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                <p>View the output in the console.</p>
<p><button id="btn_train">Train the Model</button></p>
<p>Read a full explanation of this demo on <a href="" target="_blank"></a></p>
<img src="" alt="" width="250" height="250">




                // Solve for XOR
const LEARNING_RATE = 0.1;
const EPOCHS = 200;

// Define the training data
const xs = [[0,0],[0,1],[1,0],[1,1]];
const ys = [0,1,1,0];

// Instantiate the training tensors
let xTrain = tf.tensor2d(xs, [4,2]);
let yTrain = tf.oneHot(tf.tensor1d(ys).toInt(), 2);

// Define the model.
const model = tf.sequential();
// Set up the network layers
model.add(tf.layers.dense({units: 5, activation: 'sigmoid', inputShape: [2]}));
model.add(tf.layers.dense({units: 2, activation: 'softmax', outputShape: [2]}));
// Define the optimizer
const optimizer = tf.train.adam(LEARNING_RATE);
// Init the model
    optimizer: optimizer,
    loss: 'categoricalCrossentropy',
    metrics: ['accuracy'],

const button = document.querySelector('#btn_train');
button.addEventListener('mouseover', evt => {
  console.log('Training... This will take a moment.');

// Put the training/prediction into a function because it was slowing page load.
let TrainModel = function(){
  // Train the model
  const history =, yTrain, {
    epochs: EPOCHS,
    validationData: [xTrain, yTrain],
    // Try the model on a value
     const input = tf.tensor2d([0,1], [1, 2]);
     const predictOut = model.predict(input);
     const logits = Array.from(predictOut.dataSync());
     console.log('prediction', logits, predictOut.argMax(-1).dataSync()[0]);

console.log('Ready. Press the Train Button.');