python - 如何匹配 Conv2D AutoEncoder 的输入和输出形状

Question

有一组具有以下形状的黑白图像(1000, 11, 1)。我正在尝试修改keras mnist 示例以使用我的数据，因此我编写了以下代码：

input_img = layers.Input(shape=(1000, 11, 1))

x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.MaxPooling2D((2, 2), padding='same')(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = layers.MaxPooling2D((2, 2), padding='same')(x)

x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation='relu')(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

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

打印摘要，我可以看到输出形状与输入形状不同：

Model: "model_16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_18 (InputLayer)        [(None, 1000, 11, 1)]     0         
_________________________________________________________________
conv2d_119 (Conv2D)          (None, 1000, 11, 16)      160       
_________________________________________________________________
max_pooling2d_51 (MaxPooling (None, 500, 6, 16)        0         
_________________________________________________________________
conv2d_120 (Conv2D)          (None, 500, 6, 8)         1160      
_________________________________________________________________
max_pooling2d_52 (MaxPooling (None, 250, 3, 8)         0         
_________________________________________________________________
conv2d_121 (Conv2D)          (None, 250, 3, 8)         584       
_________________________________________________________________
max_pooling2d_53 (MaxPooling (None, 125, 2, 8)         0         
_________________________________________________________________
conv2d_122 (Conv2D)          (None, 125, 2, 8)         584       
_________________________________________________________________
up_sampling2d_51 (UpSampling (None, 250, 4, 8)         0         
_________________________________________________________________
conv2d_123 (Conv2D)          (None, 250, 4, 8)         584       
_________________________________________________________________
up_sampling2d_52 (UpSampling (None, 500, 8, 8)         0         
_________________________________________________________________
conv2d_124 (Conv2D)          (None, 498, 6, 16)        1168      
_________________________________________________________________
up_sampling2d_53 (UpSampling (None, 996, 12, 16)       0         
_________________________________________________________________
conv2d_125 (Conv2D)          (None, 996, 12, 1)        145       
=================================================================
Total params: 4,385
Trainable params: 4,385
Non-trainable params: 0
_________________________________________________________________

事实上，训练失败并出现错误：

ValueError: logits and labels must have the same shape ((None, 996, 12, 1) vs (None, 1000, 11, 1))

我究竟做错了什么？如何修复我的代码以使用我的图像尺寸？

标签： pythontensorflowmachine-learningkerasautoencoder