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:
parent
b2efeae563
commit
2a81ac792e
4 changed files with 44 additions and 186 deletions
190
package-lock.json
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190
package-lock.json
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@ -50,63 +50,6 @@
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@ -152,20 +95,8 @@
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}
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"version": "1.1.0",
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"resolved": "https://registry.npmjs.org/brorand/-/brorand-1.1.0.tgz",
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"event-emitter": "~0.3.5"
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"dependencies": {
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"lower-case": "^1.1.1"
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"node-fetch": {
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"resolved": "https://registry.npmjs.org/process-nextick-args/-/process-nextick-args-2.0.1.tgz",
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"integrity": "sha1-QzdOLiyglosO8VI0YLfXMP8i7rM=",
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"dev": true
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"async-mqtt": "^2.3.0",
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"better-sqlite3": "^5.4.0",
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# 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
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
Loading…
Reference in a new issue