Welcome to the PyTorch API documentation. Here you will find comprehensive information about the PyTorch library, including its various modules, functions, and classes.

Overview

PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. It provides a flexible platform for deep learning research and development.

Getting Started

If you are new to PyTorch, we recommend starting with the PyTorch Tutorials. These tutorials cover the basics of PyTorch and help you get up and running quickly.

Core Modules

  • torch: The core PyTorch library, providing tensors, differentiable programming, and neural network modules.
  • torch.nn: Contains neural network modules such as fully connected layers, convolutional layers, and recurrent layers.
  • torch.optim: Optimizers for training neural networks, such as SGD, Adam, and RMSprop.
  • torch.utils.data: Provides utilities for data loading and preprocessing, including data loaders and datasets.

Installation

To install PyTorch, please visit the official installation guide.

Example

Here is a simple example of using PyTorch to create a neural network:

import torch
import torch.nn as nn

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 50)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(50, 1)

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

# Create an instance of the network
model = SimpleNN()

# Print the network structure
print(model)

Resources

Conclusion

We hope this documentation helps you get started with PyTorch. For more information, please visit the PyTorch website.

neural_network