https://github.com/rsokl/noggin

A simple tool for logging and plotting measurements during machine learning experiments
https://github.com/rsokl/noggin

data-visualization livedata machine-learning matplotlib neural-network python real-time

Last synced: 11 months ago
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A simple tool for logging and plotting measurements during machine learning experiments

• Host: GitHub
• URL: https://github.com/rsokl/noggin
• Owner: rsokl
• License: mit
• Created: 2018-02-21T23:38:01.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2022-12-03T14:10:33.000Z (about 3 years ago)
• Last Synced: 2025-02-27T12:23:47.316Z (12 months ago)
• Topics: data-visualization, livedata, machine-learning, matplotlib, neural-network, python, real-time
• Language: Python
• Homepage: https://noggin.readthedocs.io/en/latest
• Size: 4.82 MB
• Stars: 38
• Watchers: 6
• Forks: 1
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# noggin

![Python version support](https://img.shields.io/badge/python-3.6%20‐%203.8-blue.svg)

[![PyPi version](https://img.shields.io/pypi/v/noggin.svg)](https://pypi.python.org/pypi/noggin)

[![Build Status](https://travis-ci.com/rsokl/noggin.svg?branch=master)](https://travis-ci.com/rsokl/noggin)

[![codecov](https://codecov.io/gh/rsokl/noggin/branch/master/graph/badge.svg)](https://codecov.io/gh/rsokl/noggin)

[![Tested with Hypothesis](https://img.shields.io/badge/hypothesis-tested-brightgreen.svg)](https://hypothesis.readthedocs.io/)

[![Documentation Status](https://readthedocs.org/projects/noggin/badge/?version=latest)](https://noggin.readthedocs.io/en/latest/?badge=latest)

Noggin is a simple Python tool for ‘live’ logging and plotting measurements during experiments. Although Noggin can be used in a general context, it is designed around the train/test and batch/epoch paradigm for training a machine learning model.

Noggin’s primary features are its abilities to:

- Log batch-level and epoch-level measurements by name

- Seamlessly update a ‘live’ plot of your measurements, embedded within a Jupyter notebook

- Organize your measurements into a data set of arrays with labeled axes, via xarray

- Save and load your measurements & live-plot session: resume your experiment later without a hitch

You can read more about Noggin [here](https://noggin.readthedocs.io/en/latest)

![noggin](https://user-images.githubusercontent.com/29104956/52166468-bf425700-26db-11e9-9324-1fc83d4bc71d.gif)