diff --git a/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html
new file mode 100755
index 00000000..9264259b
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html
@@ -0,0 +1,19 @@
+
+
+
+
+ Neural Network
+
+
+
+
+
+
+
+ Neural Network Simple Classification
+ Load Model:
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/sketch.js
new file mode 100644
index 00000000..9096d96c
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/sketch.js
@@ -0,0 +1,56 @@
+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Image classification using MobileNet and p5.js
+This example uses a callback pattern to create the classifier
+=== */
+let nn;
+
+const options = {
+ inputs: 1,
+ outputs: 2,
+ task: 'classification',
+ debug: true
+}
+
+function setup(){
+ createCanvas(400, 400);
+ nn = ml5.neuralNetwork(options);
+
+
+ console.log(nn)
+ createTrainingData();
+ nn.normalizeData();
+
+ const trainingOptions={
+ batchSize: 24,
+ epochs: 32
+ }
+
+ nn.train(trainingOptions,finishedTraining); // if you want to change the training options
+ // nn.train(finishedTraining); // use the default training options
+}
+
+function finishedTraining(){
+
+ nn.classify([300], function(err, result){
+ console.log(result);
+ })
+
+}
+
+function createTrainingData(){
+ for(let i = 0; i < 400; i++){
+ if(i%2 === 0){
+ const x = random(0, width/2);
+ nn.addData( [x], ['left'])
+ } else {
+ const x = random(width/2, width);
+ nn.addData( [x], ['right'])
+ }
+ }
+}
diff --git a/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/index.html b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/index.html
new file mode 100755
index 00000000..8ec34acd
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/index.html
@@ -0,0 +1,19 @@
+
+
+
+
+ Neural Network Simple Regression
+
+
+
+
+
+
+
+ Neural Network Simple Regression
+ Load Model:
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js
new file mode 100644
index 00000000..47f82dd5
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js
@@ -0,0 +1,66 @@
+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Image classification using MobileNet and p5.js
+This example uses a callback pattern to create the classifier
+=== */
+let nn;
+
+const options = {
+ inputs: 1,
+ outputs: 1,
+ debug: true
+}
+
+function setup(){
+ createCanvas(400, 400);
+ nn = ml5.neuralNetwork(options);
+
+
+ console.log(nn)
+ createTrainingData();
+ nn.normalizeData();
+
+ const trainingOptions={
+ batchSize: 24,
+ epochs: 10
+ }
+
+ nn.train(trainingOptions,finishedTraining); // if you want to change the training options
+ // nn.train(finishedTraining); // use the default training options
+}
+
+async function finishedTraining(){
+
+ await Promise.all(
+ [...new Array(400).fill(null).map( async (item, idx) => {
+ let results = await nn.predict([idx]);
+ let prediction = results[0]
+ let x = idx
+ let y = prediction.value
+ fill(255, 0, 0);
+ rectMode(CENTER);
+ rect(x, y, 10, 10);
+ })]
+ )
+
+
+}
+
+function createTrainingData(){
+ for(let i = 0; i < width; i+=10){
+ const iters = floor(random(5, 20))
+ const spread = 50;
+ for(let j = 0; j < iters; j++){
+ let data = [i, height - i + floor(random(-spread, spread))]
+ fill(0, 0, 255);
+ ellipse(data[0], data[1], 10, 10)
+ nn.data.addData([data[0]], [data[1]])
+ }
+
+ }
+}
diff --git a/p5js/NeuralNetwork/NeuralNetwork_XOR/index.html b/p5js/NeuralNetwork/NeuralNetwork_XOR/index.html
new file mode 100644
index 00000000..04a768e8
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_XOR/index.html
@@ -0,0 +1,21 @@
+
+
+
+
+ XOR - Neural Network
+
+
+
+
+
+
+
+
+
+ XOR - Neural Network
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js
new file mode 100644
index 00000000..6c5d8cdd
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js
@@ -0,0 +1,92 @@
+// Porting XOR with tf.js to ml5!
