首先，我们要知道哪儿里会产生这四个值？

history = model_final.fit(

train_generator,

epochs=epochs,

validation_data=validation_generator,

validation_steps = int(nb_validation_samples / batch_size),

callbacks=[checkpoint,early])

当model.compile的metrics=[“accuracy”]时，history.history里包含的就是这四个值的内容

接下来，我们开始写画图代码（可直接复制代码）

import matplotlib.pyplot as plt

accuracy = history.history['accuracy']

val_accuracy = history.history['val_accuracy']

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

plt.plot(epochs, accuracy, 'bo', label = 'Training accuracy')

plt.plot(epochs, val_accuracy, 'b', label = 'Validation accuracy')

plt.title('Training And Validation val_accuracy')

plt.xlabel('Epochs')

plt.ylabel('Accuracy')

plt.legend()

plt.show()# 以上为画出val_accuracy和accuracy的图

#以下为val_loss和loss的图

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 val_accuracy')

plt.xlabel('Epochs')

plt.ylabel('Loss')

plt.legend()

plt.show()