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adding nature of code examples
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‎java/examples/Books/Nature of Code/chp10_nn/Exercise_10_5_LayeredNetworkAnimation/Connection.pde‎

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// An animated drawing of a Neural Network
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// Daniel Shiffman <http://www.shiffman.net>
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// Nature of Code
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class Connection {
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// Connection is from Neuron A to B
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Neuron a;
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Neuron b;
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// Connection has a weight
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float weight;
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// Variables to track the animation
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boolean sending = false;
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PVector sender;
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// Need to store the output for when its time to pass along
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float output = 0;
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Connection(Neuron from, Neuron to, float w) {
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weight = w;
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a = from;
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b = to;
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}
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// The Connection is active
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void feedforward(float val) {
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output = val*weight; // Compute output
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sender = a.location.get(); // Start animation at Neuron A
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sending = true; // Turn on sending
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}
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// Update traveling sender
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void update() {
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if (sending) {
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// Use a simple interpolation
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sender.x = lerp(sender.x, b.location.x, 0.1);
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sender.y = lerp(sender.y, b.location.y, 0.1);
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float d = PVector.dist(sender, b.location);
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// If we've reached the end
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if (d < 1) {
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// Pass along the output!
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b.feedforward(output);
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sending = false;
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}
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}
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}
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// Draw line and traveling circle
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void display() {
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stroke(0);
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strokeWeight(1+weight*4);
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line(a.location.x, a.location.y, b.location.x, b.location.y);
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if (sending) {
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fill(0);
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strokeWeight(1);
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ellipse(sender.x, sender.y, 16, 16);
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}
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/Exercise_10_5_LayeredNetworkAnimation/Ex_10_5_LayeredNetworkAnimation.pde‎

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// An animated drawing of a Neural Network
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// Daniel Shiffman <http://www.shiffman.net>
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// Nature of Code
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Network network;
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void setup() {
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size(750,200);
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smooth();
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// Create the Network object
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network = new Network(width/2, height/2);
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int layers = 3;
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int inputs = 2;
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Neuron output = new Neuron(250, 0);
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for (int i = 0; i < layers; i++) {
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for (int j = 0; j < inputs; j++) {
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float x = map(i, 0, layers, -300, 300);
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float y = map(j, 0, inputs-1, -75, 75);
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Neuron n = new Neuron(x, y);
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if (i > 0) {
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for (int k = 0; k < inputs; k++) {
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Neuron prev = network.neurons.get(network.neurons.size()-inputs+k-j);
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network.connect(prev, n, random(1));
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}
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}
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if (i == layers-1) {
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network.connect(n, output, random(1));
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}
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network.addNeuron(n);
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}
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}
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network.addNeuron(output);
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}
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void draw() {
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background(255);
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// Update and display the Network
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network.update();
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network.display();
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// Every 30 frames feed in an input
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if (frameCount % 30 == 0) {
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network.feedforward(random(1),random(1));
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/Exercise_10_5_LayeredNetworkAnimation/Network.pde‎

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// An animated drawing of a Neural Network
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// Daniel Shiffman <http://www.shiffman.net>
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// Nature of Code
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class Network {
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// The Network has a list of neurons
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ArrayList<Neuron> neurons;
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// The Network now keeps a duplicate list of all Connection objects.
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// This makes it easier to draw everything in this class
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ArrayList<Connection> connections;
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PVector location;
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Network(float x, float y) {
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location = new PVector(x, y);
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neurons = new ArrayList<Neuron>();
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connections = new ArrayList<Connection>();
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}
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// We can add a Neuron
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void addNeuron(Neuron n) {
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neurons.add(n);
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}
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// We can connection two Neurons
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void connect(Neuron a, Neuron b, float weight) {
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Connection c = new Connection(a, b, weight);
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a.addConnection(c);
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// Also add the Connection here
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connections.add(c);
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}
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// Sending an input to the first Neuron
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// We should do something better to track multiple inputs
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void feedforward(float input1, float input2) {
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Neuron n1 = neurons.get(0);
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n1.feedforward(input1);
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Neuron n2 = neurons.get(1);
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n2.feedforward(input2);
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}
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// Update the animation
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void update() {
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for (Connection c : connections) {
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c.update();
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}
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}
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// Draw everything
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void display() {
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pushMatrix();
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translate(location.x, location.y);
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for (Neuron n : neurons) {
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n.display();
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}
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for (Connection c : connections) {
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c.display();
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}
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popMatrix();
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/Exercise_10_5_LayeredNetworkAnimation/Neuron.pde‎

