How to Implement Logistic Regression with PyTorch
Understand Logistic Regression and sharpen your PyTorch skills
Published in
7 min readNov 15, 2020
To understand better what we’re going to do next, you can read my previous article about logistic regression:
So, what’s our plan for implementing Logistic Regression with PyTorch?
Let’s first think of the underlying math that we want to use.
There are many ways to define a loss function and then find the optimal parameters for it, among them, here we will implement in our LogisticRegression
class the following 3 ways for learning the parameters:
- We will rewrite the logistic regression equation so that we turn it into a least-squares linear regression problem with different labels and then, we use the closed-form formula to find the weights:
- Like above, we turn logistic into least-squares linear regression, but instead of the closed-form formula, we use stochastic gradient descent (SGD) to minimize the following loss function: