如何在keras中定义convlstm encoder_decoder

Question

我已经看到了使用keras中LSTM构建编码器网络的示例，但我想拥有一个convlstm encoder-decoder，并且由于convlstm2d不接受任何'prinitial_state'参数可以将编码器的初始状态传递给解码器，我尝试使用keras中的RNN，并试图将ConvlstM2D作为RNN的单元格传递，但我收到了以下错误：

ValueError: ('`cell` should have a `call` method. The RNN was passed:', <tf.Tensor 'encoder_1/TensorArrayReadV3:0' shape=(?, 62, 62, 32) dtype=float32>)

这就是我尝试定义RNN单元格的方式：

first_input = Input(shape=(None, 62, 62, 12))
encoder_convlstm2d = ConvLSTM2D(filters=32, kernel_size=(3, 3),
                                    padding='same',
                                    name='encoder'+ str(1))(first_input )
encoder_outputs, state_h, state_c = keras.layers.RNN(cell=encoder_convlstm2d, return_sequences=False, return_state=True, go_backwards=False,
                 stateful=False, unroll=False)

Answer 1

以下是我使用convlstm的基于encoder-decoder的解决方案的方法。

def convlstm(input_shape):
    print(np.shape(input_shape))
    inpTensor = Input((input_shape))
    #encoder
    net1 = ConvLSTM2D(filters=32, kernel_size=3,
                   padding='same', return_sequences=True)(inpTensor)
    max_pool1 = MaxPooling3D(pool_size=(2, 2, 2), strides=2, 
    padding='same')(net1)
    bn1 = BatchNormalization(axis=1)(max_pool1)
    dp1 = Dropout(0.2)(bn1)
    net2 = ConvLSTM2D(filters=64, kernel_size=3,
                    padding='same', return_sequences=True)(dp1)
    max_pool2 = MaxPooling3D(pool_size=(2, 2, 2), strides=2, 
    padding='same')(net2)
    bn2 = BatchNormalization(axis=1)(max_pool2)
    dp2 = Dropout(0.2)(bn2)
    net3 = ConvLSTM2D(filters=128, kernel_size=3,
                   padding='same', return_sequences=True)(dp2)
    max_pool3 = MaxPooling3D(pool_size=(2, 2, 2), strides=2, 
    padding='same')(net3)
    bn3 = BatchNormalization(axis=1)(max_pool3)
    dp3 = Dropout(0.2)(bn3)

    #decoder
    net4 = ConvLSTM2D(filters=128, kernel_size=3,
                    padding='same', return_sequences=True)(dp3)
    up1 = UpSampling3D((2, 2, 2))(net4)
    net5= ConvLSTM2D(filters=64, kernel_size=3,
                    padding='same', return_sequences=True)(up1)
    up2 = UpSampling3D((2, 2, 2))(net5)
    net6 = ConvLSTM2D(filters=32, kernel_size=3,
                    padding='same', return_sequences=False)(up2)
    up3 = UpSampling2D((2, 2))(net6)
    out = Conv2D(filters=1, kernel_size=(3, 3), activation='sigmoid',
                  padding='same', data_format='channels_last')(up3)
    #or use only return out
    return Model(inpTensor, out)