Classifying images from Fashion MNIST using logistic regression

In this tutorial, we'll use our existing knowledge of PyTorch and linear regression to solve a very different kind of problem: image classification. We'll use the famous Fashion MNIST dataset as our training dataset. It consists of 28px by 28px grayscale images of different accessories , along with labels for each image indicating which digit it represents. Here are some sample images from the dataset:

System setup

This tutorial takes a code-first approach towards learning PyTorch, and you should try to follow along by running and experimenting with the code yourself. The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Kaggle". This will run the notebook on Kaggle, a free online service for running Jupyter notebooks (you might need to create an account).

Running on your computer locally

(Skip this if you're running on Kaggle) To run this notebook locally, clone this notebook, install the required dependencies using conda, and start Jupyter by running the following commands on the terminal / Conda prompt:

pip install jovian --upgrade                      # Install the jovian library 
jovian clone aakashns/03-logistic-regression      # Download notebook & dependencies
cd 03-logistic-regression                         # Enter the created directory 
conda create -n 03-logistic-regression python=3.8 # Create an environment
conda activate 03-logistic-regression             # Activate virtual env
jupyter notebook                                  # Start Jupyter

You can find the notebook_id by cliking the Clone button at the top of this page on Jovian. For a more detailed explanation of the above steps, check out the System setup section in the first notebook.

Exploring the Data

We begin by importing torch and torchvision. torchvision contains some utilities for working with image data. It also contains helper classes to automatically download and import popular datasets like MNIST.