+// https://youtu.be/N3ZnNa01BPM
+
+let model;
+let resolution = 20;
+let cols;
+let rows;
+
+function setup() {
+ createCanvas(400, 400);
+ cols = width / resolution;
+ rows = height / resolution;
+
+ let index = 0;
+ for (let i = 0; i < cols; i++) {
+ for (let j = 0; j < rows; j++) {
+ let br = 255; //y_values[index] * 255
+ fill(br);
+ rect(i * resolution, j * resolution, resolution, resolution);
+ fill(255 - br);
+ textSize(8);
+ textAlign(CENTER, CENTER);
+ text(nf(0.5, 1, 2), i * resolution + resolution / 2, j * resolution + resolution / 2)
+ //text(nf(y_values[index], 1, 2), i * resolution + resolution / 2, j * resolution + resolution / 2)
+ index++;
+ }
+ }
+
+ let options = {
+ inputs: 2,
+ outputs: 1,
+ learningRate: 0.25,
+ debug:true
+ // hiddenUnits: 2
+ }
+ model = ml5.neuralNetwork(options);
+
+ //model = ml5.neuralNetwork(2, 1);
+ model.addData([0, 0], [0]);
+ model.addData([1, 0], [1]);
+ model.addData([0, 1], [1]);
+ model.addData([1, 1], [0]);
+
+ model.normalizeData();
+ model.train({ epochs: 50 }, whileTraining, finishedTraining);
+
+}
+
+
+function whileTraining(epoch, logs) {
+ console.log(`Epoch: ${epoch} - loss: ${logs.loss.toFixed(2)}`);
+}
+
+function finishedTraining() {
+ // console.log('done!');
+ // TODO: Support prediction on multiple rows of input data
+ let xs = [];
+ for (let i = 0; i < cols; i++) {
+ for (let j = 0; j < rows; j++) {
+ let x1 = i / cols;
+ let x2 = j / rows;
+ xs.push([x1, x2]);
+ }
+ }
+ model.predictMultiple(xs, gotResults);
+ // model.predict([1, 0], gotResults);
+}
+
+function gotResults(error, results) {
+ background(0);
+ if(error){
+ console.log(err)
+ return
+ }
+ // console.log(results);
+ let index = 0;
+ for (let i = 0; i < cols; i++) {
+ for (let j = 0; j < rows; j++) {
+ let br = results[index][0].value * 255; //y_values[index] * 255
+ fill(br);
+ rect(i * resolution, j * resolution, resolution, resolution);
+ fill(255 - br);
+ textSize(8);
+ textAlign(CENTER, CENTER);
+ text(nf(0.5, 1, 2), i * resolution + resolution / 2, j * resolution + resolution / 2)
+ text(nf(results[index][0].value, 1, 2), i * resolution + resolution / 2, j * resolution + resolution / 2)
+ index++;
+ }
+ }
+
+ // finishedTraining()
+}
diff --git a/p5js/NeuralNetwork/NeuralNetwork_basics/index.html b/p5js/NeuralNetwork/NeuralNetwork_basics/index.html
new file mode 100755
index 00000000..013d604b
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_basics/index.html
@@ -0,0 +1,19 @@
+
+
+
+
+ Neural Network
+
+
+
+
+
+
+
+ Neural Network
+ Load Model:
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.json b/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.json
new file mode 100644
index 00000000..ffc30034
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.json
@@ -0,0 +1 @@
+{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":2,"activation":"tanh","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,3],"dtype":"float32"}},{"class_name":"Dense","config":{"units":2,"activation":"tanh","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.1.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[3,2],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[2],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[2,2],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[2],"dtype":"float32"}]}]}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.weights.bin b/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.weights.bin
new file mode 100644
index 00000000..53dd4d41
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_basics/model/model.weights.bin
