From 0b4a2c0301d42713d957db5c4f37ffe18edfe1b4 Mon Sep 17 00:00:00 2001
From: JonasFovea <30690893+JonasFovea@users.noreply.github.com>
Date: Thu, 8 Feb 2018 21:43:57 +0000
Subject: [PATCH] Half Adder Example
++index.html
++sketch.js
---
examples/half_adder/index.html | 13 +++++++
examples/half_adder/sketch.js | 65 ++++++++++++++++++++++++++++++++++
2 files changed, 78 insertions(+)
create mode 100644 examples/half_adder/index.html
create mode 100644 examples/half_adder/sketch.js
diff --git a/examples/half_adder/index.html b/examples/half_adder/index.html
new file mode 100644
index 0000000..d4ee0b7
--- /dev/null
+++ b/examples/half_adder/index.html
@@ -0,0 +1,13 @@
+
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diff --git a/examples/half_adder/sketch.js b/examples/half_adder/sketch.js
new file mode 100644
index 0000000..8bc51be
--- /dev/null
+++ b/examples/half_adder/sketch.js
@@ -0,0 +1,65 @@
+let nn;
+let training_data = [{
+ inputs: [0, 0],
+ targets: [0,0]
+}, {
+ inputs: [1, 0],
+ targets: [0,1]
+}, {
+ inputs: [0, 1],
+ targets: [0,1]
+}, {
+ inputs: [1, 1],
+ targets: [1,0]
+}];
+
+let lr_slider;
+
+function setup() {
+ createElement('h1',"Half adder example");
+ createP("In this example the NN learns, how to act as half adder.")
+ createP("The half adder adds two single binary digits A and B. It has two outputs, sum (S) and carry (C). The carry signal represents an overflow into the next digit of a multi-digit addition.")
+
+ createDiv('');
+
+ createCanvas(400, 400);
+ nn = new NeuralNetwork(2, 4, 2);
+ lr_slider = createSlider(0.01, 0.1, 0.01, 0.01);
+
+ createDiv('');
+ createP("example made by JonasFovea")
+}
+
+function draw() {
+ background(0);
+
+ nn.learning_rate = lr_slider.value();
+
+ for (let i = 0; i < 100; i++) {
+ let data = random(training_data);
+ nn.train(data.inputs, data.targets);
+ }
+
+ let resolution = 10;
+ let cols = floor(width / resolution);
+ let rows = floor(height / resolution);
+
+ for (let i = 0; i < cols; i++) {
+ for (let j = 0; j < rows; j++) {
+ let x = i * resolution;
+ let y = j * resolution;
+ let input_1 = i / (cols - 1);
+ let input_2 = j / (rows - 1);
+ let output = nn.predict([input_1, input_2]);
+ let col1 = output[0] * 255;
+ let col2 = output[1] * 255;
+ let col3 = col1-col2;
+
+ fill(col1,col2,col3);
+ stroke(100,20,80);
+ noStroke();
+ rect(x, y, resolution, resolution);
+ }
+ }
+
+}