Finish refactoring to use Brain.js, but it's untested.
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cce0761fed
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03c1cbb97f
3 changed files with 45 additions and 39 deletions
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@ -2,13 +2,11 @@
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import path from 'path';
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import {
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loadLayersModel as tf_loadLayersModel,
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tensor as tf_tensor,
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setBackend as tf_setBackend
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} from '@tensorflow/tfjs';
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import brain from 'brain.js';
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import haversine from 'haversine-distance';
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import { normalise } from '../../../common/Math.mjs';
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import { normalise, clamp } from '../../../common/Math.mjs';
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import GetFromUrl from '../Helpers/GetFromUrl.mjs';
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class AIWrapper {
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constructor() {
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@ -29,12 +27,16 @@ class AIWrapper {
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console.log("Loading models");
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// WebGL isn't available inside WebWorkers yet :-(
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tf_setBackend("cpu");
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for(let gateway of this.index.index) {
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let net = new brain.NeuralNetwork();
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net.fromJSON(
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await GetFromUrl(`${path.dirname(self.location.href)}/${path.dirname(this.Config.ai_index_file)}/${gateway.filename}`)
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);
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this.gateways.set(
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gateway.id,
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await tf_loadLayersModel(`${path.dirname(self.location.href)}/${path.dirname(this.Config.ai_index_file)}/${gateway.id}/model.json`)
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net
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);
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}
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console.log("Model setup complete.");
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@ -54,13 +56,29 @@ class AIWrapper {
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for(let lng = this.map_bounds.west; lng < this.map_bounds.east; lng += this.Config.step.lng) {
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let max_predicted_rssi = -Infinity;
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for(let [, ai] of this.gateways) {
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let next_prediction = ai.predict(
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tf_tensor([ lat, lng ], [1, 2])
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).arraySync()[0][0];
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for(let [gateway_id, ai] of this.gateways) {
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let distance_from_gateway = haversine(
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{ latitude: lat, longitude: lng },
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this.gateways.get(gateway_id)
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);
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max_predicted_rssi = Math.max(
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max_predicted_rssi,
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next_prediction
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ai.run({
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latitude: normalise(lat,
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{ min: -90, max: +90 },
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{ min: 0, max: 1 }
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),
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longitude: normalise(lng,
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{ min: -180, max: +180 },
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{ min: 0, max: 1 }
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),
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distance: clamp(
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normalise(distance_from_gateway,
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{ min: 0, max: 20000 },
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{ min: 0, max: 1 }
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),
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0, 1)
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})
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);
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}
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@ -28,19 +28,20 @@ class AITrainer {
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async train_all() {
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let index = [];
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for(let gateway of this.repo_gateway.iterate()) {
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let filename = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}`);
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console.log(filename);
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let filepath = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}.json`);
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console.log(filepath);
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if(!fs.existsSync(path.dirname(filename)))
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await fs.promises.mkdir(path.dirname(filename), { recursive: true });
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if(!fs.existsSync(path.dirname(filepath)))
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await fs.promises.mkdir(path.dirname(filepath), { recursive: true });
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if(!await this.train_gateway(gateway.id, filename)) {
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if(!await this.train_gateway(gateway.id, filepath)) {
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this.l.warn(`Warning: Failed to train AI for ${gateway.id}.`);
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continue;
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}
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index.push({
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id: gateway.id,
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filename: path.basename(filepath),
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latitude: gateway.latitude,
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longitude: gateway.longitude
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});
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@ -72,22 +73,9 @@ class AITrainer {
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this.l.log(`${this.a.fgreen}${this.a.hicol}Training AI for gateway ${gateway_id}${this.a.reset}`);
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let net = this.generate_neural_net();
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// let dataset_input = tf.data.generator(
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// this.dataset_fetcher.fetch_input.bind(this.dataset_fetcher, gateway_id)
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// );
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// let dataset_output = tf.data.generator(
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// this.dataset_fetcher.fetch_output.bind(this.dataset_fetcher, gateway_id)
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// );
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//
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// let dataset = tf.data.zip({
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// xs: dataset_input,
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// ys: dataset_output
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// }).shuffle(this.settings.ai.batch_size * 4)
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// .batch(this.settings.ai.batch_size);
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//
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let dataset = this.dataset_fetcher.fetch_all(gateway_id);
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let result = net.train(dataset, {
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await net.trainAsync(dataset, {
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iterations: this.settings.ai.epochs,
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errorThresh: this.settings.ai.error_threshold,
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@ -97,7 +85,7 @@ class AITrainer {
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timeout: Infinity
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});
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await model.save(`file://${destination_filename}`);
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await fs.promises.writeFile(destination_filename, net.toJSON());
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// console.log(result);
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return true;
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@ -13,16 +13,16 @@ class DatasetFetcher {
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}
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normalise_latlng(lat, lng) {
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return [
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normalise(lat,
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return {
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latitude: normalise(lat,
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{ min: -90, max: +90 },
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{ min: 0, max: 1 }
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),
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normalise(lng,
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longitude: normalise(lng,
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{ min: -180, max: +180 },
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{ min: 0, max: 1 }
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)
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];
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};
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}
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fetch_all(gateway_id) {
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@ -33,12 +33,12 @@ class DatasetFetcher {
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let next_input = this.normalise_latlng(rssi.latitude, rssi.longitude);
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let distance_from_gateway = haversine(gateway_location, rssi);
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next_input.push(clamp(
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next_input.distance = clamp(
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normalise(distance_from_gateway,
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{ min: 0, max: 20000 },
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{ min: 0, max: 1 }
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),
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0, 1))
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0, 1);
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result.input.push(next_input);
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