Skip to main content
St Louis

Back to all posts

How to Perform Element-Wise Addition In PyTorch?

Published on
4 min read
How to Perform Element-Wise Addition In PyTorch? image

Best PyTorch Learning Resources to Buy in September 2025

1 Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

BUY & SAVE
$46.99 $54.99
Save 15%
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
2 Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD

Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD

BUY & SAVE
$43.99 $79.99
Save 45%
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
3 Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools

Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools

BUY & SAVE
$34.40 $49.99
Save 31%
Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
4 Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond

BUY & SAVE
$29.63 $51.99
Save 43%
Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond
5 PyTorch Pocket Reference: Building and Deploying Deep Learning Models

PyTorch Pocket Reference: Building and Deploying Deep Learning Models

BUY & SAVE
$16.69 $29.99
Save 44%
PyTorch Pocket Reference: Building and Deploying Deep Learning Models
6 Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications

Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications

BUY & SAVE
$41.24 $54.99
Save 25%
Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications
7 The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)

The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)

BUY & SAVE
$46.95
The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)
8 Build a Large Language Model (From Scratch)

Build a Large Language Model (From Scratch)

BUY & SAVE
$51.67 $59.99
Save 14%
Build a Large Language Model (From Scratch)
9 Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals

Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals

BUY & SAVE
$3.95
Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals
10 Learn Generative AI with PyTorch

Learn Generative AI with PyTorch

BUY & SAVE
$40.30 $59.99
Save 33%
Learn Generative AI with PyTorch
+
ONE MORE?

Performing element-wise addition in PyTorch involves adding corresponding elements of two tensors together. Here's how you can do it:

  1. Create two tensors: Start by creating two PyTorch tensors of the same shape, each containing the elements you want to add.
  2. Use the "+" operator: Simply use the "+" operator between the two tensors to perform element-wise addition. The resulting tensor will have the same shape as the input tensors, with each element being the sum of the corresponding elements in the input tensors.

Example:

import torch

Create two tensors

tensor1 = torch.tensor([1, 2, 3]) tensor2 = torch.tensor([4, 5, 6])

Perform element-wise addition

result = tensor1 + tensor2

Print the result

print(result)

Output:

tensor([5, 7, 9])

In the example above, the element-wise addition of tensor1 and tensor2 gives us a new tensor [5, 7, 9], where each element is the sum of the corresponding elements from the input tensors.

How to perform element-wise addition in PyTorch?

In PyTorch, element-wise addition between two tensors can be performed using the + operator or the torch.add() function. Here are the steps to perform element-wise addition in PyTorch:

  1. Import the required libraries:

import torch

  1. Create two tensors of the same shape with the elements you want to add:

a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6])

  1. Use either the + operator or the torch.add() function to perform element-wise addition: Using + operator:

c = a + b

Using torch.add() function:

c = torch.add(a, b)

  1. Print the result:

print(c)

The output will be:

tensor([5, 7, 9])

Note: The tensors a and b must have the same shape for element-wise addition to work. If they have different shapes, you need to reshape or broadcast them to the same shape before performing element-wise addition.

What is the criteria for tensors to be eligible for element-wise addition in PyTorch?

In PyTorch, the criteria for tensors to be eligible for element-wise addition are:

  1. The tensors must have the same shape, i.e., the same number of dimensions and the same size along each dimension.
  2. The tensors must have the same data type. PyTorch doesn't allow adding tensors of different data types directly.

If both these criteria are met, element-wise addition can be performed by simply using the "+" operator on the tensors. For example:

import torch

Creating two tensors of the same shape and data type

a = torch.tensor([1, 2, 3]) b = torch.tensor([4, 5, 6])

Element-wise addition

result = a + b

print(result)

This will output:

tensor([5, 7, 9])

How to handle broadcasting while performing element-wise addition in PyTorch?

To handle broadcasting while performing element-wise addition in PyTorch, you can use the broadcasting feature provided by PyTorch. Here's how you can do it:

  1. Make sure that the shapes of the tensors you want to add are compatible for broadcasting. The sizes of corresponding dimensions should either be equal, 1, or one of them should be empty.
  2. If the shapes are not already compatible, you can use the unsqueeze() or view() functions to reshape tensors by adding dimensions of size 1 as required.
  3. Once the shapes are compatible, simply perform the element-wise addition using the + operator.

Here's an example that demonstrates broadcasting while performing element-wise addition in PyTorch:

import torch

Create tensors

a = torch.tensor([[1, 2, 3], [4, 5, 6]]) # Shape: (2, 3) b = torch.tensor([10, 20, 30]) # Shape: (3,)

Perform element-wise addition with broadcasting

result = a + b

print(result)

In this example, the tensor a has shape (2, 3) and the tensor b has shape (3,). The shape of b is compatible for broadcasting with a, so PyTorch automatically broadcasts b along the first dimension to match the shape of a, resulting in a shape of (2, 3). The element-wise addition is then performed, resulting in the desired output:

tensor([[11, 22, 33], [14, 25, 36]])