如何在Keras中保存每个时期的训练历史？

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import ModelCheckpoint, History

# 创建一个简单的模型
model = Sequential()
model.add(Dense(10, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 创建回调函数
checkpoint = ModelCheckpoint('model-{epoch:02d}-{val_loss:.2f}.h5', monitor='val_loss', save_best_only=True, mode='min')
history = History()

# 训练模型并保存历史
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=32, callbacks=[checkpoint, history])

# 访问训练历史
print(history.history)

from keras.models import Sequential
from keras.layers import Dense

# 创建一个简单的模型
model = Sequential()
model.add(Dense(10, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 初始化历史字典
history = {}

# 自定义回调函数
class CustomCallback(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        for k, v in logs.items():
            if k not in history:
                history[k] = []
            history[k].append(v)

# 训练模型并保存历史
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=32, callbacks=[CustomCallback()])

# 访问训练历史
print(history)

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import TensorBoard

# 创建一个简单的模型
model = Sequential()
model.add(Dense(10, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 创建TensorBoard回调函数
tensorboard = TensorBoard(log_dir='./logs', histogram_freq=1, write_graph=True, write_images=True)

# 训练模型并保存历史
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=32, callbacks=[tensorboard])