Keras 如何使解码器输出形状与输入图像形状相等？

我试图找出：

解码器。output\u shape[1::==IMG\u shape

我有以下我已编程的功能。当我尝试运行解码器.output_shape[1::==IMG_shape时，我发现我的图像形状和解码器输出形状不一样

def build_deep_autoencoder(img_shape, code_size):
    """PCA's deeper brother."""
    H,W,C = img_shape

    # encoder
    encoder = keras.models.Sequential()
    encoder.add(L.InputLayer(img_shape))

    # numbers of output channels: 32, 64, 128, 256.
    encoder.add(L.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='elu'))
    encoder.add(L.MaxPooling2D())

    encoder.add(L.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='elu'))
    encoder.add(L.MaxPooling2D())

    encoder.add(L.Conv2D(filters=128, kernel_size=(3, 3), padding='same', activation='elu'))
    encoder.add(L.MaxPooling2D())

    encoder.add(L.Conv2D(filters=256, kernel_size=(3, 3), padding='same', activation='elu'))
    encoder.add(L.MaxPooling2D())

    encoder.add(L.Flatten())                  #flatten image to vector
    encoder.add(L.Dense(code_size, activation = 'elu')) 


    # decoder
    decoder = keras.models.Sequential()
    decoder.add(L.InputLayer((code_size,)))

    # numbers of output channels: 128, 64, 32, 3.
    decoder.add(L.Dense(np.prod(img_shape), activation = 'elu'))  #actual decoder, height*width*3 units
    decoder.add(L.Reshape(img_shape))         #un-flatten

    decoder.add(L.Conv2DTranspose(filters=128, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))

    decoder.add(Conv2DTranspose(filters=64, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))

    decoder.add(L.Conv2DTranspose(filters=32, kernel_size=(3, 3), strides=2, activation='elu', padding='same'))

    decoder.add(L.Conv2DTranspose(filters=3, kernel_size=(3, 3), strides=2, activation=None, padding='same'))


    return encoder, decoder

请注意，

code\u size=32

我希望

解码器的输出。output_shape[1::==IMG_shape

为

True

，但实际输出为

False