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Serialise trained AIs and save them to disk

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This commit is contained in:
Starbeamrainbowlabs 2019-07-22 11:53:30 +01:00
parent dd9d39ba52
commit 92574bc98c
2 changed files with 30 additions and 3 deletions
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3
server/settings.default.toml
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@ -67,6 +67,9 @@ batch_size = 32
# The number of epochs to train for.
epochs = 5
# The directory to output trained AIs to, relative to the repository root.
output_directory = "app/ais/"
[logging]
# The format the date displayed when logging things should take.
# Allowed values: relative (e.g like when a Linux machine boots), absolute (e.g. like Nginx server logs), none (omits it entirely))

30
server/train-ai/AITrainer.mjs
View file

@ -1,14 +1,19 @@
"use strict";
import path from 'path';
import fs from 'fs';
import tf from '@tensorflow/tfjs-node-gpu';
class AITrainer {
constructor({ settings, log, GatewayRepo, DatasetFetcher }) {
constructor({ settings, log, root_dir, GatewayRepo, DatasetFetcher }) {
this.settings = settings;
this.root_dir = root_dir;
this.l = log;
this.dataset_fetcher = DatasetFetcher;
this.repo_gateway = GatewayRepo;
this.model = this.generate_model();
}
generate_model() {
@ -36,12 +41,29 @@ class AITrainer {
}
async train_all() {
for(let gateway of this.repo_gateway.iterate()) {
await this.train_gateway(gateway.id);
let filename = path.join(this.root_dir, "..", this.settings.ai.output_directory, `${gateway.id}`);
console.log(filename);
if(!fs.existsSync(path.dirname(filename)))
await fs.promises.mkdir(path.dirname(filename), { recursive: true });
await this.train_gateway(
gateway.id,
filename
);
}
}
async train_gateway(gateway_id) {
/**
* Trains an AI to predict the coverage of a specific gateway.
* @param {string} gateway_id The id of the gateway to train an AI for.
* @param {string} destination_filename The absolute path to the file to serialise the trained to. Required because we can't serialise and return a TensorFlow model, it has to be sent somewhere because the API is backwards and upside-down :-/
* @return {Promise} A promise that resolves when training and serialisation is complete.
*/
async train_gateway(gateway_id, destination_filename) {
// TODO: Add samples here for locations that the gateway does NOT cover too
let dataset_input = tf.data.generator(
this.dataset_fetcher.fetch_input.bind(this.dataset_fetcher, gateway_id)
);
@ -60,6 +82,8 @@ class AITrainer {
epochs: this.settings.ai.epochs,
batchSize: this.settings.ai.batch_size
});
await this.model.save(`file://${destination_filename}`);
console.log(result);
}
}