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[WIP] DIY NN - examples for loading model #210

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Oct 9, 2019
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[WIP] DIY NN - examples for loading model #210

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e28ca65
adds loadModel example
joeyklee Oct 9, 2019
957cda9
updates example showcasing issues
joeyklee Oct 9, 2019
36190cd
updated example to reflect current ml5-lib temp fix
joeyklee Oct 9, 2019
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21 changes: 21 additions & 0 deletions p5js/NeuralNetwork/NeuralNetwork_load_model/index.html
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<html>

<head>
<meta charset="UTF-8">
<title>Color Classifier - Neural Network</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/p5.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/addons/p5.dom.min.js"></script>
<script src="http://localhost:8080/ml5.js" type="text/javascript"></script>

<style>

</style>
</head>

<body>
<h1>Color Classifier - Neural Network</h1>
<script src="sketch.js"></script>
</body>

</html>
1 change: 1 addition & 0 deletions p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.json
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{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"sigmoid","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,3],"dtype":"float32"}},{"class_name":"Dense","config":{"units":9,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[3,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,9],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[9],"dtype":"float32"}]}]}
Binary file added p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.weights.bin
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1 change: 1 addition & 0 deletions p5js/NeuralNetwork/NeuralNetwork_load_model/model/model_meta.json
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{"data":{"inputMin":[0,0,0],"inputMax":[255,255,255],"outputMin":0,"outputMax":1},"meta":{"inputUnits":3,"outputUnits":9,"inputs":{"b":{"dtype":"number"},"g":{"dtype":"number"},"r":{"dtype":"number"}},"outputs":{"label":{"dtype":"string","uniqueValues":["green-ish","pink-ish","orange-ish","blue-ish","brown-ish","red-ish","yellow-ish","purple-ish","grey-ish"],"legend":{"green-ish":[1,0,0,0,0,0,0,0,0],"pink-ish":[0,1,0,0,0,0,0,0,0],"orange-ish":[0,0,1,0,0,0,0,0,0],"blue-ish":[0,0,0,1,0,0,0,0,0],"brown-ish":[0,0,0,0,1,0,0,0,0],"red-ish":[0,0,0,0,0,1,0,0,0],"yellow-ish":[0,0,0,0,0,0,1,0,0],"purple-ish":[0,0,0,0,0,0,0,1,0],"grey-ish":[0,0,0,0,0,0,0,0,1]}}},"isNormalized":true}}
56 changes: 56 additions & 0 deletions p5js/NeuralNetwork/NeuralNetwork_load_model/sketch.js
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let neuralNetwork;
let submitButton;

let rSlider, gSlider, bSlider;
let labelP;
let lossP;

function setup() {
// Crude interface
lossP = createP('loss');

createCanvas(100, 100);

labelP = createP('label');

rSlider = createSlider(0, 255, 255);
gSlider = createSlider(0, 255, 0);
bSlider = createSlider(0, 255, 255);

let nnOptions = {
inputs: ['r', 'g', 'b'],
outputs: ['label'],
task: 'classification',
debug: true
};
neuralNetwork = ml5.neuralNetwork(nnOptions);
neuralNetwork.load('model/model.json', modelReady);
}

function modelReady() {
console.log('model loaded!')
classify();
};


function classify() {
let inputs = {
r: rSlider.value(),
g: gSlider.value(),
b: bSlider.value()
}
neuralNetwork.classify(inputs, gotResults);
}

function gotResults(error, results) {
if (error) {
console.error(error);
} else {
labelP.html(`label:${results[0].label}, confidence: ${results[0].confidence.toFixed(2)}`);
classify();
}
}

function draw() {
background(rSlider.value(), gSlider.value(), bSlider.value());
}