epoch 142/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6136 - accuracy: 0.6597 - precision: 0.6621 - recall: 0.6522 - val_loss: 0.5880 - val_accuracy: 0.6865 - val_precision: 0.6851 - val_recall: 0.6905 - lr: 1.0000e-04
Epoch 143/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6139 - accuracy: 0.6590 - precision: 0.6612 - recall: 0.6523 - val_loss: 0.5855 - val_accuracy: 0.6880 - val_precision: 0.6903 - val_recall: 0.6819 - lr: 1.0000e-04
Epoch 144/1000
7680/7680 [==============================] - 169s 22ms/step - loss: 0.6136 - accuracy: 0.6598 - precision: 0.6621 - recall: 0.6525 - val_loss: 0.5848 - val_accuracy: 0.6885 - val_precision: 0.6861 - val_recall: 0.6949 - lr: 1.0000e-04
Epoch 145/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6131 - accuracy: 0.6607 - precision: 0.6630 - recall: 0.6535 - val_loss: 0.5849 - val_accuracy: 0.6887 - val_precision: 0.6861 - val_recall: 0.6957 - lr: 1.0000e-04
Epoch 146/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6126 - accuracy: 0.6611 - precision: 0.6628 - recall: 0.6560 - val_loss: 0.5849 - val_accuracy: 0.6883 - val_precision: 0.6889 - val_recall: 0.6867 - lr: 1.0000e-04
Epoch 147/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6124 - accuracy: 0.6608 - precision: 0.6637 - recall: 0.6518 - val_loss: 0.5844 - val_accuracy: 0.6897 - val_precision: 0.6922 - val_recall: 0.6833 - lr: 1.0000e-04
Epoch 148/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6129 - accuracy: 0.6609 - precision: 0.6628 - recall: 0.6550 - val_loss: 0.5831 - val_accuracy: 0.6904 - val_precision: 0.6922 - val_recall: 0.6855 - lr: 1.0000e-04
Epoch 149/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6123 - accuracy: 0.6614 - precision: 0.6640 - recall: 0.6534 - val_loss: 0.5847 - val_accuracy: 0.6897 - val_precision: 0.6924 - val_recall: 0.6829 - lr: 1.0000e-04
Epoch 150/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6116 - accuracy: 0.6614 - precision: 0.6638 - recall: 0.6540 - val_loss: 0.5838 - val_accuracy: 0.6892 - val_precision: 0.6891 - val_recall: 0.6895 - lr: 1.0000e-04
Epoch 151/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6117 - accuracy: 0.6620 - precision: 0.6646 - recall: 0.6542 - val_loss: 0.5831 - val_accuracy: 0.6900 - val_precision: 0.6888 - val_recall: 0.6934 - lr: 1.0000e-04
Epoch 152/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6116 - accuracy: 0.6622 - precision: 0.6649 - recall: 0.6542 - val_loss: 0.5817 - val_accuracy: 0.6910 - val_precision: 0.6903 - val_recall: 0.6928 - lr: 1.0000e-04
Epoch 153/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6109 - accuracy: 0.6626 - precision: 0.6651 - recall: 0.6549 - val_loss: 0.5836 - val_accuracy: 0.6913 - val_precision: 0.6903 - val_recall: 0.6940 - lr: 1.0000e-04
Epoch 154/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6106 - accuracy: 0.6633 - precision: 0.6658 - recall: 0.6560 - val_loss: 0.5815 - val_accuracy: 0.6921 - val_precision: 0.6902 - val_recall: 0.6971 - lr: 1.0000e-04
Epoch 155/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6095 - accuracy: 0.6636 - precision: 0.6661 - recall: 0.6561 - val_loss: 0.5820 - val_accuracy: 0.6919 - val_precision: 0.6900 - val_recall: 0.6968 - lr: 1.0000e-04
Epoch 156/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6096 - accuracy: 0.6637 - precision: 0.6669 - recall: 0.6542 - val_loss: 0.5822 - val_accuracy: 0.6919 - val_precision: 0.6888 - val_recall: 0.7000 - lr: 1.0000e-04
Epoch 157/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6102 - accuracy: 0.6636 - precision: 0.6662 - recall: 0.6557 - val_loss: 0.5802 - val_accuracy: 0.6942 - val_precision: 0.6971 - val_recall: 0.6867 - lr: 1.0000e-04
Epoch 158/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6097 - accuracy: 0.6641 - precision: 0.6672 - recall: 0.6549 - val_loss: 0.5801 - val_accuracy: 0.6942 - val_precision: 0.7005 - val_recall: 0.6783 - lr: 1.0000e-04
Epoch 159/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6081 - accuracy: 0.6653 - precision: 0.6674 - recall: 0.6590 - val_loss: 0.5774 - val_accuracy: 0.6965 - val_precision: 0.6994 - val_recall: 0.6894 - lr: 1.0000e-04
Epoch 160/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6085 - accuracy: 0.6654 - precision: 0.6679 - recall: 0.6580 - val_loss: 0.5786 - val_accuracy: 0.6944 - val_precision: 0.6928 - val_recall: 0.6985 - lr: 1.0000e-04
Epoch 161/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6081 - accuracy: 0.6654 - precision: 0.6680 - recall: 0.6578 - val_loss: 0.5767 - val_accuracy: 0.6969 - val_precision: 0.6999 - val_recall: 0.6892 - lr: 1.0000e-04
Epoch 162/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6080 - accuracy: 0.6660 - precision: 0.6682 - recall: 0.6595 - val_loss: 0.5775 - val_accuracy: 0.6954 - val_precision: 0.6939 - val_recall: 0.6990 - lr: 1.0000e-04
Epoch 163/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6072 - accuracy: 0.6670 - precision: 0.6698 - recall: 0.6589 - val_loss: 0.5776 - val_accuracy: 0.6963 - val_precision: 0.6979 - val_recall: 0.6921 - lr: 1.0000e-04
Epoch 164/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6070 - accuracy: 0.6663 - precision: 0.6687 - recall: 0.6592 - val_loss: 0.5770 - val_accuracy: 0.6964 - val_precision: 0.6986 - val_recall: 0.6908 - lr: 1.0000e-04
