What Is PyTorch Backward Pass?

This Pytorch code example introduces you to the concept of PyTorch backward pass using a simple PyTorch example

Objective: What Is PyTorch Backward Pass?

This PyTorch code example will introduce you to PyTorch Backward Pass with the help of a simple PyTorch example.

What Is Backward Pass PyTorch?

The PyTorch backward pass, also known as backward propagation, is a process used to calculate the gradients of a neural network's parameters for the loss function. You can further use this information to update the parameters in the direction that reduces the loss. You can perform the backward pass by calling the .backward() method on the loss tensor. This recursively propagates the error signal back through the network, layer by layer, calculating the gradient of each parameter along the way.

PyTorch Vs. Tensorflow - Which One Should You Choose For Your Next Deep Learning Project?

Steps Showing How To Do A Backward Pass in PyTorch

The following steps will show you how to perform a PyTorch backward pass with the help of a simple PyTorch tensor example.

Step 1 - Import Library For PyTorch Backward Pass

First, you must import the required libraries.

import torch, torchvision

Step 2 - Build The Model And Define Parameters

As shown in the code below, from the torch vision module, you will load a pre-trained ResNet 18 model after that, you will create data, which is random tensor data for representing a single image with three channels and a height and width of 64. Then, you will initialize its corresponding labels to some random values.

torch_model = torchvision.models.resnet18(pretrained=True)

torch_data = torch.rand(1, 3, 64, 64)

data_labels = torch.rand(1, 1000

Step 3 - Generate PyTorch Forward Pass Predictions

The final step involves making predictions on the data, which is a forward pass.

predict = torch_model(torch_data)

Step 4 - Perform PyTorch Backward Pass

error = (predict - data_labels).sum()
error.backward()

Here, the above function is nothing but the backward pass. For calculating the error, you will use the prediction and data_labels. Then, you must backpropagate this error through the network. The backward propagation is kicked off when you call the ".backward()" on the error tensor.

Step 5 - Load The Optimizer

optimizer = torch.optim.SGD(torch_model.parameters(), lr=1e-2, momentum=0.9)

Step 6 - Initiate Gradient Descent

optimizer.step()

GRU Backward Pass PyTorch Implementation

The PyTorch GRU implementation with backward pass is based on the chain rule. The chain rule states that the derivative of a composite function is the product of the derivatives of the individual functions.

You can implement backward pass GRU PyTorch by calling the gru_backward_pass() function with the GRU layer, the hidden state's gradients, and the output's gradients as arguments. The function will return the gradients of the input.

The following example shows the GRU PyTorch implementation from scratch backward pass-

import torch

gru = GRU(10, 20, 2)

# Generate some input data

inputs = torch.randn(10, 10, 10)

# Calculate the output of the GRU layer

hidden = gru(inputs)

# Calculate the loss function

loss = torch.mean((hidden - inputs)**2)

# Perform the backward pass

input_grads = gru_backward_pass(gru, hidden_grads, loss.backward())

# Update the parameters of the GRU layer

optimizer.step(

How To Implement Squeezenet Backward Pass PyTorch?

You can implement a backward pass for Squeezenet in PyTorch using the following steps-

  • Calculate the gradients of the output layer for the loss function.

  • Propagate the gradients back through the network, layer by layer, using the chain rule.

  • Update the parameters of the network using the calculated gradients.

The below example shows how to implement Squeezenet backward pass in PyTorch-

import torch

from torchvision import models

# Load the SqueezeNet model

model = models.squeezenet1_1(pretrained=True)

# Generate some input data

x = torch.randn(10, 3, 224, 224)

# Calculate the output of the network

y = model(x)

# Calculate the loss function

loss = torch.mean((y - x)**2)

# Perform the backward pass

loss.backward()

# Update the parameters of the network

optimizer.step()

Dive Deeper Into The PyTorch Backward Pass With ProjectPro

This comprehensive PyTorch backward pass example helps you dive deeper into the essential concept of the PyTorch backward pass, which is crucial for training and optimizing neural network models. We have explored the steps involved in performing the backward pass, including gradient computation and backpropagation. You can further build solid expertise in PyTorch by working on enterprise-grade projects by ProjectPro. These end-to-end solved projects will help you understand the implementation of PyTorch and other tools in real-world scenarios to build effective data science and big data solutions.

 

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