sbrl/LoRaWAN-Signal-Mapping
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

Write the the data input, but it's untested.

Browse source 
There's also some confusion over the batch size. If it doesn't work, we 
probably need to  revise our understanding of the batch size in 
different contexts.
This commit is contained in:
Starbeamrainbowlabs 2019-07-18 16:34:25 +01:00
parent 6dbb6c3b87
commit 192e31a925
3 changed files with 52 additions and 27 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

8
server/settings.default.toml
View file

@ -59,6 +59,14 @@ devices = [
rssi_min = -150 rssi_min = -150
rssi_max = 0 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 number of epochs to train for.
epochs = 5
[logging] [logging]
# The format the date displayed when logging things should take. # 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)) # Allowed values: relative (e.g like when a Linux machine boots), absolute (e.g. like Nginx server logs), none (omits it entirely))

26
server/train-ai/AITrainer.mjs
View file

@ -31,15 +31,31 @@ class AITrainer {
return model; return model;
} }
async train() { async train_all() {
for(let gateway of this.repo_gateway.iterate()) { for(let gateway of this.repo_gateway.iterate()) {
let dataset = this.dataset_fetcher.fetch(gateway.id); await this.train_dataset(gateway.id);
await this.train_dataset(dataset);
} }
} }
async train_dataset(dataset) { async train_gateway(gateway_id) {
// TODO: Fill this in let dataset_input = tf.data.generator(
this.dataset_fetcher.fetch_input.bind(null, gateway_id)
);
let dataset_output = tf.data.generator(
this.dataset_fetcher.fetch_output.bind(null, gateway_id)
);
let dataset = tf.data.zip({
xs: dataset_input,
ys: dataset_output
}).shuffle(this.settings.ai.batch_size)
.batch(this.settings.ai.batch_size);
let result = await this.model.fitDataset(dataset, {
epochs: this.settings.ai.epochs,
batchSize: this.settings.ai.batch_size
});
console.log(result);
} }
} }

45
server/train-ai/DatasetFetcher.mjs
View file

@ -8,29 +8,30 @@ class DatasetFetcher {
this.repo_rssi = RSSIRepo; this.repo_rssi = RSSIRepo;
} }
fetch(gateway_id) { *fetch_input(gateway_id) {
let result = []; for(let rssi of this.repo_rssi.iterate_gateway(gateway_id)) {
for(let rssi of this.repo_rssi.iterate_gateway(gateway_id) { yield [
result.push({ normalise(rssi.latitude,
input: [ { min: -90, max: +90 },
normalise(rssi.latitude, { min: 0, max: 1 }
{ min: -90, max: +90 }, ),
{ min: 0, max: 1 } normalise(rssi.longitude,
), { min: -180, max: +180 },
normalise(rssi.longitude, { min: 0, max: 1 }
{ min: -180, max: +180 }, )
{ min: 0, max: 1 } ];
) }
], }
output: [
clamp(normalise(rssis.rssi, *fetch_output(gateway_id) {
{ min: this.settings.ai.rssi_min, max: this.settings.ai.rssi_max }, for(let rssi of this.repo_rssi.iterate_gateway(gateway_id)) {
{ min: 0, max: 1 } yield [
), 0, 1) clamp(normalise(rssi.rssi,
] { min: this.settings.ai.rssi_min, max: this.settings.ai.rssi_max },
}); { min: 0, max: 1 }
), 0, 1)
];
} }
return result;
} }
} }