Epoch 165/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6067 - accuracy: 0.6665 - precision: 0.6691 - recall: 0.6587 - val_loss: 0.5766 - val_accuracy: 0.6964 - val_precision: 0.6936 - val_recall: 0.7036 - lr: 1.0000e-04
Epoch 166/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6067 - accuracy: 0.6671 - precision: 0.6687 - recall: 0.6625 - val_loss: 0.5762 - val_accuracy: 0.6971 - val_precision: 0.6944 - val_recall: 0.7039 - lr: 1.0000e-04
Epoch 167/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6057 - accuracy: 0.6679 - precision: 0.6695 - recall: 0.6632 - val_loss: 0.5766 - val_accuracy: 0.6968 - val_precision: 0.6949 - val_recall: 0.7018 - lr: 1.0000e-04
Epoch 168/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6067 - accuracy: 0.6676 - precision: 0.6702 - recall: 0.6600 - val_loss: 0.5752 - val_accuracy: 0.6979 - val_precision: 0.6996 - val_recall: 0.6936 - lr: 1.0000e-04
Epoch 169/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6064 - accuracy: 0.6674 - precision: 0.6701 - recall: 0.6593 - val_loss: 0.5750 - val_accuracy: 0.6986 - val_precision: 0.6993 - val_recall: 0.6969 - lr: 1.0000e-04
Epoch 170/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6049 - accuracy: 0.6690 - precision: 0.6712 - recall: 0.6627 - val_loss: 0.5735 - val_accuracy: 0.6991 - val_precision: 0.7003 - val_recall: 0.6960 - lr: 1.0000e-04
Epoch 171/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6053 - accuracy: 0.6684 - precision: 0.6706 - recall: 0.6619 - val_loss: 0.5749 - val_accuracy: 0.6985 - val_precision: 0.6969 - val_recall: 0.7024 - lr: 1.0000e-04
Epoch 172/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6048 - accuracy: 0.6696 - precision: 0.6727 - recall: 0.6605 - val_loss: 0.5738 - val_accuracy: 0.6989 - val_precision: 0.6994 - val_recall: 0.6976 - lr: 1.0000e-04
Epoch 173/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6054 - accuracy: 0.6688 - precision: 0.6713 - recall: 0.6614 - val_loss: 0.5721 - val_accuracy: 0.7014 - val_precision: 0.6991 - val_recall: 0.7071 - lr: 1.0000e-04
Epoch 174/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6044 - accuracy: 0.6688 - precision: 0.6716 - recall: 0.6607 - val_loss: 0.5716 - val_accuracy: 0.7013 - val_precision: 0.7028 - val_recall: 0.6976 - lr: 1.0000e-04
Epoch 175/1000
7680/7680 [==============================] - 158s 21ms/step - loss: 0.6044 - accuracy: 0.6694 - precision: 0.6727 - recall: 0.6598 - val_loss: 0.5725 - val_accuracy: 0.7018 - val_precision: 0.7015 - val_recall: 0.7023 - lr: 1.0000e-04
Epoch 176/1000
1341/7680 [====>.........................] - ETA: 1:39 - loss: 0.6042 - accuracy: 0.6689 - precision: 0.6686 - recall: 0.6615
是不是快遇到瓶颈了
ASSISTANT
从您提供的输出来看，模型的验证准确率确实已经达到了70%以上，且仍在继续缓慢提高。这表明模型仍然在从数据学习，并在每个epoch后继续提升性能。但是，增量变小可能意味着接近其性能的上限或者在训练中遭遇瓶颈。
此时，您可以关注几个指标来判断是否正在接近瓶颈：
1. **验证损失（Validation Loss）**：如果验证损失不再显著下降，或者开始波动甚至上升，这可能是一个迹象显示模型正在过拟合或已经达到其潜在的性能上限。
2. **训练损失（Training Loss）**：如果训练损失继续降低但验证准确率和损失不再改善，这可能意味着模型在训练数据上过拟合。
3. **准确率提升（Accuracy Increases）**：如果准确率的提升非常微小或停滞不前，这可能表明模型已经难以从训练中获取更多的信息来进一步提升性能。
4. **早停（Early Stopping）**：您的模型训练中已经使用了早停策略。如果该策略被触发，表明模型在验证集上已经连续多个epochs没有改善，这也是一个明显的瓶颈迹象。
记得，即便训练达到瓶颈，这并不意味着模型一定是最优的。在实际应用中，我们还需要测试集来评估最终模型的性能。
如果您怀疑模型已经接近瓶颈，可以尝试以下几种策略来尝试进一步提升性能：
- **调整模型结构**：添加更多的层次，或者尝试不同类型的网络架构。
- **更复杂的特征工程**：添加更多特征或尝试不同的数据预处理策略。
- **超参数调优**：尝试不同的学习率、batch大小或优化器。
- **正则化**：添加dropout或正则化项可以帮助模型泛化，并有可能改善验证集上的性能。
- **数据增强**：如果适用，可以通过数据增强技术扩充训练集，提高模型的泛化能力。
- **集成方法**：将多个模型的预测结果组合起来可能会提高整体的性能。
最后，达到瓶颈不总是坏事，它可能表示模型已经从数据中学到了尽可能多的信息。在这一阶段，重要的是要通过交叉验证和独立测试来确保模型泛化能力的可靠性。
turns-00016.parquet:51253
a67caa677ac037edce44e253
turn 13/16gpt-4-1106-previewChineseJapan24 words
degenerate_repetitionAbsentFinal dense release
USER
性能提升的越来越慢，我认为要想准确率提高到80%，至少要三天吧
ASSISTANT
性能提升的速度减缓是常见的现象，尤其是当一个模型已经从数据中学到相当多信息时。在这种情况下，获取额外性能的难度会越来越大。您提到的准确率提升到80%还需要三天可能有几个含义：
1. **经验估计**：基于您对目前训练进度的观察和经验，您估计达到目标性能需要的时间。这种估计可以作为调整训练计划的依据。
2. **时间与性能权衡**：根据模型当前的学习速度，您可以决定是否值得投入更多的时间来追求更高的准确率。在某些应用中，从60%到70%的提升可能比从70%到80%的提升要容易得多。
3. **考虑早停**：既然您在模型中实现了早停（EarlyStopping），如果验证集上的性能在一定时间内没有改善，模型就会停止训练。这可以防止过度的时间投入，特别是在模型性能几乎不再提升时。
4. **曲线趋于平稳**：随着模型接近其性能极限，性能曲线通常会逐渐趋于平稳，并且每个额外的改进都需要更精细的调整和更长的时间。
5. **硬件限制**：在一台机器上，硬件（如GPU性能）将限制训练速度。可能需要更多的计算资源或更高效的模型来达到理想的性能。
如果时间是一个关键因素，并且您希望尽可能高效地使用时间来提升模型性能，那么您可能需要考虑采取额外的措施。比如：
- 使用更高效的训练技术，如更改优化器、调整学习率策略。
- 尝试更换不同的模型架构或增加模型的容量。
- 进行超参数调整来找到更有效的模型设置。
- 在多台机器上执行分布式训练或者尝试使用更强大的计算资源。
- 考虑模型剪枝或知识蒸馏以提高模型效率，在有限的资源内获得更好的性能。
请注意，持续的训练不一定能保证性能将达到80%，取决于模型架构和数据本身的限制。如果尽管尝试了各种方法但性能仍旧难以突破，可能需要重新考虑数据、特征工程、或者是否有新的模型架构可以尝试。
turns-00016.parquet:51254
ac9702f7a9433c93e5060eae
turn 14/16gpt-4-1106-previewChineseJapan55 words
degenerate_repetitionAbsentFinal dense release
ASSISTANT
早停（EarlyStopping）机制的目的是为了防止模型过拟合，同时节约训练时间。当模型在一定数量的 epochs 后没有在验证集上显示出性能改进时，训练会提前停止。EarlyStopping 回调函数有几个关键参数用于配置它的行为：
1. `monitor`：要监视的性能指标。通常是 `'val_loss'` 或 `'val_accuracy'`。
2. `mode`：确定当 `monitor` 改善时，早停触发的条件 。`'min'` 代表监视指标停止下降时触发，而 `'max'` 代表监视指标停止上升时触发。
3. `patience`：模型在停止提升后需要等待的 epochs 数量；这就是所谓的“耐心”。
4. `verbose`：是否打印早停消息。
5. `restore_best_weights`：是否在训练结束时恢复到效果最好的时期的权重。
在您的示例中，`EarlyStopping` 被设置成20个 epochs 的 `patience`，但您已经训练了181个 epochs。这可能是因为：
- 您设置的 `monitor` 参数的确有微小的进步，但是进步幅度非常小，所以早停没有被触发，模型仍在继续训练。
- `EarlyStopping` 参数可能不正确，或者在模型的训练函数中没有被正确实施。
若想要让模型在没有明显进步的情况下更早地停止训练，您可能需要重新检查 `EarlyStopping` 的设置，确保 `monitor` 正确配置，切换 `mode` 参数（如果有必要的话），并可能减小 `patience` 参数的值。同时，您还可以通过将 `verbose` 参数设置为1来获得有关早停行为的更多信息。