@@ -0,0 +1 @@
+lٽx&?[>^vDRg=a=sa>yy?p>Ԗ?cf<f>
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js
new file mode 100644
index 00000000..bbf0e3af
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js
@@ -0,0 +1,132 @@
+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Image classification using MobileNet and p5.js
+This example uses a callback pattern to create the classifier
+=== */
+let nn;
+
+function setup() {
+ // Options for Neural Network
+ const options = {
+ inputs: 3,
+ outputs: 2,
+ task: 'regression',
+ debug:true
+ };
+ // Create Neural Network
+ nn = ml5.neuralNetwork(options);
+
+ trainModel();
+
+ // Train the model
+ let trainBtn = createButton('Train Model');
+ trainBtn.position(10, 50);
+ trainBtn.mousePressed(function () {
+ trainModel();
+ });
+
+ // Predict
+ let predictBtn = createButton('Predict');
+ predictBtn.position(10, 70);
+ predictBtn.mousePressed(function () {
+ predict();
+ });
+
+ // Save and download the model
+ let saveBtn = createButton('Save Model');
+ saveBtn.position(10, 90);
+ saveBtn.mousePressed(function () {
+ nn.save();
+ });
+
+ // Load the model from local files
+ let loadLocalBtn = createButton('Load the model from local files');
+ loadLocalBtn.position(10, 110);
+ loadLocalBtn.mousePressed(function () {
+ nn.load('model/model.json', function () {
+ console.log('Model Loaded!');
+ });
+ });
+
+ // Load model
+ let loadBtn = select('#load');
+ loadBtn.changed(function () {
+ nn.load(loadBtn.elt.files, function () {
+ console.log('Model Loaded!');
+ });
+ });
+}
+
+function trainModel() {
+ // Add training data
+ // const training_input = [-0.6, 1, 0.25];
+ // const training_target = [0.3, 0.9];
+ let a, b, c;
+ let training_target;
+ for (let i = 0; i < 500; i++) {
+ if (i % 2) {
+ a = Math.random(0, 0.16);
+ b = Math.random(0.16, 0.32);
+ c = Math.random(0.32, 0.5);
+ training_target = [0, 0]
+ } else {
+ a = Math.random(0.5, 0.66);
+ b = Math.random(0.66, 0.82);
+ c = Math.random(0.82, 1);
+ training_target = [1, 1]
+ }
+
+ const training_input = [a, b, c];
+ // const training_target = [0, 1];
+
+ // nn.data.addData(
+ // {
+ // input0: training_input[0],
+ // input1: training_input[1],
+ // input2: training_input[2],
+ // },
+ // {
+ // output0: training_target[0],
+ // output1: training_target[1],
+ // });
+
+ nn.addData(training_input, training_target)
+
+ }
+
+ const trainingOptions = {
+ epochs: 32,
+ batchSize: 12
+ }
+ // Train
+ nn.normalizeData();
+ nn.train(trainingOptions, finishedTraining);
+}
+
+// Training callback
+function finishedTraining() {
+ predict();
+}
+
+function predict() {
+ const a = 0.1;
+ const b = 0.2;
+ const c = 0.4;
+ const input = [a, b, c];
+ // we should expect [0,0]
+ nn.predict(input, gotResults);
+
+}
+
+function gotResults(error, results) {
+ if (error) console.log(error);
+ if (results) {
+ console.log(results);
+ results.tensor.print()
+ }
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.csv b/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.csv
new file mode 100644
index 00000000..23f58cd0
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.csv
@@ -0,0 +1,151 @@
+val,city_name,city_name_cdp,reporting_year_cdp,scope1_ghg_emissions_tons_co2e,year_of_emission,latitude_degrees,longitude_degrees,country,region,population_cdp,population_year_cdp
+1,Abington,Abington Township,2016,193345,2010,40.1,-75.099722,USA,North America,55310,2010
+2,Addis Ababa,Addis Ababa City Administration,2016,3708292,2012,9.03,38.74,Ethiopia,Africa,3384569,2008
