Nick Z NMZivkovic

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TOPITOYPOYO

NMZivkovic / tf20-simple.py

Created April 18, 2019 08:46

NMZivkovic / predict-sr.js

Created March 31, 2019 17:13

	function calculateNewPosition(positionx, positiony, direction)
	{
	return {
	'up' : [positionx, positiony - 10],
	'down': [positionx, positiony + 10],
	'left' : [positionx - 10, positiony],
	'right' : [positionx + 10, positiony],
	'default': [positionx, positiony]
	}[direction];
	}

NMZivkovic / run-sr.js

Created March 31, 2019 17:06

	async function run() {
	recognizer = speechCommands.create('BROWSER_FFT', 'directional4w');
	await recognizer.ensureModelLoaded();

	var canvas = document.getElementById("canvas");
	var contex = canvas.getContext("2d");
	contex.lineWidth = 10;
	contex.lineJoin = 'round';

	var positionx = 400;

NMZivkovic / index.html

Created March 31, 2019 16:41

	<html>
	<head>

	<script src="https://unpkg.com/@tensorflow/tfjs@0.15.3/dist/tf.js"></script>
	<script src="https://unpkg.com/@tensorflow-models/speech-commands@0.3.0/dist/speech-commands.min.js"></script>
	</head>
	<body>
	<section class='title-area'>
	<h1>TensorFlow.js Speech Recognition</h1>
	<p class='subtitle'>Using pretrained models for speech recognition</p>

NMZivkovic / evaluateModel-tfjscnn.js

Created March 24, 2019 14:06

	function predict(model, data, testDataSize = 500) {
	const testData = data.nextDataBatch(testDataSize, true);
	const testxs = testData.xs.reshape([testDataSize, 28, 28, 1]);
	const labels = testData.labels.argMax([-1]);
	const preds = model.predict(testxs).argMax([-1]);

	testxs.dispose();
	return [preds, labels];
	}

NMZivkovic / trainModel-tfjscnn.js

Created March 24, 2019 13:57

	async function trainModelFunction(model, data, epochs) {
	const metrics = ['loss', 'val_loss', 'acc', 'val_acc'];
	const container = {
	name: 'Model Training', styles: { height: '1000px' }
	};
	const fitCallbacks = tfvis.show.fitCallbacks(container, metrics);

	const batchSize = 512;

	const [trainX, trainY] = getBatch(data, 5500);

NMZivkovic / createModel-tfjscnn.js

Created March 24, 2019 13:47

	function createModelFunction() {
	const cnn = tf.sequential();

	cnn.add(tf.layers.conv2d({
	inputShape: [28, 28, 1],
	kernelSize: 5,
	filters: 8,
	strides: 1,
	activation: 'relu',
	kernelInitializer: 'varianceScaling'

NMZivkovic / displayData-tfjscnn.js

Last active March 24, 2019 13:42

	async function singleImagePlot(image)
	{
	const canvas = document.createElement('canvas');
	canvas.width = 28;
	canvas.height = 28;
	canvas.style = 'margin: 4px;';
	await tf.browser.toPixels(image, canvas);
	return canvas;
	}

NMZivkovic / getData-tfjscnn.js

Created March 24, 2019 13:33

	async function getDataFunction() {
	var data = new MnistData();
	await data.load();
	return data;
	}

NMZivkovic / run-tfjscnn.js

Created March 24, 2019 13:30

	async function run() {
	const data = await getData();

	await displayDataFunction(data, 30);

	const model = createModel();
	tfvis.show.modelSummary({name: 'Model Architecture'}, model);

	await trainModel(model, data, 20);