如果实际上您不想让模型训练这么久，您可以尝试减小 `patience` 的值或者改变 `monitor` 的阈值，来让早停回调在您希望的时间点触发早停。如果模型的改进微小到不符合业务需求，那么即便训练继续，也可能不会带来实质性的性能提升，这时应该考虑更改模型或训练策略。
turns-00016.parquet:51255
3f743d2e0fe0ffa7a731ede5
turn 15/16gpt-4-1106-previewChineseJapan6421 words
degenerate_repetitionAbsentFinal dense release
USER
Epoch 1/1000
7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04
Epoch 2/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04
Epoch 3/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04
Epoch 4/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04
Epoch 5/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04
Epoch 6/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04
Epoch 7/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04
Epoch 8/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04
Epoch 9/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04
Epoch 10/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04
Epoch 11/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04
Epoch 12/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04
Epoch 13/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04
Epoch 14/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04
Epoch 15/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04
Epoch 16/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 17/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04
Epoch 18/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04
Epoch 19/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04
Epoch 20/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04
Epoch 21/1000
7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04
Epoch 22/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04
Epoch 23/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04
Epoch 24/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04
Epoch 25/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04
Epoch 26/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04
Epoch 27/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04
Epoch 28/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04
Epoch 29/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04
Epoch 30/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04
Epoch 31/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04
Epoch 32/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04
Epoch 33/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04
Epoch 34/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04
Epoch 35/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04
Epoch 36/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04
Epoch 37/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04
Epoch 38/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04
Epoch 39/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04
Epoch 40/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04
Epoch 41/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04
Epoch 42/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04
Epoch 43/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04
Epoch 44/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04
Epoch 45/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04
Epoch 46/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04
Epoch 47/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04
Epoch 48/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04
Epoch 49/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04
Epoch 50/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04
Epoch 51/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04
Epoch 52/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04
Epoch 53/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04
Epoch 54/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04
Epoch 55/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04
Epoch 56/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04
Epoch 57/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04
Epoch 58/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04
Epoch 59/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04
Epoch 60/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04
Epoch 61/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 62/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04
Epoch 63/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04
Epoch 64/1000
7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 65/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04
Epoch 66/1000
7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04
Epoch 67/1000
7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04
Epoch 68/1000
7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04
Epoch 69/1000
7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04
Epoch 70/1000
7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04
Epoch 71/1000
7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04
Epoch 72/1000
7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04
Epoch 73/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 74/1000
7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04
Epoch 75/1000
7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 76/1000
7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04
Epoch 77/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04
Epoch 78/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04
