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[javascript][tensorflowjs] train XOR function with tensorflow.js
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| <!doctype html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script> | |
| <script src="script.js" defer="defer"></script> | |
| </head> | |
| <body></body> | |
| </html> |
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| // `tf`: root object of tensorflow.js | |
| // build keras model | |
| const model = tf.sequential(); // factory function for keras Sequential obj | |
| model.add(tf.layers.dense({units: 4, inputDim: 2, activation: "relu"})); | |
| model.add(tf.layers.dense({units: 2, activation: "softmax"})); | |
| // names for keras are turned as camelCase string | |
| model.compile({loss: "categoricalCrossentropy", optimizer: "sgd"}); | |
| // tensor data: similar as numpy array | |
| const inputs = [[0,0], [0, 1], [1, 0], [1, 1]]; | |
| const outputs = inputs.map(([a, b]) => a ^ b); | |
| const xs = tf.tensor(inputs); | |
| const ys = tf.oneHot(tf.tensor(outputs), 2); | |
| // train and check | |
| (async () => { | |
| const tx = tf.tensor([[1, 1], [0, 1], [0, 0], [1, 0]]); | |
| performance.mark(`start`); | |
| for (let i = 0; i < 100; i++) { | |
| performance.mark(`start-fit-${i}`); | |
| const r = await model.fit(xs, ys, {epochs: 100}); | |
| performance.mark(`end-fit-${i}`); | |
| performance.measure(`fit-${i}`, `start-fit-${i}`, `end-fit-${i}`); | |
| const ty = model.predict(tx); | |
| await display(i, tx, ty); | |
| await new Promise(r => setTimeout(r, 10)); | |
| } | |
| performance.mark(`end`); | |
| performance.measure(`total`, `start`, `end`); | |
| await displayModel(model); | |
| await displayWeights(model); | |
| await displayDuration(); | |
| })().catch(console.error); | |
| // outputs | |
| async function display(i, tx, ty) { | |
| const [xs, ys, vs] = await Promise.all( | |
| [tx.data(), ty.data(), ty.argMax(1).data()]); | |
| const ms = performance.getEntriesByName(`fit-${i}`)[0].duration; | |
| const ls = [...vs].map((v, i) => { | |
| const l = 2 * i, r = l + 1; | |
| return `${xs[l]} ^ ${xs[r]} = ${v} (0=${ys[l]}, 1=${ys[r]})\n`; | |
| }); | |
| const pre = document.createElement("pre"); | |
| pre.textContent = `[Train XOR: ${i + 1} (${ms}ms)]\n` + ls.join(""); | |
| document.body.insertBefore(pre, document.body.firstChild); | |
| } | |
| async function displayDuration() { | |
| const ms = performance.getEntriesByName(`total`)[0].duration; | |
| const pre = document.createElement("pre"); | |
| pre.textContent = `(total: ${ms / 1000}sec)`; | |
| document.body.insertBefore(pre, document.body.firstChild); | |
| } | |
| async function displayModel(model) { | |
| const pre = document.createElement("pre"); | |
| pre.textContent = JSON.stringify(JSON.parse(model.toJSON()), null, 2); | |
| document.body.insertBefore(pre, document.body.firstChild); | |
| } | |
| async function displayWeights(model) { | |
| const ls = model.weights.map(({name, shape, val}) => { | |
| return JSON.stringify({name, shape, values: [...val.dataSync()]}); | |
| }); | |
| const pre = document.createElement("pre"); | |
| pre.textContent = ls.join("\n"); | |
| document.body.insertBefore(pre, document.body.firstChild); | |
| } |
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NOTE:
fit()) when tab hiddentf.oneHot(), thenfit()fails to change wieghts