Python 如何添加keras退出层？

如何添加Keras辍学层？不幸的是，我不知道我到底要在哪里添加这个层。我看了两个链接：

举个例子，我见过这个

model.add(Dense(60, input_dim=60, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(30, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))

据我所知，密集层是通过循环创建的，因此我不确定如何添加它

def get_Model(...):
   
    # build dense layer for model
    for i in range(1, len(dense_layers)):
       
        layer = Dense(dense_layers[i],
                      activity_regularizer=l2(reg_layers[i]),
                      activation='relu',
                      name='layer%d' % i)
        mlp_vector = layer(mlp_vector)

    predict_layer = Concatenate()([mf_cat_latent, mlp_vector])
    result = Dense(1, activation='sigmoid',
                   kernel_initializer='lecun_uniform', name='result')

    model = Model(inputs=[input_user, input_item], outputs=result(predict_layer))

    return model

试试这个：

for i in range(1, len(dense_layers)):
   
    layer = Dense(dense_layers[i],
                  activity_regularizer=l2(reg_layers[i]),
                  activation='relu',
                  name='layer%d' % i)
    mlp_vector = layer(mlp_vector)
    mlp_vector = Dropout(0.2)(mlp_vector)

在这里看一下函数API