index.js

"use strict";
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var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("@tensorflow/tfjs");
// Load the binding:
//require('@tensorflow/tfjs-node');  // Use '@tensorflow/tfjs-node-gpu' if running with GPU.
// utils
var tuple = function (a, b) { return [a, b]; };
// prepare the data, first is result, second is the raw text
var data = [
    [0, 'aaaaaaaaa'],
    [0, 'aaaa'],
    [1, 'bbbbbbbbb'],
    [1, 'bbbbbb']
];
// normalize the data
var arrayFill = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1];
var normalizeData = data.map(function (item) {
    return tuple(item[0], item[1].split('').map(function (c) { return c.charCodeAt(0); }).concat(arrayFill).slice(0, 10));
});
var xs = tf.tensor(normalizeData.map(function (i) { return i[1]; }));
var ys = tf.tensor(normalizeData.map(function (i) { return i[0]; }));
console.log(xs);
// Configs
var LEARNING_RATE = 1e-4;
// Train a simple model:
//const optimizer = tf.train.adam(LEARNING_RATE)
var model = tf.sequential();
model.add(tf.layers.embedding({ inputDim: 1000, outputDim: 16 }));
model.add(tf.layers.globalAveragePooling1d({}));
model.add(tf.layers.dense({ units: 16, activation: 'relu' }));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
model.summary();
model.compile({ optimizer: 'adam', loss: 'binaryCrossentropy', metrics: ['accuracy'] });
model.fit(xs, ys, {
    epochs: 10,
    validationData: [xs, ys],
    callbacks: {
        onEpochEnd: function (epoch, log) { return __awaiter(_this, void 0, void 0, function () {
            return __generator(this, function (_a) {
                console.log("Epoch " + epoch + ": loss = " + log.loss);
                return [2 /*return*/];
            });
        }); }
    }
});