我正在尝试构建卷积自动编码器，而我正在使用的数据集包含25 x 25张图像。
input_img = Input ( shape = (25 , 25, 1))
layer = input_img
layer = Conv2D (128 , kernel_size =(3 , 3) , activation = 'relu' , padding = 'same')( layer )
layer = MaxPooling2D ( pool_size =(2 , 2) , padding = 'same')( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = MaxPooling2D ( pool_size =(2 , 2) , padding = 'same')( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = Flatten ()( layer )
layer = Dense (32 , activation = 'relu')( layer )
layer = Dense (6)( layer )
encoded = layer
layer = Dense (32 , activation = 'relu')( encoded )
layer = Dense (6272 , activation = 'relu')( layer )
layer = Reshape ((7, 7, 128))( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = UpSampling2D ((2 ,2))( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = UpSampling2D ((2 ,2))( layer )
layer = Conv2D (1, kernel_size =(3 , 3) , padding = 'same')( layer )
autoencoder = Model ( input_img , layer )
但是，当我尝试执行此操作时，会得到以下尺寸：
input_35 (InputLayer) (None, 25, 25, 1) 0
_________________________________________________________________
conv2d_208 (Conv2D) (None, 25, 25, 128) 1280
_________________________________________________________________
max_pooling2d_72 (MaxPooling (None, 13, 13, 128) 0
_________________________________________________________________
conv2d_209 (Conv2D) (None, 13, 13, 128) 147584
_________________________________________________________________
max_pooling2d_73 (MaxPooling (None, 7, 7, 128) 0
_________________________________________________________________
conv2d_210 (Conv2D) (None, 7, 7, 128) 147584
_________________________________________________________________
flatten_32 (Flatten) (None, 6272) 0
_________________________________________________________________
dense_125 (Dense) (None, 32) 200736
_________________________________________________________________
dense_126 (Dense) (None, 6) 198
_________________________________________________________________
dense_127 (Dense) (None, 32) 224
_________________________________________________________________
dense_128 (Dense) (None, 6272) 206976
_________________________________________________________________
reshape_74 (Reshape) (None, 7, 7, 128) 0
_________________________________________________________________
conv2d_211 (Conv2D) (None, 7, 7, 128) 147584
_________________________________________________________________
up_sampling2d_72 (UpSampling (None, 14, 14, 128) 0
_________________________________________________________________
conv2d_212 (Conv2D) (None, 14, 14, 128) 147584
_________________________________________________________________
up_sampling2d_73 (UpSampling (None, 28, 28, 128) 0
_________________________________________________________________
conv2d_213 (Conv2D) (None, 28, 28, 1) 1153
_________________________________________________________________
reshape_75 (Reshape) (None, 1, 784) 0
_________________________________________________________________
activation_14 (Activation) (None, 1, 784) 0
_________________________________________________________________
reshape_76 (Reshape) (None, 28, 28, 1) 0
我希望输入和输出尺寸完全相同，而且我也不知道为什么不采样层选择（14,14,128）而卷积层选择（13,13,128）。
答案 0 :(得分：1)
您可以使用ZeroPadding2D或Cropping2D层。
input_img = Input ( shape = (25 , 25, 1))
layer = input_img
# layer = ZeroPadding(((3,0), (3,0)))( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) , activation = 'relu' , padding = 'same')( layer )
layer = MaxPooling2D ( pool_size =(2 , 2) , padding = 'same')( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = MaxPooling2D ( pool_size =(2 , 2) , padding = 'same')( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = Flatten ()( layer )
layer = Dense (32 , activation = 'relu')( layer )
layer = Dense (6)( layer )
encoded = layer
layer = Dense (32 , activation = 'relu')( encoded )
layer = Dense (6272 , activation = 'relu')( layer )
layer = Reshape ((7, 7, 128))( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = UpSampling2D ((2 ,2))( layer )
layer = Conv2D (128 , kernel_size =(3 , 3) ,activation = 'relu' , padding = 'same')( layer )
layer = UpSampling2D ((2 ,2))( layer )
layer = Conv2D (1, kernel_size =(3 , 3) , padding = 'same')( layer )
layer = Cropping2D(((3,0), (3,0)))( layer )
autoencoder = Model ( input_img , layer )
结果：
Model: "model_8"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_12 (InputLayer) [(None, 25, 25, 1)] 0
_________________________________________________________________
zero_padding2d_8 (ZeroPaddin (None, 28, 28, 1) 0
_________________________________________________________________
conv2d_54 (Conv2D) (None, 28, 28, 128) 1280
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 14, 14, 128) 0
_________________________________________________________________
conv2d_55 (Conv2D) (None, 14, 14, 128) 147584
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 7, 7, 128) 0
_________________________________________________________________
conv2d_56 (Conv2D) (None, 7, 7, 128) 147584
_________________________________________________________________
flatten_9 (Flatten) (None, 6272) 0
_________________________________________________________________
dense_37 (Dense) (None, 32) 200736
_________________________________________________________________
dense_38 (Dense) (None, 6) 198
_________________________________________________________________
dense_39 (Dense) (None, 32) 224
_________________________________________________________________
dense_40 (Dense) (None, 6272) 206976
_________________________________________________________________
reshape_9 (Reshape) (None, 7, 7, 128) 0
_________________________________________________________________
conv2d_57 (Conv2D) (None, 7, 7, 128) 147584
_________________________________________________________________
up_sampling2d_18 (UpSampling (None, 14, 14, 128) 0
_________________________________________________________________
conv2d_58 (Conv2D) (None, 14, 14, 128) 147584
_________________________________________________________________
up_sampling2d_19 (UpSampling (None, 28, 28, 128) 0
_________________________________________________________________
conv2d_59 (Conv2D) (None, 28, 28, 1) 1153
=================================================================
Total params: 1,000,903
Trainable params: 1,000,903
Non-trainable params: 0
_________________________________________________________________