+3,Adelaide,City of Adelaide,2016,63724,2013,-34.929,138.601,Australia,Oceania,23169,2015
+7,"Ajax, ON","Town of Ajax, ON",2016,6354,2008,43.858,-79.036389,Canada,North America,109600,2011
+8,Albany,City of Albany,2017,663997,2010,42.653,-73.757222,USA,North America,97856,2010
+9,"Alton, IL","City of Alton, IL",2016,138542,2013,38.901,-90.159722,USA,North America,26581,2015
+10,Amman,Greater Amman Municipality,2016,3322603,2014,31.95,35.932778,Jordan,"North Africa, Middle East, West Asia",3604459,2015
+11,Amsterdam,City of Amsterdam,2016,2161000,2016,52.367,4.9,Netherlands,Europe,822272,2015
+14,"Arlington, VA","City of Arlington, VA",2016,1257464,2015,38.881,-77.1372613,USA,North America,216700,2015
+16,Athens,City of Athens,2016,1260355,2014,37.984,23.727806,Greece,Europe,664046,2011
+17,Atlanta,City of Atlanta,2016,3548215,2014,33.755,-84.39,USA,North America,443775,2013
+18,Auckland,Auckland Council,2016,9133523,2013,-36.841,174.74,New Zealand,Oceania,1569900,2015
+19,Austin,City of Austin,2016,6439000,2013,30.267,-97.733333,USA,North America,888204,2015
+21,Baltimore,City of Baltimore,2017,4019044,2014,39.283,-76.616667,USA,North America,614664,2016
+28,Barreiro,Barreiro,2016,203441,2014,38.667,-9.066667,Portugal,Europe,78764,2011
+29,Basel,Basel-Stadt,2017,783932,2016,47.567,7.6,Switzerland,Europe,198206,2016
+30,Batangas,Batangas City,2016,344419,2010,13.83,121,Philippines,Southeast Asia,332458,2015
+32,Belo Horizonte,Municipality of Belo Horizonte,2016,2804787,2013,-19.917,-43.933333,Brazil,Latin America & Caribbean,2502557,2015
+38,Blacksburg,Town of Blacksburg,2017,124461.79,2015,37.23,-80.417778,USA,North America,44215,2015
+39,Bogor,Bogor City Government,2017,954020,2014,-6.597,106.7972,Indonesia,Southeast Asia,1047922,2015
+40,Bogotá,Bogotá Distrito Capital,2016,10679585.14,2015,4.711,-74.072222,Colombia,Latin America & Caribbean,7878783,2015
+43,Boston,City of Boston,2016,3603270,2014,42.358,-71.063611,USA,North America,646000,2013
+44,Boulder,City of Boulder,2016,741287,2012,40.027,-105.251945,USA,North America,104810,2015
+45,Bournemouth,City of Bournemouth,2016,350775,2013,50.72,-1.88,United Kingdom,Europe,191400,2015
+46,Brasília,City of Brasília,2016,7739830,2012,-15.79,-47.88,Brazil,Latin America & Caribbean,1409671,2015
+50,Buenos Aires,City of Buenos Aires,2016,15561157,2014,-34.603,-58.381667,Argentina,Latin America & Caribbean,3054267,2015
+52,Burlington,City of Burlington,2016,368077,2010,44.476,-73.211944,USA,North America,42284,2015
+53,Calgary,City of Calgary,2016,10448332,2015,51.05,-114.066667,Canada,North America,1203915,2015
+54,Canberra,Canberra,2016,1757500,2015,-35.307,149.124417,Australia,Oceania,400000,2016
+55,Cape Town,City of Cape Town,2016,9783734,2012,-33.925,18.423889,South Africa,Africa,3918830,2014
+56,Caracas,Alcaldía Metropolitana de Caracas,2016,14364103,2015,10.481,-66.903611,Venezuela,Latin America & Caribbean,3518590,2015
+58,Cascais,Cascais,2016,565382,2010,38.7,-9.416667,Portugal,Europe,208122,2014
+63,Chicago,City of Chicago,2017,16951471,2015,41.837,-87.684722,USA,North America,2720546,2015
+65,Cleveland,City of Cleveland,2016,7750563,2010,41.482,-81.669722,USA,North America,396815,2010
+68,Columbus,City of Columbus,2016,5487400,2015,39.983,-82.983333,USA,North America,835957,2014
+70,Curitiba,Municipality of Curitiba,2016,2686488,2013,-25.417,-49.25,Brazil,Latin America & Caribbean,1751907,2010
+72,Dallas,City of Dallas,2016,119881,2010,32.776,-96.796667,USA,North America,1257676,2013
