如何在 CNN 块上应用 TimeDistributed 层？

debugcn 发表于 Dev

格鲁姆

这是我的尝试：

inputs = Input(shape=(config.N_FRAMES_IN_SEQUENCE, config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))

def cnn_model(inputs):
    x = Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu')(inputs)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    return x

x = TimeDistributed(cnn_model)(inputs)

这给出了以下错误：

AttributeError: 'function' object has no attribute 'built'

今天

您需要使用Lambdalayer 并将您的功能包装在其中：

# cnn_model function the same way as you defined it ...

x = TimeDistributed(Lambda(cnn_model))(inputs)

或者，您可以将该块定义为模型，然后TimeDistributed在其上应用层：

def cnn_model():
    input_frame = Input(shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))

    x = Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu')(input_frame)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=64, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    x = Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=(2, 2))(x)

    model = Model(input_frame, x)
    return model

inputs = Input(shape=(config.N_FRAMES_IN_SEQUENCE, config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))

x = TimeDistributed(cnn_model())(inputs)

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