Neural Network Forward Pass Example - Jax Typing CST Test
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Neural Network Forward Pass Example — Jax Code
Forward pass of a simple 2-layer neural network in JAX.
import jax.numpy as jnp
# Input
x = jnp.array([1.0, 2.0, 3.0])
# Network parameters
W1 = jnp.array([[0.1,0.2,0.3],[0.4,0.5,0.6]])
b1 = jnp.array([0.1,0.2])
W2 = jnp.array([[0.7,0.8]])
b2 = jnp.array([0.3])
# Forward pass
def relu(x):
return jnp.maximum(0, x)
h = relu(jnp.dot(W1, x) + b1)
y_pred = jnp.dot(W2, h) + b2
print('Output:', y_pred)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.