Epoch 79/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04
Epoch 80/1000
7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04
Epoch 81/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04
Epoch 82/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 83/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04
Epoch 84/1000
7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04
Epoch 85/1000
7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04
Epoch 86/1000
7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04
Epoch 87/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04
Epoch 88/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04
Epoch 89/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04
Epoch 90/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04
Epoch 91/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04
Epoch 92/1000
7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04
Epoch 93/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 94/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04
Epoch 95/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04
Epoch 96/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04
Epoch 97/1000
7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04
Epoch 98/1000
7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04
Epoch 99/1000
7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04
Epoch 100/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04
Epoch 101/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04
Epoch 102/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04
Epoch 103/1000
7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04
Epoch 104/1000
7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04
Epoch 105/1000
7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04
Epoch 106/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04
Epoch 107/1000
7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04
Epoch 108/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04
Epoch 109/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04
Epoch 110/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04
Epoch 111/1000
7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04
Epoch 112/1000
7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04
Epoch 113/1000
7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04
Epoch 114/1000
7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04
Epoch 115/1000
7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04
Epoch 116/1000
7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04
Epoch 117/1000
7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04
Epoch 118/1000
7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04
Epoch 119/1000
7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04
Epoch 120/1000
7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04
Epoch 121/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04
Epoch 122/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 123/1000
7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04
Epoch 124/1000
7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04
Epoch 125/1000
7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04
Epoch 126/1000
7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04
Epoch 127/1000
7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04
Epoch 128/1000
7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04
Epoch 129/1000
7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04
Epoch 130/1000
7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04
Epoch 131/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04
Epoch 132/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04
Epoch 133/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04
Epoch 134/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04
Epoch 135/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6172 - accuracy: 0.6562 - precision: 0.6593 - recall: 0.6463 - val_loss: 0.5896 - val_accuracy: 0.6841 - val_precision: 0.6874 - val_recall: 0.6752 - lr: 1.0000e-04
Epoch 136/1000
7680/7680 [==============================] - 172s 22ms/step - loss: 0.6167 - accuracy: 0.6561 - precision: 0.6587 - recall: 0.6479 - val_loss: 0.5908 - val_accuracy: 0.6823 - val_precision: 0.6815 - val_recall: 0.6846 - lr: 1.0000e-04
Epoch 137/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6158 - accuracy: 0.6578 - precision: 0.6600 - recall: 0.6511 - val_loss: 0.5901 - val_accuracy: 0.6834 - val_precision: 0.6805 - val_recall: 0.6913 - lr: 1.0000e-04
Epoch 138/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6585 - precision: 0.6601 - recall: 0.6537 - val_loss: 0.5887 - val_accuracy: 0.6850 - val_precision: 0.6860 - val_recall: 0.6824 - lr: 1.0000e-04
Epoch 139/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6592 - precision: 0.6616 - recall: 0.6517 - val_loss: 0.5871 - val_accuracy: 0.6859 - val_precision: 0.6851 - val_recall: 0.6881 - lr: 1.0000e-04
Epoch 140/1000
7680/7680 [==============================] - 171s 22ms/step - loss: 0.6151 - accuracy: 0.6579 - precision: 0.6602 - recall: 0.6507 - val_loss: 0.5879 - val_accuracy: 0.6862 - val_precision: 0.6877 - val_recall: 0.6823 - lr: 1.0000e-04
Epoch 141/1000
