What is the architecture of a PyTorch model?

What is the architecture of a PyTorch model?

A PyTorch model typically consists of layers defined in a class that inherits from torch.nn.Module. You define the layers in the __init__ method and implement the forward pass in the forward method.

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

Sure! Here's a simple feedforward neural network example:

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 are the common layers used in PyTorch models?

Common layers include nn.Linear for fully connected layers, nn.Conv2d for convolutional layers, and nn.LSTM for recurrent layers.

How do I train a model in PyTorch?

To train a model, you typically define a loss function, an optimizer, and then loop through your data, performing forward and backward passes to update the model weights.