Python，keras，卷积自动编码器

Question

我正在尝试在 keras 中创建我的第一个卷积自动编码器，但我遇到了层输出形状的问题。有我的代码：

input_img = Input(shape=X_train.shape[1:])

x = Conv2D(32, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3))(input_img)
x = MaxPooling2D(pool_size=(2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3))(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

x = Conv2D(16, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3))(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3))(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_img, decoded)
print(autoencoder.summary())
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(X_train, X_train, epochs=50, batch_size=32)

结果：

_________________________________________________________________
Layer (type)                 Output Shape              Param   
=================================================================
input_87 (InputLayer)        (None, 32, 32, 3)         0         
_________________________________________________________________
conv2d_327 (Conv2D)          (None, 32, 32, 3)         9248      
_________________________________________________________________
max_pooling2d_136 (MaxPoolin (None, 32, 16, 2)         0         
_________________________________________________________________
conv2d_328 (Conv2D)          (None, 16, 16, 2)         4624      
_________________________________________________________________
max_pooling2d_137 (MaxPoolin (None, 16, 8, 1)          0         
_________________________________________________________________
conv2d_329 (Conv2D)          (None, 16, 8, 1)          2320      
_________________________________________________________________
up_sampling2d_124 (UpSamplin (None, 16, 16, 2)         0         
_________________________________________________________________
conv2d_330 (Conv2D)          (None, 32, 16, 2)         4640      
_________________________________________________________________
up_sampling2d_125 (UpSamplin (None, 32, 32, 4)         0         
_________________________________________________________________
conv2d_331 (Conv2D)          (None, 1, 32, 4)          289       
=================================================================
Total params: 21,121
Trainable params: 21,121
Non-trainable params: 0
_________________________________________________________________
None

当然还有错误：

ValueError: Error when checking target: expected conv2d_331 to have shape (None, 1, 32, 4) but got array with shape (50000, 32, 32, 3)

你知道我做错了什么吗？为什么最后一个 UpSampling2D 返回那个形状？

Answer 1

所以，您的keras 似乎已将其图像尺寸设置为channel_first（可能是Theano 作为后端）这意味着您输入的最后两个维度被视为空间维度而不是第二个和第三。另一件事是您的输出应该有3 过滤器而不是1 因为这将导致目标尺寸不匹配。总结一下：

1. 为了正确设置您的输入，您需要切换到 tensorflow 并将频道顺序更改为 channel_last 或通过以下方式转换您的输入：

   X = X.transpose([0, 3, 1, 2])
   

2. 更改以下行：

   decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)