keras 如何保存最佳的训练模型

from keras.callbacks import ModelCheckpoint
 
filepath='weights.best.hdf5'
  # 有一次提升, 则覆盖一次.
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1,save_best_only=True,mode='max',period=2) callbacks_list = [checkpoint]
 
model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=2e-6,decay=1e-7),metrics=['acc'])
 
history1 = model.fit_generator(
     train_generator,
     steps_per_epoch=100,
     epochs=40,
     validation_data=validation_generator,
     validation_steps=100, callbacks=callbacks_list)

Epoch 2/40
100/100 [==============================] - 24s 241ms/step - loss: 0.2715 - acc: 0.9380 - val_loss: 0.1635 - val_acc: 0.9600
 
Epoch 00002: val_acc improved from -inf to 0.96000, saving model to weights.best.hdf5
Epoch 3/40
100/100 [==============================] - 24s 240ms/step - loss: 0.1623 - acc: 0.9575 - val_loss: 0.1116 - val_acc: 0.9730
Epoch 4/40
100/100 [==============================] - 24s 242ms/step - loss: 0.1143 - acc: 0.9730 - val_loss: 0.0799 - val_acc: 0.9840
 
Epoch 00004: val_acc improved from 0.96000 to 0.98400, saving model to weights.best.hdf5

from keras.callbacks import ModelCheckpoint
 
# checkpoint
filepath = "weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
# 中途训练效果提升, 则将文件保存, 每提升一次, 保存一次
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True,mode='max')
callbacks_list = [checkpoint]
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
 
history1 = model.fit_generator(
     train_generator,
     steps_per_epoch=100,
     epochs=40,
     validation_data=validation_generator,
     validation_steps=100, callbacks=callbacks_list)

# load weights 加载模型权重
model.load_weights('weights.best.hdf5')
#如果想加载模型,则将model.load_weights('weights.best.hdf5')改为
#model.load_model('weights.best.hdf5')
# compile 编译
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print('Created model and loaded weights from hdf5 file')
 
# estimate
scores = model.evaluate(validation_generator, steps=30， verbose=0)
print("{0}: {1:.2f}%".format(model.metrics_names[1], scores[1]*100))
ModelCheckpoint参数说明

keras.callbacks.ModelCheckpoint(filepath,monitor='val_loss',verbose=0,save_best_only=False, save_weights_only=False, mode='auto', period=1)