Convolution Example - Jax Typing CST Test
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Convolution Example — Jax Code
Performing 1D convolution using JAX for sequence data.
import jax.numpy as jnp
from jax import lax
# Input sequence
x = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0])
# Kernel
w = jnp.array([0.2, 0.5, 0.2])
# 1D convolution
conv = lax.conv_general_dilated(x[jnp.newaxis, :, jnp.newaxis],
w[jnp.newaxis, :, jnp.newaxis],
window_strides=(1,),
padding='VALID',
dimension_numbers=('NWC','WIO','NWC'))
print('Convolution output:', conv)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.