Initial commit

Author: jt0dd

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

app-synaptic.js

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+import DataSet from "./classes/data-set.js";
+console.log("Predator activated...");
+new DataSet("letter-mini", 150, data => {
+  console.log("Data loaded", data);
+  const {Layer, Network} = window.synaptic;
+  let inputLayer = new Layer(625);
+  let hiddenLayer = new Layer(625);
+  let outputLayer = new Layer(1);
+  inputLayer.project(hiddenLayer);
+  hiddenLayer.project(outputLayer);
+  let myNetwork = new Network({
+    input: inputLayer,
+    hidden: [hiddenLayer],
+    output: outputLayer
+  });
+  var learningRate = 0.5;
+  for (var i = 0; i < 1000; i++) {
+    data.t.forEach(input => {
+      myNetwork.activate(input);
+      myNetwork.propagate(learningRate, [1]);
+    });
+    data.f.forEach(input => {
+      myNetwork.activate(input);
+      myNetwork.propagate(learningRate, [0]);
+    });
+  }
+  data.t.forEach(input => {
+    let result = myNetwork.activate(input);
+    console.log('Should output [1] ->[' + Math.round(result[0]) + ']');
+  });
+  data.f.forEach(input => {
+    let result = myNetwork.activate(input);
+    console.log('Should output [0] ->[' + Math.round(result[0]) + ']');
+  });
+});

app.js

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+import DataSet from "./classes/data-set.js";
+console.log("Predator activated...");
+new DataSet("letter-mini", 150, data => {
+  console.log("Data loaded", data);
+  const config = {
+    inputSize: 100,
+    //inputRange: 20,
+    //hiddenLayers: [20, 20],
+    outputSize: 1,
+    learningRate: 0.01,
+    decayRate: 0.999
+  };
+  const net = new brain.NeuralNetworkGPU(config);
+  let trainingSet = [];
+  for (var i = 0; i < 1000; i++) {
+    data.t.forEach(input => {
+      trainingSet.push({
+        input: input,
+        output: [1]
+      });
+    });
+    data.f.forEach(input => {
+      trainingSet.push({
+        input: input,
+        output: [0]
+      });
+    });
+  }
+  console.log('Built training set', trainingSet);
+  net.train(trainingSet);
+  console.log("[1, 0] -> [1] ->", );
+  console.log("[0, 1] -> [1] ->", Math.round(net.run([0, 1])));
+  console.log("[0, 0] -> [0] ->", Math.round(net.run([0, 0])));
+  console.log("[1, 1] -> [0] ->", Math.round(net.run([1, 1])));
+  data.t.forEach(input => {
+    let result = Math.round(net.run(input));
+    console.log('Should output [1] ->[' + result[0] + ']');
+  });
+  data.f.forEach(input => {
+    let result = Math.round(net.run(input));
+    console.log('Should output [0] ->[' + result[0] + ']');
+  });
+});

classes/data-set.js

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+function simplify(data, threshold) {
+  let result = [];
+  data.forEach(val => {
+    if (val > threshold) {
+      result.push(1);
+    } else {
+      result.push(0);
+    }
+  });
+  return result;
+}
+
+function load(type, setPath, i = 0, threshold, callback) {
+  let storage;
+  if (type == "true") {
+    storage = this.t;
+  } else if (type == "false") {
+    storage = this.f;
+  }
+  let asset = true;
+  let assetPath = "./sets/" + setPath + "/" + type + "/" + i + ".png";
+  console.log("Loading", assetPath);
+  asset = loadImage(
+    assetPath,
+    img => {
+      if (img.type != "error") {
+        let ctx = img.getContext("2d");
+        let data = ctx.getImageData(0, 0, img.width, img.height).data;
+        let simplifiedData = simplify(data, threshold);
+        storage.push(simplifiedData);
+        i++;
+        load.apply(this, [type, setPath, i, threshold, callback]);
+      } else {
+        if (callback) callback();
+      }
+    },
+    {
+      canvas: true
+    }
+  );
+}
+
+class DataSet {
+  constructor(setPath, threshold, callback) {
+    this.t = [];
+    this.f = [];
+    let state = 0;
+    load.apply(this, [
+      "true",
+      setPath,
+      0,
+      threshold,
+      () => {
+        state++;
+        if (state == 2) {
+          if (callback) callback(this);
+        }
+      }
+    ]);
+    load.apply(this, [
+      "false",
+      setPath,
+      0,
+      threshold,
+      () => {
+        state++;
+        if (state == 2) {
+          if (callback) callback(this);
+        }
+      }
+    ]);
+  }
+}
+
+export default DataSet;

index.html

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+<!doctype html>
+<html>
+
+<head>
+  <script src="./libs/load-image-all.js"></script>
+  <script src="./libs/brain.js"></script>
+  <script src="./libs/gpu.js"></script>
+  <script type="module" src="app.js"></script>
+</head>
+
+<body></body>
+
+</html>