Logistic Regression Example - Jax Typing CST Test

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Logistic Regression Example — Jax Code

A logistic regression implementation using JAX for binary classification.

import jax.numpy as jnp
from jax import grad

# Sample data
X = jnp.array([[0,0],[0,1],[1,0],[1,1]])
y = jnp.array([0,1,1,0])  # XOR example

# Initialize parameters
w = jnp.zeros(2)
b = 0.0

# Sigmoid function
def sigmoid(z):
    return 1 / (1 + jnp.exp(-z))

# Loss function
def loss(w, b):
    y_pred = sigmoid(jnp.dot(X, w) + b)
    return -jnp.mean(y * jnp.log(y_pred) + (1-y) * jnp.log(1-y_pred))

grad_loss = grad(loss, argnums=(0,1))

# Gradient descent
for _ in range(1000):
    dw, db = grad_loss(w, b)
    w -= 0.1 * dw
    b -= 0.1 * db

print('Learned weights:', w, 'bias:', b)

Jax Language Guide

JAX is an open-source Python library for high-performance numerical computing, combining NumPy-like API with automatic differentiation (autograd), GPU/TPU acceleration, and composable function transformations for machine learning and scientific computing.

Primary Use Cases

▸High-performance machine learning and deep learning model development
▸Gradient-based optimization and automatic differentiation
▸Physics simulations and scientific computing requiring differentiable functions
▸Research in reinforcement learning and generative models
▸GPU/TPU accelerated numerical computing at scale

Notable Features

▸NumPy-compatible API with hardware acceleration
▸Automatic differentiation with `grad`
▸Just-in-time compilation (`jit`) for optimized performance
▸Vectorization (`vmap`) and parallelization (`pmap`) of functions
▸Composable transformations for advanced research pipelines

Origin & Creator

JAX was developed by researchers at Google Research starting in 2018, building on Autograd and XLA (Accelerated Linear Algebra) to enable high-performance, differentiable programming.

Industrial Note

JAX is widely used in cutting-edge machine learning research, physics simulations, reinforcement learning, and differentiable programming, where composable gradients and hardware acceleration are essential.

More Jax Typing Exercises

JAX Simple Linear Regression Example JAX Neural Network Forward Pass Example JAX Mean Squared Error Example JAX Gradient Computation Example JAX Vectorized Operations Example JAX JIT Compilation Example JAX Neural Network Training Example JAX Softmax Classification Example JAX Convolution Example

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Logistic Regression Example — Jax Code

A logistic regression implementation using JAX for binary classification.

import jax.numpy as jnp from jax import grad # Sample data X = jnp.array([[0,0],[0,1],[1,0],[1,1]]) y = jnp.array([0,1,1,0]) # XOR example # Initialize parameters w = jnp.zeros(2) b = 0.0 # Sigmoid function def sigmoid(z): return 1 / (1 + jnp.exp(-z)) # Loss function def loss(w, b): y_pred = sigmoid(jnp.dot(X, w) + b) return -jnp.mean(y * jnp.log(y_pred) + (1-y) * jnp.log(1-y_pred)) grad_loss = grad(loss, argnums=(0,1)) # Gradient descent for _ in range(1000): dw, db = grad_loss(w, b) w -= 0.1 * dw b -= 0.1 * db print('Learned weights:', w, 'bias:', b)

Jax Language Guide

Primary Use Cases

▸High-performance machine learning and deep learning model development
▸Gradient-based optimization and automatic differentiation
▸Physics simulations and scientific computing requiring differentiable functions
▸Research in reinforcement learning and generative models
▸GPU/TPU accelerated numerical computing at scale

Notable Features

▸NumPy-compatible API with hardware acceleration
▸Automatic differentiation with `grad`
▸Just-in-time compilation (`jit`) for optimized performance
▸Vectorization (`vmap`) and parallelization (`pmap`) of functions
▸Composable transformations for advanced research pipelines

Origin & Creator

JAX was developed by researchers at Google Research starting in 2018, building on Autograd and XLA (Accelerated Linear Algebra) to enable high-performance, differentiable programming.

Industrial Note