PyTorch Machine Learning Library

PyTorch is an open-source machine learning library used for a wide variety of tasks such as deep learning, natural language processing (NLP), and computer vision. It provides a flexible platform to build machine learning models and comes with strong support for GPU acceleration, making it popular among researchers and developers.

Key Features of PyTorch

Tensors: PyTorch provides multi-dimensional arrays similar to NumPy but with GPU support for faster computation.
Autograd: PyTorch uses automatic differentiation to compute gradients, making it easier to implement and optimize neural networks.
Dynamic Computational Graphs: PyTorch builds computational graphs dynamically, making it easier to debug and modify.
Deep Learning Models: PyTorch includes pre-built models in the torchvision and torchtext libraries for various computer vision and NLP tasks.
Extensibility: You can build custom layers, models, and optimization techniques.

PyTorch Example

How to use PyTorch to create a Neural Network for classifying MNIST digits:

#!/usr/bin/env python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# Define a simple neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28*28, 128)  # Fully connected layer 1
        self.fc2 = nn.Linear(128, 10)     # Fully connected layer 2 (10 classes)

    def forward(self, x):
        x = x.view(-1, 28*28)            # Flatten the image
        x = torch.relu(self.fc1(x))      # Apply ReLU to fc1
        x = self.fc2(x)                  # Final output (logits)
        return x

# Load dataset and preprocess
transform   = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
trainset    = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# Initialize the model, loss function, and optimizer
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

# Training loop
for epoch in range(2):
    running_loss = 0.0
    for inputs, labels in trainloader:
        optimizer.zero_grad()                   # Zero the gradients
        outputs = net(inputs)                   # Forward pass
        loss    = criterion(outputs, labels)    # Compute the loss
        loss.backward()                         # Backward pass
        optimizer.step()                        # Optimize
        running_loss += loss.item()
    print(f'Epoch {epoch+1}, Loss: {running_loss / len(trainloader)}')

print("Finished Training")

Common PyTorch Libraries

torchvision: Contains datasets, models, and transforms for computer vision.
torchtext : For NLP models and datasets.
torchaudio : For Audio and Speech Processing.