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

使用这样生成的顺序模型:

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。所以,我在一个循环中训练K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,我可以通过查看输出来查看每个模型何时停止。即当验证错误再次增加时。有没有可能用纯的 tf 有了这个更高级别的API设置?有一些建议使用沿线使用 tflearn here .