keras中自动编码器最后一层的裁剪
我有形状keras中自动编码器最后一层的裁剪,keras,crop,convolution,keras-layer,autoencoder,Keras,Crop,Convolution,Keras Layer,Autoencoder,我有形状391 x 400的图像。我尝试使用所述的自动编码器 具体而言,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 `chan
391 x 400
的图像。我尝试使用所述的自动编码器
具体而言,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')
我得到以下信息:
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392x400
降下/裁剪/重塑为391x400
谢谢您的帮助。有一个名为
croping2d
的层。要将最后一层从392 x 400
裁剪到391 x 400
,您可以通过以下方式使用它:
cropped = Cropping2D(cropping=((1, 0), (0, 0)))(decoded)
autoencoder = Model(input_img, cropped)
元组((1,0)、(0,0))
表示从顶部裁剪一行。如果要从底部裁剪,请使用((0,1)、(0,0))
。有关裁剪
参数的详细说明,请参见