+76,"Davis, CA","City of Davis, CA",2016,348437,2010,38.554,-121.738056,USA,North America,65600,2010
+78,Denver,City of Denver,2016,4557000,2014,39.762,-104.881105,USA,North America,663862,2014
+79,Detroit,City of Detroit,2016,5520240,2012,42.331,-83.045833,USA,North America,688701,2013
+80,District of Columbia,District of Columbia,2016,3132786,2013,38.905,-77.016389,USA,North America,672228,2015
+82,Durban,City of Durban,2016,11681810,2014,-29.883,31.05,South Africa,Africa,3555868,2015
+83,Durham,City of Durham,2016,1973070,2015,35.989,-78.907222,USA,North America,250815,2015
+84,Edmonton,City of Edmonton,2016,10215142,2014,53.533,-113.5,Canada,North America,878000,2014
+86,"Emeryville, CA","City of Emeryville, CA",2016,127138,2014,37.831,-122.285278,USA,North America,10570,2014
+87,Eugene,City of Eugene,2016,1106250,2015,44.052,-123.086667,USA,North America,151190,2013
+88,Faro,Faro,2016,128,2010,37.033,-7.916667,Portugal,Europe,61214,2014
+90,Flagstaff,City of Flagstaff,2016,621293.07,2014,35.199,-111.631111,USA,North America,68667,2013
+91,Florianópolis,Prefeitura de Florianópolis,2016,1305206,2013,-27.572,-48.6256,Brazil,Latin America & Caribbean,550000,2010
+98,Gladsaxe Kommune,Gladsaxe Kommune,2017,247599,2015,55.733,12.483333,Denmark,Europe,67347,2015
+101,Greater London,Greater London Authority,2016,20620000,2013,51.507,-0.1275,United Kingdom,Europe,8600000,2015
+110,"Hamilton, ON",City of Hamilton,2016,6304593,2012,43.25,-79.866667,Canada,North America,519949,2011
+114,Hayward,City of Hayward,2016,861854,2015,37.669,-122.080796,USA,North America,158985,2015
+119,Helsinki,City of Helsinki,2016,2096206,2014,60.171,24.9375,Finland,Europe,612664,2014
+121,"Hermosa Beach, CA","City of Hermosa Beach, CA",2016,123218,2012,33.866,-118.399722,USA,North America,19750,2015
+123,Hjørring,Municipality of Hjørring,2016,289000,2014,57.467,9.983333,Denmark,Europe,65308,2016
+124,Hoeje-Taastrup Kommune,Hoeje-Taastrup Kommune,2016,173000,2014,55.648,12.27,Denmark,Europe,50002,2016
+126,Hong Kong,Government of Hong Kong Special Administrative Region,2017,44876460,2014,22.3,114.2,China,East Asia,7336600,2016
+127,Houston,City of Houston,2016,16075539,2014,29.763,-95.383056,USA,North America,2195914,2013
+133,Incheon,Incheon Metropolitan City,2016,57719912,2015,37.483,126.633333,South Korea,East Asia,2983484,2015
+134,Indianapolis,City of Indianapolis,2017,726155.11,2013,39.791,-86.148,USA,North America,853000,2015
+136,Iowa,City of Iowa City,2016,590282,2013,41.66,-91.5342,USA,North America,73415,2015
+149,Johannesburg,City of Johannesburg,2016,9068358,2014,-26.204,28.045556,South Africa,Africa,4764381,2015
+150,Kadiovacik,Village of Kadiovacik,2016,404.65,2015,38.343,26.553367,Turkey,"North Africa, Middle East, West Asia",216,2016
+152,Kaohsiung,Kaohsiung City Government,2016,43502687.67,2014,22.633,120.266667,Taiwan,East Asia,2778729,2015
+154,Knoxville,City of Knoxville,2016,2061435,2012,35.973,-83.942222,USA,North America,184281,2014
+158,La Paz,Municipalidad de La Paz,2016,1152922.4,2012,-16.5,-68.15,Bolivia,Latin America & Caribbean,789541,2015
+159,Lagos,City of Lagos,2016,29426266,2014,6.455,3.384082,Nigeria,Africa,21000000,2016
+160,Lahti,City of Lahti,2017,717200,2015,60.983,25.65,Finland,Europe,119452,2016
+162,Lakewood,City of Lakewood,2016,553900,2007,39.705,-105.081389,USA,North America,149643,2014
+164,"Lancaster, PA","City of Lancaster, PA",2017,364183,2016,40.04,-76.304444,USA,North America,60000,2016
+166,Las Vegas,City of Las Vegas,2016,11002925,2014,36.175,-115.136389,USA,North America,621970,2016
