A simple Auto Encoder typescript library for experimentation and dimensionality reduction. Supports automatic scaling.
npm install auto-encoder.tsA simple Auto Encoder typescript library for experimentation and dimensionality reduction. Supports automatic scaling.
This is a Typescript wrapper on top of autoencoder.
With additional helper functions: exportAutoEncoder(autoEncoder) and restoreAutoEncoder(json).
- Build embedding model in self-supervised manner (without manually labelling data)
- Reduce data dimension based on data distribution
- Support export/restore with JSON
- Lightweight (without node-gpy, cmake, python, cuda)
- Automatic scaling/normalizing (can be turned off)
- Static Type Checking and Completion with Typescript
- Isomorphic package: works in Node.js and browsers
- Works with plain Javascript, Typescript is not mandatory
``bash`
npm install auto-encoder.ts
You can also install auto-encoder.ts with pnpm, yarn, or slnpm
There are two ways to initialize a model:
- Provide the number of layers, input size, encoder output size (number of latent variables) and the activation function name
`typescript
import { createAutoEncoder } from 'auto-encoder.ts'
const model = createAutoEncoder({
nInputs: 10,
nHidden: 2,
nLayers: 2, // (default 2) - number of layers in each encoder/decoder
activation: 'relu', // (default 'relu') - applied to all, but the last layer
})
`
- Define each layer separately for both encoder and decoder
`typescript
import { createAutoEncoder } from 'auto-encoder.ts'
const model = createAutoEncoder({
encoder: [
{ nOut: 10, activation: 'tanh' },
{ nOut: 2, activation: 'tanh' },
],
decoder: [{ nOut: 2, activation: 'tanh' }, { nOut: 10 }],
scale: false, // (default true)
})
`
Activation functions: relu, tanh, sigmoid
#### Auto Scaling
Similar to other neural networks, auto-encoder is very sensitive to input scaling.
To make it easier the scaling is enabled by default.
you can control it with an extra parameter scale that can be true or false.
`typescript`
model.fit(X, {
batchSize: 100,
iterations: 5000,
method: 'adagrad', // (default 'adagrad')
stepSize: 0.01, // (default 0.05)
})
Optimization methods: sgd, adagrad, adam
`typescript
const Y = model.encode(X)
const Xd = model.decode(Y)
// Similar to model.decode(model.encode(X))
const Xp = model.predict(X)
`
Try the package in the browser on StatSim Vis. Choose a CSV file, change the _Projection method_ to Autoencoder, then click _Run_.
Below are the exported function and types:
`typescript
import { ActivationFunctionName, OptimizationMethodName } from 'adnn.ts'
function createAutoEncoder(options: AutoEncoderOptions): AutoEncoder
function exportAutoEncoder(autoEncoder: AutoEncoder): AutoEncoderJSON
function restoreAutoEncoder(json: AutoEncoderJSON): AutoEncoder
interface AutoEncoder {
fit(X: BatchValues, options?: FitOptions): void
encode(X: BatchValues): BatchValues
decode(X: BatchValues): BatchValues
/* @description Similar to this.decode(this.encode(X)) /
predict(X: BatchValues): BatchValues
}
type AutoEncoderJSON = {
scale: boolean | undefined
max: number[]
min: number[]
nInputs: number
nHidden: number
encoder: unknown
decoder: unknown
}
type FitOptions = {
/* @default round(totalSize/50) /
batchSize?: number
/* @default 100 /
iterations?: number
/* @default 'adagrad' /
method?: OptimizationMethodName
/* @default 0.05 /
stepSize?: number
}
type BatchValues = Values[]
type Values = number[]
type AutoEncoderOptions =
| {
/* @default true /
scale?: boolean
/* @description number of input features /
nInputs: number
/* @description number of embedding features /
nHidden: number
/**
* @description number of layers in each encoder/decoder
* @default 2
*/
nLayers?: number
/**
* @description applied to all, but the last layer
* @default 'relu'
*/
activation?: ActivationFunctionName
}
| {
/* @default true /
scale?: boolean
encoder: LayerOptions[]
decoder: LayerOptions[]
}
type LayerOptions = {
nOut: number
/* @description no activation function in the last layer of decoder gives better result /
activation?: ActivationFunctionName
}
``
This project is licensed with BSD-2-Clause
This is free, libre, and open-source software. It comes down to four essential freedoms [[ref]](https://seirdy.one/2021/01/27/whatsapp-and-the-domestication-of-users.html#fnref:2):
- The freedom to run the program as you wish, for any purpose
- The freedom to study how the program works, and change it so it does your computing as you wish
- The freedom to redistribute copies so you can help others
- The freedom to distribute copies of your modified versions to others