PyTorch Basics – Part Nineteen: Logistic Regression Implementation

In this tutorial, we will be implementing logistic regression using PyTorch. Logistic regression is a linear model used for binary classification tasks.

Steps to Implement Logistic Regression:

1. Import PyTorch libraries: import torch and torch.nn
2. Define the logistic regression model class:

	class LogisticRegression(nn.Module):
    	def __init__(self, input_size, num_classes):
        	super(LogisticRegression, self).__init__()
        	self.linear = nn.Linear(input_size, num_classes)
        	
    	def forward(self, x):
        	out = self.linear(x)
        	return out
	

3. Instantiate the model:

	model = LogisticRegression(input_size, num_classes)
	

4. Define the loss function and optimizer:

	criterion = nn.CrossEntropyLoss()
	optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
	

5. Train the model:

	for epoch in range(num_epochs):
    	# Forward pass
    	outputs = model(inputs)
    	loss = criterion(outputs, labels)
    	
    	# Backward pass and optimization
    	optimizer.zero_grad()
    	loss.backward()
    	optimizer.step()
	

6. Evaluate the model:

	with torch.no_grad():
    	outputs = model(test_inputs)
    	_, predicted = torch.max(outputs.data, 1)
    	accuracy = (predicted == test_labels).sum().item() / test_labels.size(0)
    	print('Accuracy: {}'.format(accuracy))
	

By following these steps, you can successfully implement logistic regression using PyTorch for binary classification tasks.