Define PyTorch Model Tutorial

Welcome to the tutorial on defining PyTorch models. PyTorch is a popular deep learning framework that provides a dynamic computational graph. In this guide, we will walk you through the process of defining a PyTorch model.

Introduction to PyTorch Models

A PyTorch model is a collection of layers that work together to perform a specific task, such as image classification or natural language processing. In PyTorch, you can define models using the nn.Module class.

Step-by-Step Guide

1. Import Necessary Libraries

First, you need to import the necessary libraries.

import torch
import torch.nn as nn

2. Define the Model

Next, define your model by subclassing nn.Module. In this example, we will create a simple neural network for image classification.

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64 * 32 * 32, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.max_pool2d(x, 2)
        x = torch.relu(self.conv2(x))
        x = torch.max_pool2d(x, 2)
        x = x.view(-1, 64 * 32 * 32)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

3. Instantiate the Model

Create an instance of the model.

model = SimpleNet()

4. Training the Model

To train the model, you need to define a loss function and an optimizer. Here, we will use the cross-entropy loss and the Adam optimizer.

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

5. Training Loop

Now, you can train the model using a training loop.

for epoch in range(10):  # number of epochs
    for data, target in train_loader:  # your data loader
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    print(f'Epoch {epoch+1}, Loss: {loss.item()}')