PyTorch nn

The torch.nn module in PyTorch provides the foundation for building and training neural network models. It includes a wide range of pre-built layers, activation functions, loss functions, and other components necessary for creating complex deep learning models. The torch.nn module is highly flexible and customizable, allowing developers to design and implement neural network architectures tailored to specific tasks.

Syntax

The primary way to define a neural network model in PyTorch is as follows:

import torch.nn as nn

# Define a neural network model
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        # Define layers, activations, and other components here
        # For example: self.layer1 = nn.Linear(in_features, out_features)

    def forward(self, x):
        # Implement the forward pass of the model
        # For example: x = self.layer1(x)
        return x  # Replace 'x' with the final output after applying all layers

    # Define other methods if necessary

• NetworkNeural: A custom neural network class that subclasses the nn.Module.

Example

The following example demonstrates how to define a simple neural network model using the nn.Module with two fully connected layers, apply a ReLU activation function to the first layer’s output, and create an instance of the model:

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

# Create a model instance
model = SimpleNN()