PyTorch Basics | Part Seventeen | Linear Regression Implementation

In this article, we will discuss how to implement linear regression using PyTorch. Linear regression is a simple yet powerful method for modeling the relationship between a dependent variable and one or more independent variables.

Step 1: Import necessary libraries

First, we need to import the necessary libraries for implementing linear regression with PyTorch:


import torch
import torch.nn as nn
import torch.optim as optim


Step 2: Generate some random data

Next, we will generate some random data for our linear regression model. We will create a dataset with 100 samples, where the input variable X is a randomly generated tensor and the output variable y is computed as y = 3*X + 2 + noise.


X = torch.rand(100, 1)
noise = 0.1 * torch.rand(100, 1)
y = 3*X + 2 + noise


Step 3: Define the linear regression model

We will define a simple linear regression model with one input feature and one output feature:


class LinearRegression(nn.Module):
def __init__(self):
super(LinearRegression, self).__init__()
self.linear = nn.Linear(1, 1)

def forward(self, x):
return self.linear(x)


Step 4: Initialize the model and set hyperparameters

Now, we will initialize our linear regression model and set hyperparameters such as learning rate and number of epochs:


model = LinearRegression()
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
epochs = 1000


Step 5: Train the model

Finally, we will train our linear regression model on the random data that we generated:


for epoch in range(epochs):
optimizer.zero_grad()
outputs = model(X)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, loss.item()))


Conclusion

In this article, we have implemented a simple linear regression model using PyTorch. Linear regression is a fundamental machine learning technique that is commonly used for regression tasks. By following the steps outlined above, you can easily implement linear regression models in PyTorch for your own datasets.