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Updating the weights using an optimizer in PyTorch

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by vigneshchennai74 dot icon Updated: Mar 28, 2023

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Updating the weights using an optimizer in PyTorch is a crucial step in training a neural network, as it helps improve the model's accuracy. The code snippet provided defines a simple neural network using PyTorch and then uses the Adam optimizer to train the network on a randomly generated dataset. 


The SampleNet class defines a simple neural network that takes a tensor x as input, multiplies it by a learnable parameter theta, and returns the result. The theta parameter is initialized randomly using torch.rand() and is updated during training using the Adam optimizer. The train_data variable contains a randomly generated dataset with 1000 samples and ten features. The last 5 features are generated by multiplying the first five by 2 to create a simple linear relationship between the features. During training, the Adam optimizer is used to update the neural network weights to minimize the mean squared error (MSE) loss between the predicted outputs and the true outputs. The mse_loss variable defines the MSE loss function used to calculate the loss. 


The training loop runs for five epochs, and in each epoch, the optimizer is used to update the network parameters based on the gradients of the loss function. The output of the loss function is printed for each epoch, along with the learned value of theta at the end of training. 

This code demonstrates how PyTorch can define a simple neural network, train it using an optimizer, and evaluate its performance on a randomly generated dataset. 

Preview of the output that you will get on running this code from your IDE

Code

In this solution we have used torch is a Python package that provides a wide range of tools and functions for building and training neural networks.

Pytorch customize weight

Lines of Code : 45dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np
import torch


class SampleNet(torch.nn.Module):
    def __init__(self):
        super(SampleNet, self).__init__()

        self.theta = torch.nn.Parameter(torch.rand(1))

    def forward(self, x):
        x = x * self.theta.expand_as(x)  # expand_as() to match sizes
        return x


train_data = np.random.rand(1000, 10)
train_data[:, 5:] = 2 * train_data[:, :5]
train_data = torch.Tensor(train_data)

sample_net = SampleNet()

optimizer = torch.optim.Adam(params=sample_net.parameters())
mse_loss = torch.nn.MSELoss()

for epoch in range(5):
    for data in train_data:
        x = data[:5]
        y = data[5:]

        optimizer.zero_grad()
        prediction = sample_net(x)
        loss = mse_loss(y, prediction)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch}, Loss {loss.data.item()}")
print(f"Learned theta: {sample_net.theta.data.item()}")

Epoch 0, Loss 0.03369491919875145
Epoch 1, Loss 0.0018534092232584953
Epoch 2, Loss 1.2343853995844256e-05
Epoch 3, Loss 2.2044337466553543e-09
Epoch 4, Loss 4.0527581290916714e-12
Learned theta: 1.999994158744812

Instructions

To execute this code in VSCode, you can follow these steps:


  1. Install Python and the PyTorch library on your desktop.
  2. Open VSCode and create a new Python file in the editor.
  3. Copy the code snippet and paste it into your file in VSCode.
  4. Save the file with a meaningful name and the appropriate file extension (.py for Python).
  5. Open the VSCode terminal and navigate to the directory where your file is saved.
  6. Run the command "python filename.py" to execute the code and see the output in the terminal.



I hope you found this useful. I have added the version information in the following sections.


I found this code snippet by searching for in kandi Pytorch customize weight You can try any such use case!

Environment Tested

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

  • Visual Studio Code Version 1.76.0
  • Pytorch Version 1.13.1
  • Numpy Version 1.21.6


The use of an optimizer helps to automate the process of updating the weights of a neural network, which can be a tedious and error-prone task if done manually. 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 do Handle Updating the weights using an optimizer in PyTorch.

Dependent Library

pytorch-tutorialby yunjey

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PyTorch Tutorial for Deep Learning Researchers

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            pytorch-tutorialby yunjey

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

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                      numpyby numpy

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

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                                          You can search for any dependent library on kandi like 'pytorch' and 'numpy '

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