Вам необходимо использовать слой Lambda
и обернуть в него свою функцию:
# cnn_model function the same way as you defined it ...
x = TimeDistributed(Lambda(cnn_model))(inputs)
В качестве альтернативы, вы можете определить этот блок как модель и затем применить к нему слой TimeDistributed
:
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)