7680/7680 [==============================] - 165s 22ms/step - loss: 0.6151 - accuracy: 0.6575 - precision: 0.6602 - recall: 0.6489 - val_loss: 0.5878 - val_accuracy: 0.6861 - val_precision: 0.6831 - val_recall: 0.6944 - lr: 1.0000e-04
Epoch 142/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6136 - accuracy: 0.6597 - precision: 0.6621 - recall: 0.6522 - val_loss: 0.5880 - val_accuracy: 0.6865 - val_precision: 0.6851 - val_recall: 0.6905 - lr: 1.0000e-04
Epoch 143/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6139 - accuracy: 0.6590 - precision: 0.6612 - recall: 0.6523 - val_loss: 0.5855 - val_accuracy: 0.6880 - val_precision: 0.6903 - val_recall: 0.6819 - lr: 1.0000e-04
Epoch 144/1000
7680/7680 [==============================] - 169s 22ms/step - loss: 0.6136 - accuracy: 0.6598 - precision: 0.6621 - recall: 0.6525 - val_loss: 0.5848 - val_accuracy: 0.6885 - val_precision: 0.6861 - val_recall: 0.6949 - lr: 1.0000e-04
Epoch 145/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6131 - accuracy: 0.6607 - precision: 0.6630 - recall: 0.6535 - val_loss: 0.5849 - val_accuracy: 0.6887 - val_precision: 0.6861 - val_recall: 0.6957 - lr: 1.0000e-04
Epoch 146/1000
7680/7680 [==============================] - 173s 23ms/step - loss: 0.6126 - accuracy: 0.6611 - precision: 0.6628 - recall: 0.6560 - val_loss: 0.5849 - val_accuracy: 0.6883 - val_precision: 0.6889 - val_recall: 0.6867 - lr: 1.0000e-04
Epoch 147/1000
7680/7680 [==============================] - 174s 23ms/step - loss: 0.6124 - accuracy: 0.6608 - precision: 0.6637 - recall: 0.6518 - val_loss: 0.5844 - val_accuracy: 0.6897 - val_precision: 0.6922 - val_recall: 0.6833 - lr: 1.0000e-04
Epoch 148/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6129 - accuracy: 0.6609 - precision: 0.6628 - recall: 0.6550 - val_loss: 0.5831 - val_accuracy: 0.6904 - val_precision: 0.6922 - val_recall: 0.6855 - lr: 1.0000e-04
Epoch 149/1000
7680/7680 [==============================] - 165s 21ms/step - loss: 0.6123 - accuracy: 0.6614 - precision: 0.6640 - recall: 0.6534 - val_loss: 0.5847 - val_accuracy: 0.6897 - val_precision: 0.6924 - val_recall: 0.6829 - lr: 1.0000e-04
Epoch 150/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6116 - accuracy: 0.6614 - precision: 0.6638 - recall: 0.6540 - val_loss: 0.5838 - val_accuracy: 0.6892 - val_precision: 0.6891 - val_recall: 0.6895 - lr: 1.0000e-04
Epoch 151/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6117 - accuracy: 0.6620 - precision: 0.6646 - recall: 0.6542 - val_loss: 0.5831 - val_accuracy: 0.6900 - val_precision: 0.6888 - val_recall: 0.6934 - lr: 1.0000e-04
Epoch 152/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6116 - accuracy: 0.6622 - precision: 0.6649 - recall: 0.6542 - val_loss: 0.5817 - val_accuracy: 0.6910 - val_precision: 0.6903 - val_recall: 0.6928 - lr: 1.0000e-04
Epoch 153/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6109 - accuracy: 0.6626 - precision: 0.6651 - recall: 0.6549 - val_loss: 0.5836 - val_accuracy: 0.6913 - val_precision: 0.6903 - val_recall: 0.6940 - lr: 1.0000e-04
Epoch 154/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6106 - accuracy: 0.6633 - precision: 0.6658 - recall: 0.6560 - val_loss: 0.5815 - val_accuracy: 0.6921 - val_precision: 0.6902 - val_recall: 0.6971 - lr: 1.0000e-04
Epoch 155/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6095 - accuracy: 0.6636 - precision: 0.6661 - recall: 0.6561 - val_loss: 0.5820 - val_accuracy: 0.6919 - val_precision: 0.6900 - val_recall: 0.6968 - lr: 1.0000e-04
Epoch 156/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6096 - accuracy: 0.6637 - precision: 0.6669 - recall: 0.6542 - val_loss: 0.5822 - val_accuracy: 0.6919 - val_precision: 0.6888 - val_recall: 0.7000 - lr: 1.0000e-04
Epoch 157/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6102 - accuracy: 0.6636 - precision: 0.6662 - recall: 0.6557 - val_loss: 0.5802 - val_accuracy: 0.6942 - val_precision: 0.6971 - val_recall: 0.6867 - lr: 1.0000e-04
Epoch 158/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6097 - accuracy: 0.6641 - precision: 0.6672 - recall: 0.6549 - val_loss: 0.5801 - val_accuracy: 0.6942 - val_precision: 0.7005 - val_recall: 0.6783 - lr: 1.0000e-04
Epoch 159/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6081 - accuracy: 0.6653 - precision: 0.6674 - recall: 0.6590 - val_loss: 0.5774 - val_accuracy: 0.6965 - val_precision: 0.6994 - val_recall: 0.6894 - lr: 1.0000e-04
Epoch 160/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6085 - accuracy: 0.6654 - precision: 0.6679 - recall: 0.6580 - val_loss: 0.5786 - val_accuracy: 0.6944 - val_precision: 0.6928 - val_recall: 0.6985 - lr: 1.0000e-04
Epoch 161/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6081 - accuracy: 0.6654 - precision: 0.6680 - recall: 0.6578 - val_loss: 0.5767 - val_accuracy: 0.6969 - val_precision: 0.6999 - val_recall: 0.6892 - lr: 1.0000e-04
Epoch 162/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6080 - accuracy: 0.6660 - precision: 0.6682 - recall: 0.6595 - val_loss: 0.5775 - val_accuracy: 0.6954 - val_precision: 0.6939 - val_recall: 0.6990 - lr: 1.0000e-04
Epoch 163/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6072 - accuracy: 0.6670 - precision: 0.6698 - recall: 0.6589 - val_loss: 0.5776 - val_accuracy: 0.6963 - val_precision: 0.6979 - val_recall: 0.6921 - lr: 1.0000e-04
Epoch 164/1000