+167,Lausanne,Ville de Lausanne,2016,456843,2013,46.52,6.6335,Switzerland,Europe,140000,2015
+171,Lima,Metropolitan Municipality of Lima,2016,12378734,2012,-12.043,-77.028333,Peru,Latin America & Caribbean,8755262,2014
+173,Lisbon,City of Lisbon,2016,1023910,2014,38.714,-9.139444,Portugal,Europe,547773,2011
+175,Ljubljana,City of Ljubljana,2017,1829808,2014,46.056,14.508333,Slovenia,Europe,288307,2016
+176,"London, ON","City of London, ON",2016,2771000,2014,42.984,-81.2497,Canada,North America,375000,2014
+177,Los Altos Hills,Los Altos Hills,2016,47552,2015,37.371,-122.1375,USA,North America,8334,2013
+178,Los Angeles,City of Los Angeles,2016,19595119,2013,34.05,-118.25,USA,North America,3928864,2014
+181,Madrid,Ayuntamiento de Madrid,2016,6795577,2013,40.383,-3.716667,Spain,Europe,3156572,2015
+183,Makati,City Government of Makati,2016,462586,2011,14.55,121.03,Philippines,Southeast Asia,529039,2010
+185,Manchester,Greater Manchester,2016,9587290,2013,53.467,-2.233333,United Kingdom,Europe,2762000,2014
+186,Melbourne,City of Melbourne,2016,785326,2015,-37.814,144.963056,Australia,Oceania,137889,2016
+187,Mexico City,Mexico City,2016,16842493.45,2014,19.433,-99.133333,Mexico,Latin America & Caribbean,8874724,2014
+188,Milano,Comune di Milano,2016,3728678,2013,45.467,9.183333,Italy,Europe,1350680,2014
+189,Minneapolis,City of Minneapolis,2016,2890572,2014,44.983,-93.266667,USA,North America,407207,2014
+190,Moita,Município de Moita,2016,49910,2013,38.65,-8.983333,Portugal,Europe,66029,2011
+191,Montreal,Ville de Montreal,2016,13634331,2009,45.5,-73.566667,Canada,North America,1886481,2011
+195,Nagoya,City of Nagoya,2016,7690000,2013,35.183,136.9,Japan,East Asia,2274511,2015
+202,Nashville and Davidson,Metropolitan Government of Nashville and Davidson County,2017,6680194,2014,36.155,-86.761944,USA,North America,678889,2015
+204,New Orleans,City of New Orleans,2016,2419891,2014,29.95,-90.066667,USA,North America,389617,2015
+205,New Taipei,New Taipei City Government,2016,6149934,2014,25.011,121.445833,Taiwan,East Asia,3966818,2014
+206,New York City,New York City,2017,38962264,2015,40.713,-74.0059,USA,North America,8537673,2016
+209,North Vancouver,City of North Vancouver,2017,182896,2015,49.317,-123.066667,Canada,North America,52898,2016
+210,Oakland,City of Oakland,2017,956414,2015,37.804,-122.270833,USA,North America,419000,2015
+211,Okayama,City of Okayama,2016,114000,2013,34.65,133.916667,Japan,East Asia,705917,2015
+213,Oslo,City of Oslo,2016,1094422,2013,59.95,10.75,Norway,Europe,658390,2016
+214,Palmas,Prefeitura de Palmas,2016,589055.31,2013,-10.184,-48.333611,Brazil,Latin America & Caribbean,272726,2015
+215,Palo Alto,City of Palo Alto,2016,152631,2015,37.429,-122.138056,USA,North America,66955,2015
+216,Paris,City of Paris,2016,3359242,2014,48.857,2.3508,France,Europe,2265886,2015
+219,Philadelphia,City of Philadelphia,2016,12397594.06,2012,39.95,-75.166667,USA,North America,1560297,2014
+222,"Piedmont, CA","City of Piedmont, CA",2016,17876,2011,37.817,-122.233333,USA,North America,11082,2013
+225,Pingtung,Pingtung County Government,2017,2768427.4,2013,22.675,120.491414,Taiwan,East Asia,835792,2016
+226,Pittsburgh,City of Pittsburgh,2016,2073556,2013,40.44,-79.976389,USA,North America,305704,2016
+227,"Portland, OR","City of Portland, OR",2016,4610317,2014,45.52,-122.681944,USA,North America,619360,2014
+230,Pretoria Tshwane,Pretoria - Tshwane,2016,4648959,2015,-25.667,28.333333,South Africa,Africa,3200000,2016
