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?
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)
在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)