Visualizing the Feature Maps and Filters by Convolutional Neural Networks

A simple guide for interpreting what Convolutional Neural Network is learning using Pytorch

Illustration by Author

The post is the fourth in a series of guides to building deep learning models with Pytorch. Below, there is the full series:

1. Pytorch Tutorial for Beginners
2. Manipulating Pytorch Datasets
3. Understand Tensor Dimensions in DL models
4. CNN & Feature visualizations (this post)
5. Hyperparameter tuning with Optuna
6. K Fold Cross Validation
7. Convolutional Autoencoder
8. Denoising Autoencoder
9. Variational Autoencoder

The goal of the series is to make Pytorch more intuitive and accessible as possible through examples of implementations. There are many tutorials on the Internet to use Pytorch to build many types of challenging models, but it can also be confusing at the same time because there are always slight differences when you pass from one tutorial to another. In this series, I want to start from the simplest topics to the more advanced ones.

Introduction