7680/7680 [==============================] - 168s 22ms/step - loss: 0.6070 - accuracy: 0.6663 - precision: 0.6687 - recall: 0.6592 - val_loss: 0.5770 - val_accuracy: 0.6964 - val_precision: 0.6986 - val_recall: 0.6908 - lr: 1.0000e-04
Epoch 165/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6067 - accuracy: 0.6665 - precision: 0.6691 - recall: 0.6587 - val_loss: 0.5766 - val_accuracy: 0.6964 - val_precision: 0.6936 - val_recall: 0.7036 - lr: 1.0000e-04
Epoch 166/1000
7680/7680 [==============================] - 162s 21ms/step - loss: 0.6067 - accuracy: 0.6671 - precision: 0.6687 - recall: 0.6625 - val_loss: 0.5762 - val_accuracy: 0.6971 - val_precision: 0.6944 - val_recall: 0.7039 - lr: 1.0000e-04
Epoch 167/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6057 - accuracy: 0.6679 - precision: 0.6695 - recall: 0.6632 - val_loss: 0.5766 - val_accuracy: 0.6968 - val_precision: 0.6949 - val_recall: 0.7018 - lr: 1.0000e-04
Epoch 168/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6067 - accuracy: 0.6676 - precision: 0.6702 - recall: 0.6600 - val_loss: 0.5752 - val_accuracy: 0.6979 - val_precision: 0.6996 - val_recall: 0.6936 - lr: 1.0000e-04
Epoch 169/1000
7680/7680 [==============================] - 161s 21ms/step - loss: 0.6064 - accuracy: 0.6674 - precision: 0.6701 - recall: 0.6593 - val_loss: 0.5750 - val_accuracy: 0.6986 - val_precision: 0.6993 - val_recall: 0.6969 - lr: 1.0000e-04
Epoch 170/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6049 - accuracy: 0.6690 - precision: 0.6712 - recall: 0.6627 - val_loss: 0.5735 - val_accuracy: 0.6991 - val_precision: 0.7003 - val_recall: 0.6960 - lr: 1.0000e-04
Epoch 171/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6053 - accuracy: 0.6684 - precision: 0.6706 - recall: 0.6619 - val_loss: 0.5749 - val_accuracy: 0.6985 - val_precision: 0.6969 - val_recall: 0.7024 - lr: 1.0000e-04
Epoch 172/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6048 - accuracy: 0.6696 - precision: 0.6727 - recall: 0.6605 - val_loss: 0.5738 - val_accuracy: 0.6989 - val_precision: 0.6994 - val_recall: 0.6976 - lr: 1.0000e-04
Epoch 173/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6054 - accuracy: 0.6688 - precision: 0.6713 - recall: 0.6614 - val_loss: 0.5721 - val_accuracy: 0.7014 - val_precision: 0.6991 - val_recall: 0.7071 - lr: 1.0000e-04
Epoch 174/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6044 - accuracy: 0.6688 - precision: 0.6716 - recall: 0.6607 - val_loss: 0.5716 - val_accuracy: 0.7013 - val_precision: 0.7028 - val_recall: 0.6976 - lr: 1.0000e-04
Epoch 175/1000
7680/7680 [==============================] - 158s 21ms/step - loss: 0.6044 - accuracy: 0.6694 - precision: 0.6727 - recall: 0.6598 - val_loss: 0.5725 - val_accuracy: 0.7018 - val_precision: 0.7015 - val_recall: 0.7023 - lr: 1.0000e-04
Epoch 176/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6036 - accuracy: 0.6702 - precision: 0.6727 - recall: 0.6631 - val_loss: 0.5713 - val_accuracy: 0.7010 - val_precision: 0.7032 - val_recall: 0.6955 - lr: 1.0000e-04
Epoch 177/1000
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6029 - accuracy: 0.6708 - precision: 0.6730 - recall: 0.6644 - val_loss: 0.5704 - val_accuracy: 0.7022 - val_precision: 0.7077 - val_recall: 0.6889 - lr: 1.0000e-04
Epoch 178/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.6030 - accuracy: 0.6707 - precision: 0.6726 - recall: 0.6650 - val_loss: 0.5720 - val_accuracy: 0.7008 - val_precision: 0.6979 - val_recall: 0.7082 - lr: 1.0000e-04
Epoch 179/1000
7680/7680 [==============================] - 166s 22ms/step - loss: 0.6028 - accuracy: 0.6707 - precision: 0.6722 - recall: 0.6664 - val_loss: 0.5701 - val_accuracy: 0.7021 - val_precision: 0.7007 - val_recall: 0.7055 - lr: 1.0000e-04
Epoch 180/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6026 - accuracy: 0.6710 - precision: 0.6728 - recall: 0.6657 - val_loss: 0.5705 - val_accuracy: 0.7027 - val_precision: 0.6976 - val_recall: 0.7157 - lr: 1.0000e-04
Epoch 181/1000
7680/7680 [==============================] - 164s 21ms/step - loss: 0.6023 - accuracy: 0.6711 - precision: 0.6733 - recall: 0.6646 - val_loss: 0.5691 - val_accuracy: 0.7034 - val_precision: 0.7013 - val_recall: 0.7086 - lr: 1.0000e-04
Epoch 182/1000
7680/7680 [==============================] - 160s 21ms/step - loss: 0.6022 - accuracy: 0.6720 - precision: 0.6731 - recall: 0.6690 - val_loss: 0.5681 - val_accuracy: 0.7044 - val_precision: 0.7029 - val_recall: 0.7081 - lr: 1.0000e-04
Epoch 183/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6013 - accuracy: 0.6726 - precision: 0.6743 - recall: 0.6678 - val_loss: 0.5702 - val_accuracy: 0.7025 - val_precision: 0.7020 - val_recall: 0.7038 - lr: 1.0000e-04
Epoch 184/1000
7680/7680 [==============================] - 158s 21ms/step - loss: 0.6014 - accuracy: 0.6723 - precision: 0.6743 - recall: 0.6667 - val_loss: 0.5687 - val_accuracy: 0.7042 - val_precision: 0.7042 - val_recall: 0.7043 - lr: 1.0000e-04
Epoch 185/1000
7680/7680 [==============================] - 159s 21ms/step - loss: 0.6015 - accuracy: 0.6726 - precision: 0.6745 - recall: 0.6670 - val_loss: 0.5686 - val_accuracy: 0.7036 - val_precision: 0.7031 - val_recall: 0.7048 - lr: 1.0000e-04
Epoch 186/1000
7678/7680 [============================>.] - ETA: 0s - loss: 0.6009 - accuracy: 0.6728 - precision: 0.6755 - recall: 0.6653
Epoch 186: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.
