留出验证集

x_val = x_train[:1000]
partial_x_train = x_train[1000:]

y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]

训练模型

history = model.fit(partial_x_train,
                    partial_y_train,
                    epochs=20,
                    batch_size=512,
                    validation_data=(x_val, y_val))

绘制训练损失和验证损失

import matplotlib.pyplot as plt

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(loss)+ 1)

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

训练损失和验证损失.png

绘制训练精度和验证精度

plt.clf() # 清空图像

acc = history.history['acc']
val_acc = history.history['val_acc']

plt.plot(epochs, acc, 'ro', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()

训练精度和验证精度.png

可以看出在第9轮之后开始过拟合。

重新开始训练一个模型

model.fit(x_train,
          one_hot_train_labels,
          epochs=9,
          batch_size=512
          )
results = model.evaluate(x_test, one_hot_test_labels)

print(results)

[0.9613658222680843, 0.7902938557966204]

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