+236,Quito,Distrito Metropolitano de Quito,2016,4653611,2011,-0.217,-78.516667,Ecuador,Latin America & Caribbean,2239191,2010
+238,Ravenna,Comune di Ravenna,2016,11115,2014,44.417,12.2,Italy,Europe,158911,2015
+239,Recife,Municipality of Recife,2017,1333830,2015,-8.05,-34.9,Brazil,Latin America & Caribbean,1625583,2016
+240,Reno,City of Reno,2017,1371400,2014,39.527,-119.821944,USA,North America,236995,2014
+241,Reykjavík,City of Reykjavík,2016,346630,2013,64.133,-21.933333,Iceland,Europe,122460,2015
+242,"Richmond, VA","City of Richmond, VA",2016,1470127,2013,37.533,-77.466667,USA,North America,217853,2014
+243,Rio de Janeiro,Prefeitura do Rio de Janeiro,2016,16171534,2012,-22.908,-43.196389,Brazil,Latin America & Caribbean,6476631,2015
+247,Rotterdam,Gemeente Rotterdam,2017,17840039,2016,51.917,4.5,Netherlands,Europe,624000,2014
+248,Salvador,City of Salvador,2016,3242166,2013,-12.975,-38.476667,Brazil,Latin America & Caribbean,2902927,2014
+249,San Antonio,City of San Antonio,2016,7214051,2013,29.417,-98.5,USA,North America,1400000,2012
+252,San Francisco,City of San Francisco,2016,4186111,2012,37.783,-122.416667,USA,North America,864816,2015
+254,San Luis Potosí,Ayuntamiento de San Luis Potosí,2016,1096122.89,2015,22.151,-100.976111,Mexico,Latin America & Caribbean,824229,2015
+255,Santa Monica,City of Santa Monica,2017,119026,2015,34.022,-118.481389,USA,North America,92000,2014
+257,Santarém,Santarém,2016,245483,2006,39.233,-8.683333,Portugal,Europe,61752,2011
+258,Santiago,Región Metropolitana de Santiago,2016,23120027.8,2013,-33.45,-70.666667,Chile,Latin America & Caribbean,7314176,2015
+260,Santiago de Guayaquil,Santiago de Guayaquil,2016,5429899,2014,-2.183,-79.883333,Ecuador,Latin America & Caribbean,2350915,2010
+263,Savannah,City of Savannah,2017,2381327,2014,32.017,-81.116667,USA,North America,145674,2015
+264,Seattle,City of Seattle,2016,5150000,2012,47.61,-122.333056,USA,North America,662400,2015
+268,Seoul,Seoul Metropolitan Government,2016,25082520,2013,37.567,126.966667,South Korea,East Asia,10297138,2015
+276,"Somerville, MA","City of Somerville, MA",2016,461798,2014,42.388,-71.1,USA,North America,78900,2014
+277,Sorocaba,Prefeitura de Sorocaba,2016,980290,2012,-23.502,-47.458056,Brazil,Latin America & Caribbean,644919,2015
+278,St Louis,City of St Louis,2016,2481945.42,2014,38.627,-90.197778,USA,North America,319294,2010
+279,Stockholm,City of Stockholm,2017,1834343,2015,59.329,18.068611,Sweden,Europe,923516,2015
+281,Suwon,Suwon City,2016,3670140,2013,37.267,127.016667,South Korea,East Asia,1221973,2015
+283,Sydney,City of Sydney,2016,359064,2015,-33.865,151.209444,Australia,Oceania,205339,2015
+285,Taipei City,Taipei City Government,2016,6493593,2014,25.033,121.633333,Taiwan,East Asia,2704810,2015
+288,Taoyuan,Taoyuan City Hall,2017,21143194.6,2014,24.991,121.314328,Taiwan,East Asia,2153521,2017
+291,Tokyo,Tokyo Metropolitan Government,2016,27611000,2014,35.683,139.683333,Japan,East Asia,13513734,2015
+294,Toronto,City of Toronto,2016,16151019,2013,43.7,-79.4,Canada,North America,2753100,2011
+296,Tucson,City of Tucson,2016,2571089,2012,32.222,-110.926389,USA,North America,529845,2015
+297,Turku,City of Turku,2016,421200,2013,60.45,22.266667,Finland,Europe,186000,2015
+300,Udine,Comune di Udine,2016,438005,2013,46.067,13.233333,Italy,Europe,99528,2013
+302,"University City, MO","University City, MO",2016,266265,2005,38.664,-90.327778,USA,North America,35371,2010
+305,Vancouver,City of Vancouver,2016,2389748,2014,49.25,-123.1,Canada,North America,603500,2011
