如何使用tensorflow.models.Sequential（）提前停止？

使用如下生成的顺序模型：

def generate_model():
    model = Sequential()
    model.add(Conv1D(64, kernel_size=10, strides=1,
                     activation='relu', padding='same',
                     input_shape=(MAXLENGTH, NAMESPACELENGTH)))
    model.add(MaxPooling1D(pool_size=4, strides=2))
    model.add(Conv1D(32, 3, activation='relu', padding='same'))
    model.add(MaxPooling1D(pool_size=4))
    model.add(Flatten())
    model.add(Dense(10, activation='relu'))
    model.add(Dense(1, activation='linear'))
    model.compile(loss='mean_squared_error', 
                  optimizer='adam', metrics=['mean_squared_error'])
    return model

我想做Kfold交叉验证建模。因此，我在一个循环中训练K个模型：

models = []
for ndx_train, ndx_val in kfold.split(X, y):
    model = generate_model()
    N_train = len(ndx_train)
    X_batch = X[ndx_train]
    y_batch = y[ndx_train]
    model.fit(X_batch, y_batch, epochs=100, verbose=1, steps_per_epoch=10,
             validation_data=(X[ndx_val], y[ndx_val]), validation_steps=100)

    models.append(model)

现在，通过查看输出，我可以看到我希望每个模型何时停止。即当验证错误再次增加时。使用纯

tf

和这种更高级别的api设置，是否可以轻松做到这一点？有一些建议是通过使用回调来使用

tflearn

：

from tensorflow.keras.callbacks import EarlyStopping
callbacks = [
    EarlyStopping(monitor='val_mean_squared_error', patience=2, verbose=1),
]
model.fit(..., callbacks=callbacks)

通过使用回调：

from tensorflow.keras.callbacks import EarlyStopping
callbacks = [
    EarlyStopping(monitor='val_mean_squared_error', patience=2, verbose=1),
]
model.fit(..., callbacks=callbacks)