7680/7680 [==============================] - 163s 21ms/step - loss: 0.6009 - accuracy: 0.6728 - precision: 0.6755 - recall: 0.6653 - val_loss: 0.5687 - val_accuracy: 0.7041 - val_precision: 0.7009 - val_recall: 0.7122 - lr: 1.0000e-04
Epoch 187/1000
7680/7680 [==============================] - 167s 22ms/step - loss: 0.5964 - accuracy: 0.6768 - precision: 0.6787 - recall: 0.6714 - val_loss: 0.5591 - val_accuracy: 0.7108 - val_precision: 0.7156 - val_recall: 0.6996 - lr: 5.0000e-05
Epoch 188/1000
6645/7680 [========================>.....] - ETA: 2:19 - loss: 0.5945 - accuracy: 0.6780 - precision: 0.6800 - recall: 0.6724
ASSISTANT
通过观察您的训练输出日志，我们可以看到模型的准确率确实在缓步提升，这通常表明模型正在逐步学习数据集中的模式和特征。不过，您提到的“性能提升越来越慢”，通常这是因为模型开始逼近其性能极限，进一步的准确率提升变得更加困难，每一次的增益都可能需要更长时间的训练以及更多的数据。
您对于准确率提高到80%需要三天时间的判断，可能是基于当前收敛速度所做的估计。但实际上，是否能够达到80%准确率以及需要多久时间，除了受到模型本身架构和训练过程设置的影响外，还与数据的固有特性密切相关。如果数据本身就无法提供足够的信息来区分目标变量的类别，那么模型性能可能永远无法达到80%。
同时，从您的输出日志我们注意到，在第186个epoch时，训练过程中的回调函数 `ReduceLROnPlateau` 被触发了，学习率降了一半。降低学习率通常用于在模型接近最优值时进行更细致的权值调节，这有可能会促使模型获得更高的准确率。但是否足以到达80%、以及需要多长时间则很难准确预测。
另一方面，如果你希望加快性能提升，可能需要考虑以下策略：
1. **调整训练策略**：如更改批次大小、优化器种类或其他超参数调整。
2. **修改模型架构**：增加网络的复杂性，如添加更多层或神经元，或者尝试不同的模型结构。
3. **数据增强**：使用数据增强技术生成额外的训练样本。
4. **特征工程**：尝试创建新的特征或转换现有特征。
5. **正则化与dropout**：防止过拟合的同时促使模型更好地从数据中学习。
6. **集成学习或模型融合**：结合多个模型的力量可能会提高整体性能。
请注意，单纯地增加训练时间并不能保证模型的表现会如期望的那样提升，有效的调整策略和方法的选择将是关键。同时，切记在增强模型复杂度和调整训练策略的同时，仔细监控模型在验证集上的表现，以确保所做的改进是合理的并能够实现所期望的效果。
turns-00016.parquet:51256
84351e1aeb3d9c219285bee0
turn 16/16gpt-4-1106-previewChineseJapan17 words
degenerate_repetitionAbsentFinal dense release
ASSISTANT
观察到训练时间的增加可能由几个因素导致：
1. **数据加载**：如果训练数据没有被充分利用内存缓存，或者在数据预处理和加载方面出现了瓶颈，这可能会导致额外的耗时。
2. **硬件资源竞争**：如果您的计算环境是多任务的，可能有其他进程或任务与您的训练过程竞争硬件资源（如CPU、GPU、内存），从而减慢了训练速度。
3. **批量大小**：如果您在之前的epoch中与188轮之间改变了批量大小，增加批量大小可以提高GPU的利用率，但同样也需要更长的时间来处理更大的数据量。
4. **模型保存和回调**：如果您正在使用模型检查点或其他需要在每个epoch结束时运行的回调函数，这些操作可能产生额外的时间开销。
5. **学习率变化**：在您的输出中提到，学习率在第186轮减半了。理论上，学习率的变化不应直接影响到每轮的运行时间，除非它间接触发了其他变化，例如导致优化器以不同的方式计算权重更新。
6. **网络带宽或IO操作**：如果模型训练需要频繁地进行读取或写入操作，如从远程存储加载数据或日志记录，网络带宽或磁盘速度可能会成为制约因素。
7. **随机性**：深度学习训练过程中存在一定程度的随机性，如数据集的随机打乱、权重初始化等，有时这也能导致不同epoch的时间波动。
为了确切地了解为什么188轮训练时间会增加，您需要仔细检查模型训练过程中的日志和监控系统资源使用情况，例如GPU使用率、内存消耗、CPU负载等。此外，确认没有对训练流程做任何可能影响训练时间的修改也很重要。如果训练时间的增加是一个持续性问题，而非偶发事件，那么找到并解决这个问题将有助于提高训练效率。
