Validate M2G-Net code implementations against mathematical documentation.
npx skills add https://github.com/Cuki2910/M2G-NET --skill math-validator
Please help me install this Agent Skill. Skill: math-validator Source: https://github.com/Cuki2910/M2G-NET/tree/main/.agents/skills/math-validator Command: npx skills add https://github.com/Cuki2910/M2G-NET --skill math-validator
This Skill addresses the need for rigorous validation of M2G-Net code against its mathematical documentation, ensuring accuracy in model architecture, loss functions, and metrics.
Run the math validation process for the latest code changes by executing the command: python scripts/verify_math.py
High-intent search queries and answers about installing and using this skill.
To validate M2G-Net code, run `python scripts/verify_math.py` to automate checking tensor shapes and operations for consistency against documented formulas. This verifies model architecture, loss functions, and metrics.
Mathematical validation compares code implementations against specified formulas to check tensor shapes and operations for consistency. It ensures accuracy in model architecture, loss functions, and metrics within the M2G-Net framework.
You need math validation before submitting a code change that affects model math. Use it to verify that your implementation aligns with the documented formulas to ensure accuracy in model architecture, loss functions, and metrics.
Yes, you can manually spot-check modified formulas. The validation process provides guidelines for manual review of modified formulas, complementing the automated verification process executed by scripts.
No external dependencies are required for model verification. The math validation process utilizes internal scripts to automate the verification of tensor shapes and operations for consistency.
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