Logistic Regression using PyTorch

 

Logistic regression is a statistical model that is used to predict a binary dependent variable based on one or more independent variables. It is a type of generalized linear model that uses the logistic function as the activation function.

To perform logistic regression using PyTorch, you will need to install PyTorch and import it into your Python script. Then, you will need to define the independent and dependent variables as PyTorch tensors.

Next, you will define the logistic regression model using the nn.Linear module from PyTorch's nn library, followed by an activation function such as the nn.Sigmoid function. This combination of a linear transformation and a sigmoid activation function implements the logistic regression model.

Here is an example of logistic regression using PyTorch:

import torch
import torch.nn as nn

# Define the independent and dependent variables as tensors
x = torch.tensor(x_train, dtype=torch.float)
y = torch.tensor(y_train, dtype=torch.float)

# Define the logistic regression model
model = nn.Sequential(
  nn.Linear(in_features=1, out_features=1),
  nn.Sigmoid()
)

# Define the loss function and optimizer
loss_fn = nn.BCEWithLogitsLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Training loop
for i in range(100):
  # Forward pass: compute predicted y by passing x to the model
  y_pred = model(x)

  # Compute and print loss
  loss = loss_fn(y_pred, y)
  print(f'Step {i}, Loss: {loss.item()}')

  # Zero the gradients before running the backward pass
  optimizer.zero_grad()

  # Backward pass: compute gradient of the loss with respect to all the learnable parameters
  loss.backward()

  # Update the parameters using gradient descent
  optimizer.step()

# Make predictions on the test data
x_test = torch.tensor(x_test, dtype=torch.float)
y_pred = model(x_test).detach().numpy()

In this example, x and y are the independent and dependent variables, respectively. They are defined as PyTorch tensors. The logistic regression model is defined as a nn.Sequential object containing a nn.Linear module followed by a nn.Sigmoid activation function. The loss function is defined as the binary cross-entropy loss with logits, and the optimizer is defined as the SGD optimizer with a learning rate of 0.01. The training loop is run for 100 steps, and the model is evaluated on the test data by making predictions using the model object and the test data as the input.