What is PyTorch ?

Last Updated : 01 Mar, 2025

PyTorch is a deep learning library built on Python and Torch (a Lua-based framework). It provides GPU acceleration, dynamic computation graphs, and an intuitive interface for deep learning researchers and developers. PyTorch follows a “define-by-run” approach, meaning that its computational graphs are constructed on the fly, allowing for better debugging and model customization.

Key Features of PyTorch

PyTorch uses dynamic graphs, allowing flexibility in model execution and debugging.
PyTorch provides an automatic differentiation engine that simplifies gradient computation for deep learning.
PyTorch supports CUDA, allowing computations to be performed efficiently on GPUs.

How to Install PyTorch?

PyTorch can be installed on Windows, macOS, and Linux using pip for CPU (without GPU):

!pip install torch torchvision torchaudio

PyTorch Tensors

Tensors are the fundamental data structures in PyTorch, similar to NumPy arrays but with GPU acceleration capabilities. PyTorch tensors support automatic differentiation, making them suitable for deep learning tasks.

Python

import torch  # Creating a 1D tensor x = torch.tensor([1.0, 2.0, 3.0]) print('1D Tensor: \n', x)  # Creating a 2D tensor y = torch.zeros((3, 3)) print('2D Tensor: \n', y)

Output:

1D Tensor:
tensor([1., 2., 3.])

2D Tensor:
tensor([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])

Operations on Tensors

Python

a = torch.tensor([1.0, 2.0]) b = torch.tensor([3.0, 4.0])  # Element-wise addition print('Element Wise Addition of a & b: \n', a + b)  # Matrix multiplication print('Matrix Multiplication of a & b: \n',        torch.matmul(a.view(2, 1), b.view(1, 2)))

Output:

Element Wise Addition of a & b:
tensor([4., 6.])

Matrix Multiplication of a & b:
tensor([[3., 4.],
[6., 8.]])

Reshaping and Transposing Tensors

Python

import torch  t = torch.tensor([[1, 2, 3, 4],                  [5, 6, 7, 8],                  [9, 10, 11, 12]])  # Reshaping print("Reshaping") print(t.reshape(6, 2))  # Resizing (deprecated, use reshape) print("\nResizing") print(t.view(2, 6))  # Transposing print("\nTransposing") print(t.transpose(0, 1))

Output:

Reshaping
tensor([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10],
[11, 12]])

Resizing
tensor([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12]])

Transposing
tensor([[ 1, 5, 9],
[ 2, 6, 10],
[ 3, 7, 11],
[ 4, 8, 12]])

Autograd and Computational Graphs

The autograd module automates gradient calculation for backpropagation. This is crucia in training deep neural networks.

Python

x = torch.tensor(2.0, requires_grad=True) y = x ** 2 y.backward() print(x.grad) #(dy/dx = 2x = 4 when x=2)

Output:

tensor(4.)

PyTorch dynamically creates a computational graph that tracks operations and gradients for backpropagation.

Building Neural Networks in PyTorch

In PyTorch, neural networks are built using the torch.nn module, where:

nn.Linear(in_features, out_features) defines a fully connected (dense) layer.
Activation functions like torch.relu, torch.sigmoid, or torch.softmax are applied between layers.
forward() method defines how data moves through the network.

To build a neural network in PyTorch, we create a class that inherits from torch.nn.Module and defines its layers and forward pass.

Python

class NeuralNetwork(nn.Module):     def __init__(self):         super(NeuralNetwork, self).__init__()         self.fc1 = nn.Linear(10, 16)  # First layer         self.fc2 = nn.Linear(16, 8)   # Second layer         self.fc3 = nn.Linear(8, 1)    # Output layer      def forward(self, x):         x = torch.relu(self.fc1(x))         x = torch.relu(self.fc2(x))         x = torch.sigmoid(self.fc3(x))          return x  model = NeuralNetwork() print(model)

Output:

NeuralNetwork(
(fc1): Linear(in_features=10, out_features=16, bias=True)
(fc2): Linear(in_features=16, out_features=8, bias=True)
(fc3): Linear(in_features=8, out_features=1, bias=True)
)

Define Loss Function and Optimizer

Once we define our model, we need to specify:

A loss function to measure the error.
An optimizer to update the weights based on computed gradients.

We use nn.BCELoss() for binary cross-entropy loss and used optim.Adam() for Adam optimizer to combine the benefits of momentum and adaptive learning rates.

Python

model = NeuralNetwork() criterion = nn.BCELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

Train the Model

The training involves:

Generating dummy data (100 samples, each with 10 features).
Running a training loop where we:
- optimizer.zero_grad() clears the accumulated gradients from the previous step.
- Forward Pass (model(inputs)) passes inputs through the model to generate predictions.
- Loss Computation (criterion(outputs, targets)) computes the difference between predictions and actual labels.
- Backpropagation (loss.backward()) computes gradients for all weights.
- Optimizer Step (optimizer.step()) updates the weights based on the computed gradients.

Python

inputs = torch.randn((100, 10))  targets = torch.randint(0, 2, (100, 1)).float()   epochs = 20  for epoch in range(epochs):     optimizer.zero_grad()  # Reset gradients     outputs = model(inputs)  # Forward pass     loss = criterion(outputs, targets)  # Compute loss     loss.backward()  # Compute gradients     optimizer.step()  # Update weights      if (epoch+1) % 5 == 0:         print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}")

Output:

Epoch [5/20], Loss: 0.7014
Epoch [10/20], Loss: 0.6906
Epoch [15/20], Loss: 0.6744
Epoch [20/20], Loss: 0.6483

PyTorch vs TensorFlow

Feature	PyTorch	TensorFlow
Computational Graph	Dynamic	Static (TF 1.x), Dynamic (TF 2.0)
Ease of Use	Pythonic, easy to debug	Steeper learning curve
Performance	Fast with eager execution	Optimized for large-scale deployment
Deployment	TorchScript & ONNX	TensorFlow Serving & TensorFlow Lite
Popularity in Research	Widely used	Also widely used but more in production

Applications of PyTorch

Computer Vision: PyTorch is widely used in image classification, object detection, and segmentation using CNNs and Transformers (e.g., ViT).
Natural Language Processing (NLP): PyTorch supports transformers, recurrent neural networks (RNNs), and LSTMs for applications like text generation and sentiment analysis.
Reinforcement Learning: PyTorch is used in Deep Q-Networks (DQN), Policy Gradient Methods, and Actor-Critic Algorithms.
Healthcare and Bioinformatics: Used in medical image analysis, drug discovery, and protein structure prediction.

PyTorch is used in academia and industry for computer vision, NLP, and reinforcement learning applications. With its strong community support and easy-to-use API, PyTorch continues to be one of the leading deep learning frameworks.

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Article Tags :

What is PyTorch ?

Key Features of PyTorch

How to Install PyTorch?

PyTorch Tensors

Operations on Tensors

Reshaping and Transposing Tensors

Autograd and Computational Graphs

Building Neural Networks in PyTorch

Define Loss Function and Optimizer

Train the Model

PyTorch vs TensorFlow

Applications of PyTorch

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