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How do I calculate the partial derivative with respect to \$x\$?

I am trying to implement CNN using python Numpy. I searched so much, but all I found was for one filter with one channel for Convolution. Suppose we have an X as Image with this shape: (N_Height, N_Width, N_Channel) = (5,5,3) And Let’s say I have 16 filters with this shape: (F_Height, F_Width, N_Channel) = […]

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How do I calculate the partial derivative with respect to \$x\$?

I am trying to implement CNN using python Numpy. I searched so much, but all I found was for one filter with one channel for Convolution. Suppose we have an X as Image with this shape: (N_Height, N_Width, N_Channel) = (5,5,3) And Let’s say I have 16 filters with this shape: (F_Height, F_Width, N_Channel) = […]

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gradient respect to Input in CNN Backpropagation

I am trying to implement CNN using python Numpy. I searched so much, but all I found was for one filter with one channel for Convolution. Suppose we have an X as Image with this shape: (N_Height, N_Width, N_Channel) = (5,5,3) And Let’s say I have 16 filters with this shape: (F_Height, F_Width, N_Channel) = […]

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ValueError: No gradients provided for any variable in Tensorflow 2.0, WAE

I am a tensorflow2.0 learner who is trying to reproduce the code of Wasserstein AutoEncoder or Adversarial AutoEncoder and do some interesting based on that on my own. My code below is based on timsainb/tensorflow2-generative-models with some of modifications. import tensorflow as tf import numpy as np import pandas as pd import tensorflow_probability as tfp […]