How to train a tfjs model on logic gates

Using tensorflow for JavaScript to train an artificial intelligence model to effectively evaluate and predict expected output of a logic gate given two inputs.

training data is sourced from a two dimentional array truth table
// logic truth table
// -----------------
const truthTable = [
    [
        [[[0, 0], [0, 1], [1, 1], [1, 1]],
        [[0], [1], [1], [1]]]
    ]
];

The first pairs of numbers are the input proceeded by the output

	import * as tf from '@tensorflow/tfjs'
	// logic truth table
	// -----------------
	const logic = [
	[
	[
	[
	[0, 0],
	[0, 1],
	[1, 1],
	[1, 1]
	],
	[
	[0],
	[1],
	[1],
	[1]
	]
	]
	]
	]
	// tabulate logic gate
	// ----------------------------------------
	// ┌──────────────────────┐
	// │ (index) | 0 | 1 | (values) |
	// ├──────────────────────┤
	// │ 0 | 0 | 0 | 0 |
	// │ 1 | 0 | 1 | 1 |
	// │ 2 | 1 | 0 | 1 |
	// │ 3 | 1 | 1 | 1 |
	// └──────────────────────┘
	// Artificial intelligence model
	// first layer has 2 inputs
	// hidden layer has 4 input and 1 output
	const model = tf.sequential()
	model.add(tf.layers.dense({
	inputShape: [2],
	activation: 'sigmoid',
	units: 4
	}))
	model.add(tf.layers.dense({
	inputShape: [4],
	activation: 'sigmoid',
	units: 1
	}))
	model.compile({
	loss: 'meanSquaredError',
	optimizer: tf.train.adam(0.1)
	})
	// it's a function used train a model
	function fit(input) {
	return new Promise((resolve) => {
	var train = model.fit(tf.tensor2d(input[0]), tf.tensor2d(input[1]), {
	epochs: 500
	})
	train.then(() => {
	// -----------
	console.table(input[0])
	console.table(input[1])
	console.table(input[0][1])
	// -------------
	return new Promise((resolve) => {
	const prediction = model.predict(tf.tensor2d([input[0][1]]))
	const predictionPromise = prediction.data()
	predictionPromise.then((output) => {
	console.table(Object.entries(output))
	})
	})
	})
	})
	}
	fit(logic[0][0])

view raw tfjs-logic.js hosted with ❤ by GitHub