How do I train a model using PyTorch?

How do I train a model using PyTorch?

To train a model in PyTorch, you need to define your model architecture, choose a loss function, and an optimizer.

Can you give me an example of a simple neural network?

Sure! Here's a simple feedforward neural network:

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

What about the training loop?

The training loop typically involves:

1. Forward pass: Compute predictions.
2. Compute loss: Compare predictions to true values.
3. Backward pass: Compute gradients.
4. Update weights: Use the optimizer to adjust weights.

How do I evaluate the model after training?

To evaluate the model, you can use a separate validation dataset and compute metrics like accuracy or loss on that dataset.