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我有一个大约 70,000 个训练图像的列表，每个形状（颜色通道数，高度宽度）= (3, 30, 30)，以及大约 20,000 个测试图像。我的卷积自动编码器定义为：

 # Same as the code above, but with some params changed
# Now let's define the model. 

# Set input dimensions:
input_img = Input(shape=(3, 30, 30))

# Encoder: define a chain of Conv2D and MaxPooling2D layers
x = Convolution2D(128, 3, 3, 
                  activation='relu', 
                  border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)

# at this point, the representation is (8, 4, 4) i.e. 128-dimensional

# Decoder: a stack of Conv2D and UpSampling2D layers
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(64, 3, 3, 
                  activation='relu', 
                  border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(128, 3, 3, 
                  activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, 
                        activation='sigmoid', 
                        border_mode='same')(x)

autoencoder2 = Model(input_img, decoded)
autoencoder2.compile(optimizer='adadelta', loss='mse')

这是来自这里的自动编码器。

它抛出一个错误：

Error when checking model target: expected convolution2d_14 to have shape (None, 1, 28, 28) but got array with shape (76960, 3, 30, 30)

这很奇怪，因为我已经清楚地将指定的输入形状更改为 (3, 30, 30)。我缺少一些实现技术性吗？

4

3 回答 3

2

您忘记在解码器的最后一个 convnet 层中添加 border_mode='same' 。

于 2016-09-17T19:42:49.443 回答
0

您应该将最后一个卷积层的形状从 (1,3,3) 更改为 (3,3,3)，如下所示：

decoded = Convolution2D(3, 3, 3, 
                    activation='sigmoid', 
                    border_mode='same')(x)
于 2017-03-10T09:45:02.387 回答
0

在https://blog.keras.io/building-autoencoders-in-keras.html中，他们忘记添加

'border_mode='same''.

例如，在您的倒数第二个卷积层中；

x = Convolution2D(128, 3, 3, activation='relu')(x)

于 2017-03-03T03:40:00.373 回答