PyTorch Loss Functions: The Ultimate Guide

PyTorch Loss Functions: The Ultimate Guide

In this article, we’ll talk about popular loss functions in PyTorch, and about building custom loss functions. Once you’re done reading, you should know which one to choose for your project.

The way you configure your loss functions can make or break the performance of your algorithm. By correctly configuring the loss function, you can make sure your model will work how you want it to.

Your neural networks can do a lot of different tasks. Whether it’s classifying data, like grouping pictures of animals into cats and dogs, or regression tasks, like predicting monthly revenues, or anything else. Every task has a different output and needs a different type of loss function.

Luckily for us, there are loss functions we can use to make the most of machine learning tasks. 

In this article, we’ll talk about popular loss functions in PyTorch, and about building custom loss functions. Once you’re done reading, you should know which one to choose for your project. 

We’ll talk about:

  • What are loss functions (in PyTorch or other)?
  • How to add PyTorch loss functions
  • Which loss functions are available in PyTorch?
  • How to create a custom loss function in PyTorch

What are the loss functions?

Before we jump into PyTorch specifics, let’s refresh our memory of what loss functions are. 

Loss functions are used to gauge the error between the prediction output and the provided target value. A loss function tells us how far the algorithm model is from realizing the expected outcome. The word ‘loss’ means the penalty that the model gets for failing to yield the desired results.

For example, a loss function (let’s call it J) can take the following two parameters:

  • Predicted output (y_pred)
  • Target value (y)

neural network loss

Illustration of a neural network loss

This function will determine your model’s performance by comparing its predicted output with the expected output. If the deviation between y_pred *and *y is very large, the loss value will be very high.

If the deviation is small or the values are nearly identical, it’ll output a very low loss value. Therefore, you need to use a loss function that can penalize a model properly when it is training on the provided dataset.

Loss functions change based on the problem statement that your algorithm is trying to solve.

deep learning model evaluation machine-learning

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