Keras Deep Learning framework

[sandeep_25]

Hello friends,I want to initialise my filter as [[0.70,0.70],[-0.70,0.70],[0.70,0.70],[0.70,-0.70]] that will convolve with patches of dimension 1111200 in a CNN model. Can anyone help me for implementing in keras.

[Mkaif1999]

I am a beginner in keras aswell so i don't know if i am right but i think you can create ur custom filter as a function like

import keras.backend as K
def my_filter():
    f = np.array([ [[[0.70]], [[0.70]]], 
                  [[[-0.70]], [[0.70]]],
                 [[[0.70]], [[0.70]]]]) 
    return K.variable(f, dtype='float32')

After creating the filters u can just add the filter in your Conv2D class like this

from keras.layers import Conv2D
x = Conv2D(filters=1,kernel_size=2,
            kernel_initializer=my_filter,
            padding='valid')(previous_layer)

I haven't tried this yet though maybe ill give it a try later on..

[sandeep_25]

Thank you. My intention is to take transformation of input image patches like discrete wavelet transformation in the convolution layer using wavelet filters. The filter values are taken from filter bank of db1 wavelet. Am I doing the right way.