Start replacing Tensorflow with Brain.js, but it's not finished yet

Note to self: bug them to upgrade ot GPU.js 2 for future projects
This commit is contained in:
Starbeamrainbowlabs 2019-07-29 14:36:52 +01:00
parent b2efeae563
commit 2a81ac792e
4 changed files with 44 additions and 186 deletions

190
package-lock.json generated
View file

@ -50,63 +50,6 @@
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@ -24,6 +24,7 @@
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@ -59,13 +59,12 @@ devices = [
rssi_min = -150
rssi_max = 0
# Data is streamed from the SQLite databse. The batch size specifies how many
# rows to gather for training at once.
# Note that the entire available dataset will eventually end up being used -
# this setting just controls how much of ti is in memory at once.
batch_size = 32
# The architecture of the neural network, as an arary of integers.
# Each integer represents the number of nodes in a layer of the neural network.
network_arch = [ 64 ]
# The number of epochs to train for.
epochs = 5
epochs = 1000
# The percentage, between 0 and 1, of data that should be set aside for validation.
validation_split = 0.1

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@ -3,7 +3,7 @@
import path from 'path';
import fs from 'fs';
import tf from '@tensorflow/tfjs-node-gpu';
import brain from 'brain.js';
class AITrainer {
constructor({ ansi, settings, log, root_dir, GatewayRepo, DatasetFetcher }) {
@ -15,28 +15,14 @@ class AITrainer {
this.repo_gateway = GatewayRepo;
}
generate_model() {
let model = tf.sequential();
model.add(tf.layers.dense({
units: 64, // 64 nodes
activation: "sigmoid", // Sigmoid activation function
inputShape: [3], // 3 inputs - lat, long, and distance from gateway
}))
model.add(tf.layers.dense({
units: 1, // 1 output value - RSSI
activation: "sigmoid" // The example code uses softmax, but this is generally best used for classification tasks
}));
model.compile({
optimizer: tf.train.adam(),
loss: tf.losses.absoluteDifference,
metrics: [ tf.metrics.meanSquaredError ]
generate_neural_net() {
let net = new brain.NeuralNetwork({
hiddenLayers: this.settings.ai.network_arch,
activation: "sigmoid"
});
this.l.log(`Model:`);
model.summary();
return model;
return net;
}
async train_all() {
@ -84,7 +70,7 @@ class AITrainer {
*/
async train_gateway(gateway_id, destination_filename) {
this.l.log(`${this.a.fgreen}${this.a.hicol}Training AI for gateway ${gateway_id}${this.a.reset}`);
let model = this.generate_model();
let model = this.generate_neural_net();
// let dataset_input = tf.data.generator(
// this.dataset_fetcher.fetch_input.bind(this.dataset_fetcher, gateway_id)