这是我的模特：
filters = 256
kernel_size = 3
strides = 1
factor = 4 # the factor of upscaling
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：
__________________________________________________________________________________________________
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: 19,480,579
Trainable params: 19,480,579
Non-trainable params: 0
__________________________________________________________________________________________________
None
重要的部分在最后，靠近输出：
__________________________________________________________________________________________________
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，看看运行网络时出现的错误：
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期望有一个(1400, 1400, 1)张量，却得到一个(1400, 1400, 3)张量，而摘要说UpSampling2D应该返回一个(1400, 1400, 2)张量？
为了澄清一下上下文，我们假设它是一个网络，该网络可以拍摄350x350x3的图像并输出1400x1400x3的图像。
答案 0 :(得分：0)
显然，错误消息与conv2d_35实体没有特别关系，而是与我的损失函数链接的网络的最后一个实体。
由于我选择了sparse_categorical_crossentropy作为损失函数，因此它期望一个单一的向量。
将损失设置为mean_squared_error即可解决问题。