Question

我有形状391 x 400的图像。我尝试使用here所述的自动编码器。

具体来说，我使用了以下代码：

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

input_img = Input(shape=(391, 400, 1))  # adapt this if using `channels_first` image data format

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

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

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

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

我得到以下内容：

ValueError: Error when checking target: expected conv2d_37 to have shape (None, 392, 400, 1) but got array with shape (500, 391, 400, 1)

我需要的是：将最后一层从392 x 400删除/裁剪/重新整形为391 x 400的图层。

感谢您的帮助。

Answer 1

有一个名为Cropping2D的图层。要将最后一个图层从392 x 400裁剪为391 x 400，您可以按以下方式使用它：

cropped = Cropping2D(cropping=((1, 0), (0, 0)))(decoded)
autoencoder = Model(input_img, cropped)

元组((1, 0), (0, 0))表示从顶部裁剪1行。如果您想从底部裁剪，请改用((0, 1), (0, 0))。您可以查看documentation以获取有关cropping参数的详细说明。

在keras中自动编码器的最后一层中裁剪

1 个答案: