I have images of shape 391 x 400. I attempted to use the autoencoder as described here.
Specifically, I have used the following code:
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')
I am getting the following:
ValueError: Error when checking target: expected conv2d_37 to have shape (None, 392, 400, 1) but got array with shape (500, 391, 400, 1)
What I need: a layer that would drop/crop/reshape the last layer from 392 x 400 to 391 x 400.
Thank you for any help.
There's a layer called Cropping2D. To crop the last layer from 392 x 400 to 391 x 400, you can use it by:
cropped = Cropping2D(cropping=((1, 0), (0, 0)))(decoded)
autoencoder = Model(input_img, cropped)
The tuple ((1, 0), (0, 0)) means to crop 1 row from the top. If you want to crop from bottom, use ((0, 1), (0, 0)) instead. You can see the documentation for more detailed description about the cropping argument.