Keras卷积自动编码器:图层形状

时间:2016-08-27 14:00:52

标签: python keras autoencoder

我已经获得了大约70,000张训练图像的列表,每张图像的形状(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')

来自here的自动编码器。

它会抛出错误:

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,因为我已经明确地将指定的输入形状更改为(3,30,30)。我是否缺少一些实施技术?

3 个答案:

答案 0 :(得分:2)

您忘记添加border_mode ='相同'在解码器的最后一个convnet层中。

答案 1 :(得分:0)

https://blog.keras.io/building-autoencoders-in-keras.html中,他们忘了添加

'border_mode='same''

例如,在你的第二个卷积层中;

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

答案 2 :(得分:0)

你应该将最后一个卷积层的形状从(1,3,3)变为(3,3,3),如下所示:

decoded = Convolution2D(3, 3, 3, 
                    activation='sigmoid', 
                    border_mode='same')(x)