Master PyTorch with MNIST Classification Training

Table of Contents

1. Introduction
2. Basic Concepts
   1. Training Loop
   2. Optimizer
   3. Loss Function
3. Training a Classifier on MNIST using PyTorch
   1. Importing Libraries
   2. Creating the Network
   3. Defining the Optimizer and Loss Function
   4. Implementing the Training Loop
   5. Adding a Residual Connection
4. Converting PyTorch Code to PyTorch Lightning
   1. Benefits of PyTorch Lightning
   2. Training on Multiple GPUs and TPUs
   3. Automatic Logging and Checkpoints
5. Conclusion

Training a Classifier on MNIST using PyTorch

In this article, we will learn how to train a classifier on the MNIST dataset using PyTorch. We will cover the basic concepts of a training loop, optimizer, and loss function. If You are already familiar with these concepts, you can skip to the last section for a quick summary.

To get started, we will be using Google Colab. You can Create a new notebook on Colab and follow along. We will begin by importing the necessary libraries, including PyTorch.

import torch
from torch import nn

Next, we will create our model. In this case, we will start with a simple multi-layer perceptron (MLP) with fully connected layers. Our model will consist of an input layer, two Hidden layers, and an output layer. The input layer will have 28*28 = 784 nodes, representing the pixels in each image. We will choose 64 nodes for each hidden layer and 10 nodes for the output layer, which corresponds to the 10 digits in the MNIST dataset.

model = nn.Sequential(
    nn.Linear(784, 64),
    nn.ReLU(),
    nn.Linear(64, 64),
    nn.ReLU(),
    nn.Linear(64, 10)
)

After creating the model, we define our optimizer. We will use stochastic gradient descent (SGD) as our optimizer, which is a common choice for training neural networks. We provide the model parameters to the optimizer, including the learning rate.

optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

Next, we define our loss function. Since We Are dealing with a classification task, we will use cross-entropy loss, which is a commonly used loss function for classification problems.

loss_function = nn.CrossEntropyLoss()

Now, we can move on to implementing the training loop. We will iterate over the dataset for a given number of epochs. Each epoch consists of multiple batches, which we will retrieve from the training loader. For each batch, we will compute the forward pass, the objective function (loss), and then backpropagate the gradients. Finally, we will update the parameters using the optimizer.

epochs = 5

for epoch in range(epochs):
    for batch in training_loader:
        x, y = batch
        x = x.view(x.size(0), -1)  # Reshape input to a flat vector

        optimizer.zero_grad()
        logits = model(x)
        loss = loss_function(logits, y)
        loss.backward()
        optimizer.step()

To improve the performance of our model, we can add a residual connection. This allows us to bypass certain layers and create shortcuts in the network. In this example, we add a residual connection between the first and Second hidden layers.

class ResidualModel(nn.Module):
    def __init__(self):
        super(ResidualModel, self).__init__()
        self.l1 = nn.Linear(784, 64)
        self.l2 = nn.Linear(64, 64)
        self.dropout = nn.Dropout(0.1)
        self.l3 = nn.Linear(64, 10)

    def forward(self, x):
        h1 = nn.functional.relu(self.l1(x))
        h2 = nn.functional.relu(self.l2(h1))
        h2 += h1  # Residual connection
        output = self.dropout(h2)
        return self.l3(output)

model = ResidualModel()

In the final section, we will explore how to convert our PyTorch code into PyTorch Lightning. PyTorch Lightning is a lightweight wrapper for PyTorch that simplifies the training process and provides additional features such as training on multiple GPUs and automatic logging.

In conclusion, we have learned how to train a classifier on the MNIST dataset using PyTorch. We covered the basic concepts of a training loop, optimizer, and loss function. We also explored how to add a residual connection to our model. Lastly, we discussed the benefits of using PyTorch Lightning for training neural networks.

Highlights

• Training a classifier on the MNIST dataset using PyTorch
• Exploring basic concepts such as training loop, optimizer, and loss function
• Implementing a multi-layer perceptron (MLP) model with fully connected layers
• Adding a residual connection to improve model performance
• Converting PyTorch code to PyTorch Lightning for convenience and additional features

FAQ:

Q: What is PyTorch? A: PyTorch is an open-source machine learning library used for building and training neural networks.

Q: What is the MNIST dataset? A: The MNIST dataset is a collection of 60,000 handwritten digits commonly used for training and evaluating machine learning models.

Q: What is a residual connection? A: A residual connection is a technique used in neural networks to allow gradients to flow directly from earlier layers to later layers, bypassing certain layers. This helps mitigate the vanishing gradient problem and enables more efficient training.

Q: What is PyTorch Lightning? A: PyTorch Lightning is a lightweight wrapper for PyTorch that simplifies the training process and provides additional features such as training on multiple GPUs and automatic logging.

Q: What are the benefits of using PyTorch Lightning? A: PyTorch Lightning makes it easier to write clean and organized code, reduces boilerplate code, and provides convenient features for training and logging.

Q: Can PyTorch models be trained on multiple GPUs? A: Yes, PyTorch models can be trained on multiple GPUs using parallel processing. This can significantly speed up training time for large models and datasets.