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Nikola Živković NMZivkovic

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NMZivkovic / tf20-simple.py
Created April 18, 2019 08:46
import tensorflow as tf
const1 = tf.constant([[1,2,3], [1,2,3]]);
const2 = tf.constant([[3,4,5], [3,4,5]]);
result = tf.add(const1, const2);
print(result)
@NMZivkovic
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
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
NMZivkovic / index.html
Created March 31, 2019 16:41
<html>
<head>
<script src="https://unpkg.com/@tensorflow/[email protected]/dist/tf.js"></script>
<script src="https://unpkg.com/@tensorflow-models/[email protected]/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
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
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
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
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
NMZivkovic / getData-tfjscnn.js
Created March 24, 2019 13:33
async function getDataFunction() {
var data = new MnistData();
await data.load();
return data;
}
@NMZivkovic
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);