Convolutional Layer Hacking with Python and Numpy

Convolutional Layer Hacking with Python and Numpy

Create a convolutional layer from scratch in python, hack its weights with custom kernels, and verify that its results match with pytorch

If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. We will: Code from scratch in python a convolutional layer to understand bit by bit what is going on when we pass data through one of these layers. Hack the parameters of the convnet to produce a mix of custom kernels and random ones. Compare the results using Pytorch and Python-numpy to verify that we obtain exactly the same outputs. Ready? Let’s do it.

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