Training your First Multi-Layer Perceptron (MLP) in Pytorch: A Hands-On Tutorial

As a university tutor, my recent experience teaching MSc students about deep learning and training their first multi-layer perceptron (MLP) in Pytorch was both eye-opening and rewarding. The beginners in the field had many questions that made me reflect on my own journey as a beginner in deep learning. In this blog post, I will share my story and provide a tutorial on training an MLP in Pytorch for those who are familiar with numpy, TensorFlow, or those looking to deepen their understanding of deep learning.

We started by importing the necessary libraries for our tutorial, including torch, torch.nn, and torchvision. The torch.nn package contains the layers needed to train our neural network, while torch.nn.functional contains functions that can be called directly without prior initialization. We also discussed the importance of using a GPU for faster training speed, especially when working with larger datasets and models.

Next, we looked at image transforms and how to normalize input data to improve training stability. We used the CIFAR10 dataset for our tutorial, which contains 50K training images and 10K test images. We discussed the importance of data splits for training, validation, and testing to ensure reliable performance metrics.

We also explored the DataLoader class in Pytorch, which allows us to load batches of images and labels for training our model. The training loop and validation loop functions were implemented to train and evaluate the model on the dataset. We discussed the importance of model design choices, such as batch size, model architecture, and regularization.

In the tutorial, we built an MLP model with a single hidden layer and trained it on the CIFAR10 dataset. We achieved a validation accuracy of 53.52%, which is a good starting point for further improvements. We discussed the limitations of our classifier and encouraged readers to experiment with different model architectures and datasets.

In conclusion, training an MLP in Pytorch is a great way to dive into deep learning and gain practical experience with neural networks. By following the tutorial and experimenting with different designs, you can improve your understanding and skills in deep learning. Check out the full code on GitHub and stay tuned for more tutorials and projects to further enhance your knowledge in this exciting field.