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Training and predicting an equation in chrome browser asynchronously using tensorflow.js
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Training a model on browser</title> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script> | |
| <script lang="js"> | |
| async function doTraining(model){ | |
| const history = | |
| await model.fit(xs, ys, | |
| { epochs: 500, | |
| callbacks:{ | |
| onEpochEnd: async(epoch, logs) =>{ | |
| console.log("Epoch:" | |
| + epoch | |
| + " Loss:" | |
| + logs.loss); | |
| } | |
| } | |
| }); | |
| } | |
| const model = tf.sequential(); | |
| model.add(tf.layers.dense({units: 1, inputShape: [1]})); | |
| model.compile({loss:'meanSquaredError', | |
| optimizer:'sgd'}); | |
| model.summary(); | |
| const xs = tf.tensor2d([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], [6, 1]); | |
| const ys = tf.tensor2d([-3.0, -1.0, 2.0, 3.0, 5.0, 7.0], [6, 1]); | |
| doTraining(model).then(() => { | |
| alert(model.predict(tf.tensor2d([10], [1,1]))); | |
| }); | |
| </script> | |
| </head> | |
| <body> | |
| <h1 align="center">Press 'f12' key or 'Ctrl' + 'Shift' + 'i' to check whats going on</h1> | |
| </body> | |
| </html> |
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