这是我的模型：
filters = 256kernel_size = 3strides = 1factor = 4 # 上采样的因子inputLayer = Input(shape=(img_height//factor, img_width//factor, img_depth))conv1 = Conv2D(filters, kernel_size, strides=strides, padding='same')(inputLayer)res = Conv2D(filters, kernel_size, strides=strides, padding='same')(conv1)act = ReLU()(res)res = Conv2D(filters, kernel_size, strides=strides, padding='same')(act)res_rec = Add()([conv1, res])for i in range(15): # 16-1 res1 = Conv2D(filters, kernel_size, strides=strides, padding='same')(res_rec) act = ReLU()(res1) res2 = Conv2D(filters, kernel_size, strides=strides, padding='same')(act) res_rec = Add()([res_rec, res2])conv2 = Conv2D(filters, kernel_size, strides=strides, padding='same')(res_rec)a = Add()([conv1, conv2])up = UpSampling2D(size=4)(a)outputLayer = Conv2D(filters=3, kernel_size=1, strides=1, padding='same')(up)model = Model(inputs=inputLayer, outputs=outputLayer)model.summary()显示如下：
__________________________________________________________________________________________________Layer (type) Output Shape Param # Connected to ==================================================================================================input_1 (InputLayer) (None, 350, 350, 3) 0 __________________________________________________________________________________________________conv2d_1 (Conv2D) (None, 350, 350, 256 7168 input_1[0][0] __________________________________________________________________________________________________conv2d_2 (Conv2D) (None, 350, 350, 256 590080 conv2d_1[0][0] __________________________________________________________________________________________________re_lu_1 (ReLU) (None, 350, 350, 256 0 conv2d_2[0][0] __________________________________________________________________________________________________conv2d_3 (Conv2D) (None, 350, 350, 256 590080 re_lu_1[0][0] __________________________________________________________________________________________________add_1 (Add) (None, 350, 350, 256 0 conv2d_1[0][0] conv2d_3[0][0] __________________________________________________________________________________________________conv2d_4 (Conv2D) (None, 350, 350, 256 590080 add_1[0][0] __________________________________________________________________________________________________re_lu_2 (ReLU) (None, 350, 350, 256 0 conv2d_4[0][0] __________________________________________________________________________________________________conv2d_5 (Conv2D) (None, 350, 350, 256 590080 re_lu_2[0][0] __________________________________________________________________________________________________add_2 (Add) (None, 350, 350, 256 0 add_1[0][0] conv2d_5[0][0] __________________________________________________________________________________________________conv2d_6 (Conv2D) (None, 350, 350, 256 590080 add_2[0][0] __________________________________________________________________________________________________re_lu_3 (ReLU) (None, 350, 350, 256 0 conv2d_6[0][0] __________________________________________________________________________________________________conv2d_7 (Conv2D) (None, 350, 350, 256 590080 re_lu_3[0][0] __________________________________________________________________________________________________add_3 (Add) (None, 350, 350, 256 0 add_2[0][0] conv2d_7[0][0] ...... this goes on for a long time ..... __________________________________________add_15 (Add) (None, 350, 350, 256 0 add_14[0][0] conv2d_31[0][0] __________________________________________________________________________________________________conv2d_32 (Conv2D) (None, 350, 350, 256 590080 add_15[0][0] __________________________________________________________________________________________________re_lu_16 (ReLU) (None, 350, 350, 256 0 conv2d_32[0][0] __________________________________________________________________________________________________conv2d_33 (Conv2D) (None, 350, 350, 256 590080 re_lu_16[0][0] __________________________________________________________________________________________________add_16 (Add) (None, 350, 350, 256 0 add_15[0][0] conv2d_33[0][0] __________________________________________________________________________________________________conv2d_34 (Conv2D) (None, 350, 350, 256 590080 add_16[0][0] __________________________________________________________________________________________________add_17 (Add) (None, 350, 350, 256 0 conv2d_1[0][0] conv2d_34[0][0] __________________________________________________________________________________________________up_sampling2d_1 (UpSampling2D) (None, 1400, 1400, 2 0 add_17[0][0] __________________________________________________________________________________________________conv2d_35 (Conv2D) (None, 1400, 1400, 3 771 up_sampling2d_1[0][0] ==================================================================================================Total params: 19,480,579Trainable params: 19,480,579Non-trainable params: 0__________________________________________________________________________________________________None重要部分就在结尾，靠近输出处：
__________________________________________________________________________________________________add_17 (Add) (None, 350, 350, 256 0 conv2d_1[0][0] conv2d_34[0][0] __________________________________________________________________________________________________up_sampling2d_1 (UpSampling2D) (None, 1400, 1400, 2 0 add_17[0][0] __________________________________________________________________________________________________conv2d_35 (Conv2D) (None, 1400, 1400, 3 771 up_sampling2d_1[0][0] ==================================================================================================现在，看看我运行网络时得到的错误：
Traceback (most recent call last): File "C:/Users/payne/PycharmProjects/PixelEnhancer/trainTest.py", line 280, in <module> setUpImages() File "C:/Users/payne/PycharmProjects/PixelEnhancer/trainTest.py", line 96, in setUpImages setUpData(trainData, testData) File "C:/Users/payne/PycharmProjects/PixelEnhancer/trainTest.py", line 135, in setUpData setUpModel(X_train, Y_train, validateTestData, trainingTestData) File "C:/Users/payne/PycharmProjects/PixelEnhancer/trainTest.py", line 176, in setUpModel train(model, X_train, Y_train, validateTestData, trainingTestData) File "C:/Users/payne/PycharmProjects/PixelEnhancer/trainTest.py", line 192, in train batch_size=32) File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training.py", line 950, in fit batch_size=batch_size) File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training.py", line 787, in _standardize_user_data exception_prefix='target') File "C:\Users\payne\Anaconda3\envs\ml-gpu\lib\site-packages\keras\engine\training_utils.py", line 137, in standardize_input_data str(data_shape))ValueError: Error when checking target: expected conv2d_35 to have shape (1400, 1400, 1) but got array with shape (1400, 1400, 3)为什么我的最后一个卷积层期望得到一个形状为(1400, 1400, 1)的张量，但却得到了形状为(1400, 1400, 3)的张量，而摘要中说UpSampling2D应该返回一个形状为(1400, 1400, 2)的张量？
为了澄清一下背景：这应该是一个网络，它接收一个350x350x3的图像，并输出一个1400x1400x3的图像。
回答：
显然，错误信息并不是专门针对conv2d_35实体的，而是与我的损失函数链接的网络的最后一个实体有关的。
由于我选择了sparse_categorical_crossentropy作为损失函数，它期望得到一个单一维度的向量。
将损失函数设置为mean_squared_error解决了这个问题。
