如何使用Keras从密集层转换为conv2D层？

Question

我只是想做标题中所说的。这是我的代码：

def ConvAutoEncoder(train_data,test_data,n_epochs = 50,batchSize = 128,data_shape=(IMAGE_SIZE,IMAGE_SIZE,3)):

print('Training Neural Network')
input_img = Input(shape=data_shape)

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
print(x.shape)
x = Conv2D(4, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
print(encoded.shape)

# at this point the representation is (6, 6, 4 i.e. 128-dimensional
encoded = Flatten()(encoded)
encoded = Dense( 6*6*4,activation='relu')(encoded)
print(encoded.shape)
endoded = Reshape((6,6,4))(encoded)
print(encoded.shape)

x = Conv2D(4, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
print(x.shape)
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

autoencoder.fit(train_data, train_data,
            epochs=n_epochs,
            batch_size=batchSize,
            shuffle=True,
            verbose=2,
            validation_data=(test_data, test_data),
            callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])


return autoencoder

但是，当我运行它时，“重塑”层根本不执行任何操作，重塑前的输出形状为（？，144），其后的形状也为（？144）。我使用的重塑是错误的还是有其他方法将密集层连接到conv2D层？