turns-00016.parquet:51257
135ace4750fc5f002daa382e
turn 1/3gpt-4-1106-previewChineseTaiwan2 words
degenerate_repetitionAbsentFinal dense release
ASSISTANT
没问题，请问您想要询问什么问题，需要关于哪方面的信息？我会尽力以中文提供答案。
turns-00016.parquet:51258
e28e59be9e0f19ca3ce19fc7
turn 2/3gpt-4-1106-previewChineseTaiwan31 words
degenerate_repetitionAbsentFinal dense release
ASSISTANT
美国共和党（Republican Party）是美国的两大主要政党之一，与民主党（Democratic Party）相对立。共和党支持者通常被称为“共和党人”（Republicans），党徽是大象。
以下是美国共和党的简要发展历史概览：
- 创党初期（1854年）：共和党成立于1854年，由反对奴隶制扩展的活动家和自由土地党成员以及一些反对当时民主党政策的北方民主党人共同创立。共和党诞生的目的之一，就是阻止奴隶制在美国新的领土扩张。
- 初次执政（1860年）：共和党在1860年选出第一个总统亚伯拉罕·林肯（Abraham Lincoln），其上台后不久，美国就发生了南北战争。林肯领导国家取得了胜利，并废除了奴隶制。
- 政策转向（1865年 - 1933年）：在南北战争后和20世纪初，共和党成为了推动工业化和商业发展的主要力量。在此时期，共和党支持了许多推动高关税、金本位制和有限政府干预经济领域的政策。
- 全国大萧条与新政（1933年 - 1960年）：在大萧条（1929年 - 1939年）期间，由于经济困难，民主党总统富兰克林·D·罗斯福开展了著名的“新政”，意在通过政府干预来恢复国家经济活力。而共和党在这一时期大多保持反对立场。
- 民权运动（1950年代 - 1960年代）：尽管历史上共和党支持民权，但在60年代的民权运动期间，共和党与民主党在对待民权问题上的差距逐渐缩小。共和党中的一些成员开始更多地支持州权，而民主党在此期间变得更加倾向于联邦政府保护民权。
- 社会保守转变（1960年代 - 1980年代）：在尼克松总统任内，共和党倡导了所谓的“南方战略”，开始吸引传统上属于民主党的南方白人保守选民。随着里根（Ronald Reagan）1980年代的执政，共和党越来越倾向于社会保守和自由市场经济政策。
- 当代共和党（1990年代至今）：在乔治·H·W·布什、乔治·W·布什与唐纳德·特朗普的总统任期内，共和党继续推行减税、强化国防和保守的社会政策。近年来，共和党在特朗普领导下，强调美国优先政策和对外贸易保护主义，同时在移民政策上采取了更加强硬的立场。
turns-00016.parquet:51259
b400b5cb0213a43fbc99f071
turn 3/3gpt-4-1106-previewChineseTaiwan22 words
degenerate_repetitionAbsentFinal dense release
ASSISTANT
美国民主党（Democratic Party）是美国两大主要政党之一，与共和党相对立。其党徽通常为一头驴。
以下是美国民主党的简要发展历史概览：
- 创党初期（1828年）：民主党的前身是由托马斯·杰弗逊领导成立的民主-共和党。到了1828年，该党逐渐发展成为今天的民主党，安德鲁·杰克逊成为第一位民主党总统。
- 支持奴隶制（19世纪）：在19世纪，民主党是支持奴隶制的南方派的主要政治力量。此外，当时的民主党支持较低的关税和对银行体系的扩张。
- 分裂与重组（1860年 - 1900年）：美国内战期间，民主党遭遇分裂，战后支持重建南方社会和经济的北方民主党和南方保持分歧。到了19世纪末，民主党开始吸引城市的移民和劳工阶层，支持议程渐渐向进步改革转型。
- 新政与改革（1930年代 - 1940年代）：在弗兰克林·D·罗斯福总统的领导下，民主党提倡新政（New Deal），一系列的社会经济政策和立法旨在缓解大萧条的严重影响，促进社会经济恢复和改革。
- 民权与新民主党（1960年代）：在林登·约翰逊总统任期内，民主党通过了一系列的民权法案，包括1964年的《民权法案》和1965年的《投票权法案》，从而为美国的少数族裔争取了更多的权利。这一时期，民主党开始从南方白人统治的党派转型为更多元化的政治力量。
- 现代民主党（1970年代至今）：今天的民主党倾向于支持广泛的公民权利、社会福利项目、环境保护和渐进的税收政策。近年来，民主党在比尔·克林顿、巴拉克·奥巴马和乔·拜登等人的领导下，继续强调社会正义、经济机会均等和包容性政策。
每个阶段，民主党的政策主张和支持基础都会根据社会经济环境的变化而不断调整。它历史上涵盖了不同的地域、经济和种族群体，目前被看作是较为青睐政府在经济中发挥较大作用、并支持社会自由主义的党派。