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Backward pass and gradient computation in PyTorch

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by sneha@openweaver.com dot icon Updated: Mar 29, 2023

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PyTorch is an open-source machine learning library depending on the Torch library, used for applications such as computer vision and natural language processing. Facebook's artificial intelligence research group primarily develops it, and Uber's "Pyro" probabilistic programming language software is built on it. 



PyTorch provides two high-level features: 

  • Tensor computation with strong GPU acceleration 
  • Deep neural networks built on a tape-based autograd system 


Backward pass in PyTorch is the process of running the backward pass of a neural network. This involves calculating the gradients of the loss function concerning the network's parameters. This is done using the autograd package, which provides automatic differentiation for all operations performed on Tensors. 



Gradient computation in PyTorch is the process of calculating the gradient of a loss function concerning the parameters of a neural network. This is done by backpropagation, which uses the chain rule to compute the loss gradients concerning each network parameter. The gradients are then used to update the network weights during training. 



Here is an example of Backward pass and gradient computation in PyTorch 



Fig1: Preview of Output when the Code is run in IDE

Code


In this solution, we will use PyTorch to perform Backward pass and gradient computation


backward, grad function in pytorch

Lines of Code : 14dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import torch
from torch.autograd import Variable
x = Variable(torch.FloatTensor([[1,2],[3,4]]), requires_grad=True)
y = x + 2
z = y * y
gradient = torch.ones(2, 2)
z.backward(gradient)
print(x.grad)

Variable containing:
  6   8
 10  12
[torch.FloatTensor of size 2x2]

Instructions


  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install PyTorch - pip install pytorch
  4. Copy the snippet using the 'copy' button and paste it into that file.
  5. Run the file using run button.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for " Backward and grad function in PyTorch" in kandi. You can try any such use case!

Dependent Libraries


pytorch-tutorialby yunjey

Python doticonstar image 26754 doticonVersion:Currentdoticon
License: Permissive (MIT)

PyTorch Tutorial for Deep Learning Researchers

Support
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            pytorch-tutorialby yunjey

            Python doticon star image 26754 doticonVersion:Currentdoticon License: Permissive (MIT)

            PyTorch Tutorial for Deep Learning Researchers
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have PyTorch that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the PyTorch page in kandi.


                      You can search for any dependent library on kandi like PyTorch.

                      Environment Tested


                      I tested this solution in the following versions. Be mindful of changes when working with other versions.

                      1. The solution is created in Python3.9.6
                      2. The solution is tested on PyTorch 2.0.0 version.


                      Using this solution, we are able to perform Backward pass and gradient computation in PyTorch.


                      This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to perform Backward pass and gradient computation in PyTorch.

                      Support


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