python - 在Tensorflow中，我不知道为什么我训练了一个模型，训练损失减少了，但似乎没有训练

Question

这是我的代码和培训结果。

batch_size = 100
epochs = 50
yale_history = yale_classifier.fit(x_train, y_train_oh,batch_size=batch_size,epochs=epochs,validation_data=(x_train,y_train_oh))

Epoch 1/50
20/20 [==============================] - 32s 2s/step - loss: 3.9801 - accuracy: 0.2071 - val_loss: 3.6919 - val_accuracy: 0.0245
Epoch 2/50
20/20 [==============================] - 30s 2s/step - loss: 1.2557 - accuracy: 0.6847 - val_loss: 4.1914 - val_accuracy: 0.0245
Epoch 3/50
20/20 [==============================] - 30s 2s/step - loss: 0.4408 - accuracy: 0.8954 - val_loss: 4.6284 - val_accuracy: 0.0245
Epoch 4/50
20/20 [==============================] - 30s 2s/step - loss: 0.1822 - accuracy: 0.9592 - val_loss: 4.9481 - val_accuracy: 0.0398
Epoch 5/50
20/20 [==============================] - 30s 2s/step - loss: 0.1252 - accuracy: 0.9760 - val_loss: 5.3728 - val_accuracy: 0.0276
Epoch 6/50
20/20 [==============================] - 30s 2s/step - loss: 0.0927 - accuracy: 0.9816 - val_loss: 5.7009 - val_accuracy: 0.0260
Epoch 7/50
20/20 [==============================] - 30s 2s/step - loss: 0.0858 - accuracy: 0.9837 - val_loss: 6.0049 - val_accuracy: 0.0260
Epoch 8/50
20/20 [==============================] - 30s 2s/step - loss: 0.0646 - accuracy: 0.9867 - val_loss: 6.3786 - val_accuracy: 0.0260
Epoch 9/50
20/20 [==============================] - 30s 2s/step - loss: 0.0489 - accuracy: 0.9898 - val_loss: 6.5156 - val_accuracy: 0.0260

你可以看到我也使用了训练数据作为验证数据。训练损失与验证损失不同，这很奇怪。此外，当我评估它时，似乎我的模型根本没有经过如下训练。

yale_classifier.evaluate(x_train, y_train_oh)

62/62 [==============================] - 6s 96ms/step - loss: 7.1123 - accuracy: 0.0260
[7.112329483032227, 0.026020407676696777]

您有解决此问题的建议吗？

标签： pythontensorflowmachine-learningkerasdeep-learning