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Introduction to Linear Regression - ( Linear Regression with PyTorch )

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Syntax

Linear Regression with PyTorch

import torch

x = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32)
y = torch.tensor([[2], [4], [6], [8]], dtype=torch.float32)

w = torch.tensor([[0.0]], dtype=torch.float32, requires_grad=True)
b = torch.tensor([[0.0]], dtype=torch.float32, requires_grad=True)

learning_rate = 0.01
epochs = 100

for epoch in range(epochs):
    y_pred = torch.matmul(x, w) + b

    loss = torch.mean((y_pred - y) ** 2)

    loss.backward()

    with torch.no_grad():
        w -= learning_rate * w.grad
        b -= learning_rate * b.grad

        w.grad.zero_()
        b.grad.zero_()

print(f'w: {w}')
print(f'b: {b}')

Example

Linear Regression with PyTorch

import torch

x = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32)
y = torch.tensor([[2], [4], [6], [8]], dtype=torch.float32)

w = torch.tensor([[0.0]], dtype=torch.float32, requires_grad=True)
b = torch.tensor([[0.0]], dtype=torch.float32, requires_grad=True)

learning_rate = 0.01
epochs = 100

for epoch in range(epochs):
    y_pred = torch.matmul(x, w) + b

    loss = torch.mean((y_pred - y) ** 2)

    loss.backward()

    with torch.no_grad():
        w -= learning_rate * w.grad
        b -= learning_rate * b.grad

        w.grad.zero_()
        b.grad.zero_()

print(f'w: {w}')
print(f'b: {b}')

test = torch.tensor([[5]], dtype=torch.float32)
result = torch.matmul(test, w) + b
print(f"Prediction for test data: {result}")

Output

Linear Regression with PyTorch

w: tensor([[2.0002]], requires_grad=True)
b: tensor([[-0.0005]], requires_grad=True)

Prediction for test data: tensor([[9.9996]], grad_fn=<AddBackward0>)

Explanation

Linear Regression is a technique used in Machine Learning to predict a target variable based on one or more predictor variables. It involves finding the best-fit line that predicts the target variable based on the given input variables. The best-fit line is determined by minimizing the distance between the predicted and actual values of the target variable.

PyTorch is a popular open-source machine learning library that provides efficient handling of tensor data structures and automatic gradient computation for training models. We can use PyTorch to implement linear regression by defining the model parameters and optimizing them using gradient descent.

Use

Linear Regression is a widely used technique in Machine Learning for predicting continuous variables. It can be used for various applications such as predicting housing prices, stock prices, and more. PyTorch is a very useful tool for implementing linear regression as it provides many built-in functions for handling tensor data structures and optimizing model parameters using gradient descent.

Important Points

  • Linear Regression is a technique used in Machine Learning for predicting a target variable based on one or more predictor variables
  • PyTorch is a popular open-source machine learning library
  • PyTorch provides efficient handling of tensor data structures and automatic gradient computation for training models
  • PyTorch can be used for implementing linear regression by defining model parameters and optimizing them using gradient descent

Summary

In conclusion, Linear Regression is a very useful technique in Machine Learning for predicting continuous variables. PyTorch is a powerful library that can be used for implementing linear regression as it provides efficient handling of tensor data structures and automatic gradient computation for training models. By defining model parameters and optimizing them using gradient descent, we can make accurate predictions and achieve good results in linear regression problems.

Gradient with PyTorchPrediction & Linear Class
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