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Working with Images & Logistic Regression in PyTorch

This tutorial covers the following topics:

  • Working with images in PyTorch (using the MNIST dataset)
  • Splitting a dataset into training, validation, and test sets
  • Creating PyTorch models with custom logic by extending the nn.Module class
  • Interpreting model outputs as probabilities using Softmax and picking predicted labels
  • Picking a useful evaluation metric (accuracy) and loss function (cross-entropy) for classification problems
  • Setting up a training loop that also evaluates the model using the validation set
  • Testing the model manually on randomly picked examples
  • Saving and loading model checkpoints to avoid retraining from scratch
import jovian

Working with Images

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 MNIST Handwritten Digits Database as our training dataset. It consists of 28px by 28px grayscale images of handwritten digits (0 to 9) and labels for each image indicating which digit it represents. Here are some sample images from the dataset: