PyTorch Tutorial: Logistic Regression

In this tutorial, we will learn how to implement logistic regression using PyTorch. Logistic regression is a popular method for binary classification tasks where we want to predict whether an instance belongs to one class or another.

Step 1: Importing PyTorch


import torch


Step 2: Loading the Data


# Load your dataset here


Step 3: Building the Model


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

def forward(self, x):
output = torch.sigmoid(self.linear(x))
return output


Step 4: Training the Model


# Define your loss function and optimizer
# Train the model


Step 5: Evaluating the Model


# Evaluate the model on test data


Conclusion

With this tutorial, you should now be able to implement logistic regression using PyTorch. It is a powerful tool for binary classification tasks and can be easily customized for more complex problems. Happy coding!