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What is PyTorch ? - GeeksforGeeks

Last Updated : 03 Jul, 2025

PyTorch is a deep learning library built on Python. 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.

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

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

!pip install torch torchvision torchaudio

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.

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:

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:

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:

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

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.

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.

import torch
import torch.nn as nn

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:

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.

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

The training involves:

1. Generating dummy data (100 samples, each with 10 features).

2. 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.

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:

Lets see a quick difference between pytorch and tensorflow:

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

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

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