How do I implement a training loop in PyTorch?

How do I implement a training loop in PyTorch?

To implement a training loop in PyTorch, you typically follow these steps: 1. Load your dataset. 2. Define your model. 3. Choose a loss function and optimizer. 4. Loop over your data for a specified number of epochs, performing forward and backward passes.

Can you show me a code example?

Sure! Here's a simple example:

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

# Initialize model, loss, and optimizer
model = SimpleModel()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Training loop
for epoch in range(100):
    for data, target in dataloader:
        optimizer.zero_grad()  # Zero the gradients
        output = model(data)   # Forward pass
        loss = criterion(output, target)  # Compute loss
        loss.backward()  # Backward pass
        optimizer.step()  # Update weights

What should I do if my model is overfitting?

If your model is overfitting, you can try several techniques: 1. Use regularization methods like L2 regularization or dropout. 2. Increase your dataset size with data augmentation. 3. Simplify your model architecture. 4. Use early stopping during training.