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pypto-case-norm-batchnorm

Demonstrate 3D batch normalization with Pypto on batched inputs.

6|1|Updated Apr 19, 2026
Search Tags:#example#broadcast#3d#pypto#reduction#batchnorm#norm
One-click install
npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-norm-batchnorm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pypto-case-norm-batchnorm
Source: https://github.com/xchang1121/AutoResearch-CC-hook/tree/main/skills/pypto/cases/pypto-case-norm-batchnorm
Command: npx skills add https://github.com/xchang1121/AutoResearch-CC-hook --skill pypto-case-norm-batchnorm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This 3D BatchNorm pattern demonstrates how to perform per-channel normalization on a batched 3D tensor using Pypto, illustrating dimensionality reduction and broadcast-enabled operations.

Core Features & Use Cases

  • Demonstrates 3D normalization across (B, C, spatial) with multi-axis reductions.
  • Shows how to use loop-based channel-wise processing and expand_clone broadcasting to restore shapes.
  • Use Case: a compact example for validating Pypto's norm, reduction, and broadcast capabilities in 3D workloads.

Quick Start

Run the Pypto 3D BatchNorm example to verify channel-wise normalization on a sample input.

Frequently Asked Questions about pypto-case-norm-batchnorm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement 3D batch normalization across batch, channel, and spatial dimensions?▼

Pypto implements 3D batch normalization using loop reductions to compute per-channel statistics, then applies expand_clone broadcasting to restore the original tensor shapes after dimensionality reduction.

How does multi-axis reduction work for per-channel normalization on a batched 3D tensor?▼

Multi-axis reduction collapses the batch and spatial dimensions of the 3D tensor to isolate per-channel statistics. The expand_clone primitive then broadcasts these statistics back so normalization can be applied across the original shape.

Can I use Pypto primitives for loop reductions and broadcasting to validate 3D workloads?▼

Yes, Pypto primitives like loop reductions, sum, and expand_clone are used to validate norm, reduction, and broadcast capabilities. This Skill provides a compact example for verifying these operations on 3D batched workloads.

What is the best way to restore tensor shapes after computing channel-wise statistics in 3D normalization?▼

The best way to restore tensor shapes is using the expand_clone broadcasting primitive. It takes the reduced per-channel statistics and expands them back to match the original batched 3D tensor dimensions for normalization.

Do I need external dependencies to run the Pypto 3D BatchNorm example?▼

No external dependencies are required. The Skill operates using Pypto's built-in primitives for loop reductions, sum, and expand_clone broadcasting to execute the 3D batch normalization on a sample input.

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