ShortcutML

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 []( Credits Perceptron & Hopfield Based on Synaptic Image Classifier Based on ml5.js Supports