# ShortcutML | Shortcuts Library

> Machine learning library for shortcuts.

## What this shortcut does

Features:
Image Classifier
Multi-Layer Perceptron
Hopfield Network
Planned:
Discreet Sequence Recall
Genetic Algorithm
KNN
etc
Image Classifier
No training required but does require a manual tap interaction step to use.
Example Use
Classification
Classification is done by passing a dictionary containing a base64 encoded PNG Image.
{
 "Architecture": "Image Classifier",
 "Image": "iVBORw0K..."
}
Multi-Layer Perceptron
Training
Training the Perceptron is done by passing a dictionary describing the inputs, hidden layer, and outputs.
Training data is a \n separated list of the form [inputs,...]=>[output]
{
 "Name": "XOR",
 "Architecture": "Perceptron",
 "Inputs": 2,
 "Hidden": [2],
 "Outputs": 1,
 "Training Data": "[0,1]=>[1]\n[1,0]=>[1]\n[1,1]=>[0]\n[0,0]=>[0]",
 "Error": 0.0001,
 "Iterations": 50000
}
Inference
Inference is done by passing a dictionary with Inputs provided as [inputs,...]
{
 "Name":"XOR",
 "Architecture":"Perceptron",
 "Inputs":"[0,0]"
}
Hopfield Network
Training
Training the Hopfield network is done by passing a dictionary describing the inputs binary sequences and bit-length of the network.
{
 "Name":"10-Bit Example",
 "Architecture":"Hopfield",
 "Bit Length":10,
 "Training Data":[
 "[0, 1, 0, 1, 0, 1, 0, 1, 0, 1]",
 "[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]"
]}
Feeding
Feeding the hopfield is done by passing a dictionary with Inputs provided as [input,...]
{
 "Name":"10-Bit Example",
 "Architecture":"Hopfield",
 "Inputs":"[0,1,0,1,0,1,0,1,1,1]"
}
Dependency Badge
[![This shortcut depends on ShortcutML](http://magaimg.net/img/8kdh.png)](
Credits
Perceptron & Hopfield Based on Synaptic
Image Classifier Based on ml5.js
Supports

## Compatibility

iPhone, iPad

## Required apps



[View this page](https://shortcutlibrary.app/shortcuts/shortcutml)

[Download Shortcuts Library on the App Store](https://apps.apple.com/app/id6759494377)