Praxis

Models to assist in helping neural networks improve their abilities.

Why the name?

"Efficiency" is what is trying to be obtained, we could effectively call them "heuristic"s (probably the more technical name), but that'd be no fun to type. Too if we are targeting simplicity the very model, should not its name reflect that? with Here is a list of other projects and what they call their "heuristic" models:

Project Name	Praxis Synonym	Url
Caffe	Solvers	https://github.com/BVLC/caffe/tree/master/src/caffe/solvers
Tensor	Estimator/Optimizer	https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/estimator
torch	Optim	https://github.com/torch/optim
Synaptic	Trainer	https://github.com/cazala/synaptic/blob/master/src/Trainer.js
mlpack	Optimizer	https://github.com/mlpack/mlpack/tree/master/src/mlpack/core/optimizers
Shogun	Optimization	https://github.com/shogun-toolbox/shogun/tree/develop/src/shogun/optimization
Accord.net	Models	https://github.com/accord-net/framework/tree/master/Sources/Accord.Statistics/Models
Brain.js	Praxis

A praxis can be used on a layer as its means of learning like this:

import { Pool } from 'brain.js/layer';
import { MRmsProp } from 'brain.js/praxis';

new Pool({ praxis: (layer) => new MRmsProp(layer, { /* optional settings*/ }) });

For layer and praxis shorthand helpers you can do:

import { pool } from 'brain.js/layer';
import { mRmsProp } from 'brain.js/praxis';

pool({ praxis: mRmsProp });

A praxis can also be used with the FeedForward and planned Recurrent classes like this, which will cause all layers to inherit praxis:

import { input, pool, relu, output } from 'brain.js/layer';
import { mRmsProp } from 'brain.js/praxis';
new FeedForward({
  praxis: mRmsProp, // defines for all layers, their praxis
  input: () => input(),
  hiddenLayers: [
    (input) => pool({ praxis: mRmsProp }, input), // overrides network praxis
    (input) => relu(input)
  ],
  output: () => output()
})