当我尝试训练下面描述的自编码器时，收到了一个错误，错误信息是‘传递了一个形状为(256, 28, 28, 1)的目标数组给形状为(None, 0, 28, 1)的输出，而使用的是`binary_crossentropy`损失函数。这个损失函数期望目标与输出具有相同的形状。’ 输入和输出的维度都应该是(28,28,1)，其中256是批量大小。运行.summary()确认解码器模型的输出是正确的(28,28,1)，但当编码器和解码器一起编译时，这似乎发生了变化。您知道这是怎么回事吗？当生成网络时，这三个函数是依次调用的。
def buildEncoder(): input1 = Input(shape=(28,28,1)) input2 = Input(shape=(28,28,1)) merge = concatenate([input1,input2]) convEncode1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(merge) maxPoolEncode1 = MaxPooling2D(pool_size=(2, 1))(convEncode1) convEncode2 = Conv2D(16, (3,3), activation = 'sigmoid', padding = 'same')(maxPoolEncode1) convEncode3 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convEncode2) model = Model(inputs = [input1,input2], outputs = convEncode3) model.compile(loss='binary_crossentropy', optimizer=adam) return modeldef buildDecoder(): input1 = Input(shape=(28,28,1)) upsample1 = UpSampling2D((2,1))(input1) convDecode1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(upsample1) crop1 = Cropping2D(cropping = ((0,28),(0,0)))(convDecode1) crop2 = Cropping2D(cropping = ((28,0),(0,0)))(convDecode1) convDecode2_1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(crop1) convDecode3_1 = Conv2D(16, (3,3), activation = 'relu', padding = 'same')(crop2) convDecode2_2 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convDecode2_1) convDecode3_2 = Conv2D(1, (3,3), activation = 'sigmoid', padding = 'same')(convDecode3_1) model = Model(inputs=input1, outputs=[convDecode2_2,convDecode3_2]) model.compile(loss='binary_crossentropy', optimizer=adam) return modeldef buildAutoencoder(): autoInput1 = Input(shape=(28,28,1)) autoInput2 = Input(shape=(28,28,1)) encode = encoder([autoInput1,autoInput2]) decode = decoder(encode) model = Model(inputs=[autoInput1,autoInput2], outputs=[decode[0],decode[1]]) model.compile(loss='binary_crossentropy', optimizer=adam) return model运行model.summary()函数确认了这个模型的最终输出维度
回答：
看起来您在编码器中计算形状时出现了错误。您假设解码器将获得(None, 28, 28, 1)，但实际上您的编码器输出的却是(None, 14, 28, 1)。
print(encoder) # Tensor("model_1/conv2d_3/Sigmoid:0", shape=(?, 14, 28, 1), dtype=float32)现在在您的解码器中，您在假设有(28, 28, 1)的基础上进行裁剪等操作，这可能会将其裁剪到0。单独的模型是可以工作的，问题出在您将它们连接起来的时候。
