Python 如何展平嵌套模型？（keras功能API）

我使用keras模型函数API定义了一个简单的模型。它的一个层是一个完全连续的模型，所以我得到了一个嵌套的层结构（见下图）

如何将此嵌套层结构转换为平面层结构？（使用脚本，而不是手动…）

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我所拥有的：

我想将其转换为：

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生成嵌套层结构的代码：

def create_network_with_one_subnet():
    # define subnetwork
    subnet = keras.models.Sequential()
    subnet.add(keras.layers.Conv2D(6, (3, 3), padding='same'))
    subnet.add(keras.layers.MaxPool2D())
    subnet.add(keras.layers.Conv2D(12, (3, 3), padding='same'))
    subnet.add(keras.layers.MaxPool2D())
    #subnet.summary()


    # define complete network
    input_shape = (32, 32, 1)
    net_in = keras.layers.Input(shape=input_shape)
    net_out = subnet(net_in)
    net_out = keras.layers.Flatten()(net_out)
    net_out = keras.layers.Dense(1)(net_out)
    net_complete = keras.Model(inputs=net_in, outputs=net_out)
    net_complete.compile(loss='binary_crossentropy',
                         optimizer=keras.optimizers.Adam(lr=0.001),
                         metrics=['acc'],
                         )
    net_complete.summary()
    return net_complete

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啊,比预想的容易多了。在谷歌搜索正确的关键字后，解决方案如下：

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处理具有多个级别的嵌套模型的更好解决方案：

def flatten_model(model_nested):
    def get_layers(layers):
        layers_flat = []
        for layer in layers:
            try:
                layers_flat.extend(get_layers(layer.layers))
            except AttributeError:
                layers_flat.append(layer)
        return layers_flat

    model_flat = tfk.models.Sequential(
        get_layers(model_nested.layers)
    )
    return model_flat

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什么是tfk？tensorflow.kerashis不适用于连接/添加层！！！

def create_network_with_one_subnet():
    # define subnetwork
    subnet = keras.models.Sequential()
    subnet.add(keras.layers.Conv2D(6, (3, 3), padding='same'))
    subnet.add(keras.layers.MaxPool2D())
    subnet.add(keras.layers.Conv2D(12, (3, 3), padding='same'))
    subnet.add(keras.layers.MaxPool2D())
    #subnet.summary()


    # define complete network
    input_shape = (32, 32, 1)
    net_in = keras.layers.Input(shape=input_shape)
    net_out = subnet(net_in)
    net_out = keras.layers.Flatten()(net_out)
    net_out = keras.layers.Dense(1)(net_out)
    net_complete = keras.Model(inputs=net_in, outputs=net_out)
    net_complete.compile(loss='binary_crossentropy',
                         optimizer=keras.optimizers.Adam(lr=0.001),
                         metrics=['acc'],
                         )
    net_complete.summary()
    return net_complete

def flatten_model(model_nested):
    layers_flat = []
    for layer in model_nested.layers:
        try:
            layers_flat.extend(layer.layers)
        except AttributeError:
            layers_flat.append(layer)
    model_flat = keras.models.Sequential(layers_flat)
    return model_flat

def flatten_model(model_nested):
    def get_layers(layers):
        layers_flat = []
        for layer in layers:
            try:
                layers_flat.extend(get_layers(layer.layers))
            except AttributeError:
                layers_flat.append(layer)
        return layers_flat

    model_flat = tfk.models.Sequential(
        get_layers(model_nested.layers)
    )
    return model_flat