In this tutorial, we will explore how to train a neural network to recognize handwritten digits using PyTorch. If you are familiar with TensorFlow and Keras, this guide will highlight the differences in implementing the same task with PyTorch.
Contents
Loading the Data
To begin, we need to install three Python packages: torch, torchvision, and matplotlib. These packages will be essential for our project. Once installed, we will use the torch and torchvision libraries to load the MNIST handwritten digits dataset. The data will be downloaded from an online source using the torchvision package.
Defining the Model Architecture
Next, we will define the architecture of our convolutional neural network (CNN) model. The CNN will consist of two convolutional layers, a dropout layer, and two fully connected layers. It is important to note that in PyTorch, we manually specify the activation functions in the forward method of our model. This is different from TensorFlow and Keras, where activation functions are specified during the layer definition.
Training the Model
Training a PyTorch model involves some manual steps compared to TensorFlow and Keras. We start by defining an optimizer, which is responsible for updating the model’s parameters based on the calculated gradients. The most common optimizer used is the Adam optimizer. We also define a loss function, such as cross-entropy loss, to measure the difference between the predicted and actual digit labels.
For training, we iterate over the dataset in batches using data loaders. We use the DataLoader class from the torch.utils.data module to load the training and test data in batches. During each training iteration, we calculate the model’s output, compute the loss, backpropagate the loss, and update the parameters using the optimizer. We display the training progress, including the epoch, batch index, loss, and accuracy, to track the model’s performance.
Evaluating the Model
After training the model, we evaluate its performance on the test set. In the evaluation phase, we disable gradient calculation and set the model to evaluation mode. Similar to training, we iterate over the test dataset in batches and calculate the model’s output. We calculate the test loss and the number of correct predictions. Finally, we display the average test loss and accuracy.
Making Predictions
Once the model is trained, we can use it to make predictions on new handwritten digit images. We can pass a single image to the model and receive the predicted digit label. We demonstrate this by loading a handwritten digit image, preprocessing it, passing it through the model, and displaying the predicted label.
This project serves as an introductory guide to PyTorch and the concept of image classification using CNNs. By following this tutorial, you have learned how to train a neural network to recognize handwritten digits. With further exploration, you can apply this knowledge to various other image classification tasks. Thank you for reading, and we hope this tutorial has been informative and helpful. For more exciting technology content, visit Techal.
