Coding Llama 3: Building from scratch in PyTorch – Part 1

Welcome to part 1 of our Coding Llama series, where we will be exploring how to build a neural network from scratch using PyTorch. In this tutorial, we will cover the basics of creating a simple neural network with PyTorch and training it to perform a binary classification task.

Setting up your environment

Before we get started, make sure you have PyTorch installed on your system. You can install PyTorch by following the instructions on the official PyTorch website.

Creating the neural network architecture

Now that you have PyTorch installed, let’s start by defining the architecture of our neural network. We will be building a simple feedforward neural network with one hidden layer. Here’s the code snippet to create the neural network:

    import torch
    import torch.nn as nn
    
    class SimpleNN(nn.Module):
        def __init__(self):
            super(SimpleNN, self).__init__()
            self.fc1 = nn.Linear(2, 4)
            self.relu = nn.ReLU()
            self.fc2 = nn.Linear(4, 1)
            self.sigmoid = nn.Sigmoid()
        
        def forward(self, x):
            x = self.fc1(x)
            x = self.relu(x)
            x = self.fc2(x)
            x = self.sigmoid(x)
            return x
    

Training the neural network

Now that we have defined our neural network architecture, let’s move on to training the model. We will generate some random data for our binary classification task and use PyTorch’s built-in functions to train the model. Here’s a code snippet to train the neural network:

    import torch.optim as optim
    import torch.nn.functional as F
    
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    model = SimpleNN().to(device)
    criterion = nn.BCELoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01)
    
    for epoch in range(100):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
    

Conclusion

Congratulations! You have successfully built and trained a simple neural network from scratch using PyTorch. In the next part of this series, we will delve deeper into more advanced topics such as optimizing hyperparameters and improving the performance of our neural network. Stay tuned for more Coding Llama tutorials!