Start writing the AI training code, but it's not finished yet

server/package-lock.json

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server/package.json

@@ -20,6 +20,7 @@
 	"license": "MPL-2.0",
 	"dependencies": {
 		"@iarna/toml": "^2.2.3",
+		"@tensorflow/tfjs-node-gpu": "^1.2.3",
 		"aes-js": "^3.1.2",
 		"async-mqtt": "^2.3.0",
 		"awilix": "^4.2.2",

server/settings.default.toml

@@ -51,6 +51,8 @@ devices = [
 	"CHANGE_THIS"
 ]
 
+[ai]
+# Settings relating to the training of the AI. Note that a number of these settings can also be specified by environment variables, to aid with fiddling with the parameters to find the right settings.
 
 [logging]
 # The format the date displayed when logging things should take.

server/train-ai/AITrainer.mjs

@@ -0,0 +1,37 @@
+"use strict";
+
+import tf from '@tensorflow/tfjs-node-gpu';
+
+class AITrainer {
+	constructor({ settings }) {
+		this.settings = settings;
+		this.model = this.generate_model();
+	}
+	
+	generate_model() {
+		let model = tf.sequential();
+		model.add(tf.layers.dense({
+			units: 256, // 256 nodes
+			activation: "sigmoid", // Sigmoid activation function
+			inputShape: [3], // 2 inputs - lat and long
+		}))
+		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: "absoluteDifference",
+			metrics: [ "accuracy", "meanSquaredError" ]
+		});
+		
+		return model;
+	}
+	
+	train() {
+		
+	}
+}
+
+export default AITrainer;