# How to Calculate Total Loss and Accuracy at Every Epoch and Plot Using Matplotlib in Pytorch – 2021 Guide

Today we are going to discuss how to plot and calculate the total loss and accuracy at every epoch in the code below. The following code is the Pytorch implementation of how to calculate total loss and accuracy at every epoch.

For those who are interested, a summary of how I went about calculating the loss and accuracy at every epoch and plotting it all can be found here.

As a machine learning enthusiast, I was excited to see that the latest version of Pytorch (PyTorch v0.4) was released recently. The new release comes with a lot of promising new features and performance. In this post, I will show you how to calculate total loss and accuracy at every epoch and plot using matplotlib in PyTorch.

Read more about plot loss matplotlib and let us know what you think.PyTorch is a powerful machine learning library that provides a clean interface for building deep learning models. You can understand neural networks by observing how they work during training. Sometimes you want to compare the learning and validation metrics of your PyTorch model instead of showing the learning process.

In this post, you will learn: How to collect and display metrics while training your deep learning models and how to create graphs from the data collected during training. CIFAR10 training and test datasets loading and normalizing with torchvision : Graph of losses on training and validation datasets during training epochs. plt.plot(train_losses, ‘-o’) plt.plot(eval_losses, ‘-o’) plt.xlabel(‘epoch’) plt.ylabel(‘losses’) plt.legend([‘Train’, ‘Valid’]) plt.title(‘Train vs Valid Losses’) plt.show() This code draws a graph with a loss value for each epoch. ### Execute this code in Google Colab.

In many computational jobs, we have to calculate loss and accuracy. This is the simplest loss and accuracy calculation. To calculate loss, we need to find the difference between the actual and target values. To calculate accuracy, we need to find the difference between the actual and the predicted values. You can find the difference more easily as below.. Read more about pytorch training loop with validation and let us know what you think.