keras 画训练过程曲线

# Fit the model

history = tiramisu.fit(train_data, train_label, batch_size=batch_size, epochs=nb_epoch,

callbacks=callbacks_list, class_weight=class_weighting,verbose=1, validation_data=(test_data, test_label), shuffle=True) # validation_split=0.33

# This save the trained model weights to this file with number of epochs

tiramisu.save_weights('weights/prop_tiramisu_weights_67_12_func_10-e7_decay{}.hdf5'.format(nb_epoch))

import matplotlib.pyplot as plt

# list all data in history

print(history.history.keys())

# summarize history for accuracy

plt.plot(history.history['acc'])

plt.plot(history.history['val_acc'])

plt.title('model accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

# summarize history for loss

plt.plot(history.history['loss'])

plt.plot(history.history['val_loss'])

plt.title('model loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()