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// An animated drawing of a Neural Network
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// Daniel Shiffman <http://www.shiffman.net>
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// Nature of Code
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class Neuron {
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// Neuron has a location
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PVector location;
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// Neuron has a list of connections
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ArrayList<Connection> connections;
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// We now track the inputs and sum them
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float sum = 0;
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// The Neuron's size can be animated
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float r = 32;
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Neuron(float x, float y) {
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location = new PVector(x, y);
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connections = new ArrayList<Connection>();
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}
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// Add a Connection
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void addConnection(Connection c) {
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connections.add(c);
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}
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// Receive an input
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void feedforward(float input) {
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// Accumulate it
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sum += input;
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// Activate it?
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if (sum > 1) {
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fire();
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sum = 0; // Reset the sum to 0 if it fires
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}
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}
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// The Neuron fires
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void fire() {
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r = 64; // It suddenly is bigger
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// We send the output through all connections
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for (Connection c : connections) {
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c.feedforward(sum);
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}
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}
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// Draw it as a circle
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void display() {
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stroke(0);
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strokeWeight(1);
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// Brightness is mapped to sum
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float b = map(sum,0,1,255,0);
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fill(b);
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ellipse(location.x, location.y, r, r);
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// Size shrinks down back to original dimensions
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r = lerp(r,32,0.1);
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/Exercise_10_5_LayeredNetworkAnimation/sketch.properties‎

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mode=Standard

‎java/examples/Books/Nature of Code/chp10_nn/LayeredNetworkViz/Connection.pde‎

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class Connection {
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float weight;
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Neuron a;
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Neuron b;
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Connection(Neuron from, Neuron to,float w) {
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weight = w;
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a = from;
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b = to;
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}
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void display() {
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stroke(0);
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strokeWeight(weight*4);
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line(a.location.x, a.location.y, b.location.x, b.location.y);
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/LayeredNetworkViz/LayeredNetworkViz.pde‎

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Network network;
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void setup() {
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size(640, 360);
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smooth();
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network = new Network(4,3,1);
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}
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void draw() {
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background(255);
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network.display();
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}
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‎java/examples/Books/Nature of Code/chp10_nn/LayeredNetworkViz/Network.pde‎

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class Network {
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ArrayList<Neuron> neurons;
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PVector location;
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Network(int layers, int inputs, int outputs) {
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location = new PVector(width/2, height/2);
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neurons = new ArrayList<Neuron>();
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Neuron output = new Neuron(250, 0);
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for (int i = 0; i < layers; i++) {
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for (int j = 0; j < inputs; j++) {
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float x = map(i, 0, layers, -200, 200);
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float y = map(j, 0, inputs-1, -100, 100);
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println(j + " " + y);
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Neuron n = new Neuron(x, y);
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if (i > 0) {
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for (int k = 0; k < inputs; k++) {
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Neuron prev = neurons.get(neurons.size()-inputs+k-j);
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prev.connect(n);
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}
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}
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if (i == layers-1) {
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n.connect(output);
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}
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neurons.add(n);
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}
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}
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neurons.add(output);
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}
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void display() {
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pushMatrix();
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translate(location.x, location.y);
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for (Neuron n : neurons) {
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n.display();
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}
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popMatrix();
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/LayeredNetworkViz/Neuron.pde‎

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class Neuron {
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PVector location;
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ArrayList<Connection> connections;
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Neuron(float x, float y) {
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location = new PVector(x, y);
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connections = new ArrayList<Connection>();
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}
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void connect(Neuron n) {
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Connection c = new Connection(this, n, random(1));
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connections.add(c);
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}
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void display() {
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stroke(0);
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strokeWeight(1);
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fill(0);
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ellipse(location.x, location.y, 16, 16);
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for (Connection c : connections) {
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c.display();
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}
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}
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}
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‎java/examples/Books/Nature of Code/chp10_nn/LayeredNetworkViz/sketch.properties‎

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mode=Standard

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