+306,Venezia,Comune di Venezia,2016,955471,2005,45.438,12.335833,Italy,Europe,263104,2016
+307,Vilnius,Vilnius City Municipality,2016,1994560,2013,54.683,25.283333,Lithuania,Europe,542626,2015
+309,Warsaw,City of Warsaw,2016,7930452,2014,52.233,21.016667,Poland,Europe,1626514,2016
+311,Wellington,Wellington City Council,2017,621179,2014,-41.289,174.777222,New Zealand,Oceania,209102,2017
+314,"Windsor, ON",City of Windsor,2016,2475703,2014,42.283,-83,Canada,North America,210891,2011
+315,Winnipeg,City of Winnipeg,2016,5167453,1998,49.899,-97.139167,Canada,North America,718400,2015
+327,Yilan,Yilan County,2016,6911264.55,2013,24.751,121.759167,Taiwan,East Asia,458777,2014
+329,Yokohama,City of Yokohama,2016,12572000,2013,35.444,139.638056,Japan,East Asia,3719589,2015
+330,Yonkers,City of Yonkers,2016,982940,2010,40.941,-73.864444,USA,North America,199766,2013
+331,Zaragoza,City of Zaragoza,2016,1175162.74,2014,41.65,-0.883333,Spain,Europe,661108,2015
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.json b/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.json
new file mode 100644
index 00000000..d21bf662
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.json
@@ -0,0 +1,2105 @@
+{
+ "meta":"https://doi.pangaea.de/10.1594/PANGAEA.884141",
+ "data":[
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+ "city_name_cdp": "Abington Township",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 193345,
+ "year_of_emission": 2010,
+ "latitude_degrees": 40.1,
+ "longitude_degrees": -75.099722,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 55310,
+ "population_year_cdp": 2010
+ },
+ {
+ "val": 2,
+ "city_name": "Addis Ababa",
+ "city_name_cdp": "Addis Ababa City Administration",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 3708292,
+ "year_of_emission": 2012,
+ "latitude_degrees": 9.03,
+ "longitude_degrees": 38.74,
+ "country": "Ethiopia",
+ "region": "Africa",
+ "population_cdp": 3384569,
+ "population_year_cdp": 2008
+ },
+ {
+ "val": 3,
+ "city_name": "Adelaide",
+ "city_name_cdp": "City of Adelaide",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 63724,
+ "year_of_emission": 2013,
+ "latitude_degrees": -34.929,
+ "longitude_degrees": 138.601,
+ "country": "Australia",
+ "region": "Oceania",
+ "population_cdp": 23169,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 7,
+ "city_name": "Ajax, ON",
+ "city_name_cdp": "Town of Ajax, ON",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 6354,
+ "year_of_emission": 2008,
+ "latitude_degrees": 43.858,
+ "longitude_degrees": -79.036389,
+ "country": "Canada",
+ "region": "North America",
+ "population_cdp": 109600,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 8,
+ "city_name": "Albany",
+ "city_name_cdp": "City of Albany",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 663997,
+ "year_of_emission": 2010,
+ "latitude_degrees": 42.653,
+ "longitude_degrees": -73.757222,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 97856,
+ "population_year_cdp": 2010
+ },
+ {
+ "val": 9,
+ "city_name": "Alton, IL",
+ "city_name_cdp": "City of Alton, IL",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 138542,
+ "year_of_emission": 2013,
+ "latitude_degrees": 38.901,
+ "longitude_degrees": -90.159722,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 26581,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 10,
+ "city_name": "Amman",
+ "city_name_cdp": "Greater Amman Municipality",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 3322603,
+ "year_of_emission": 2014,
+ "latitude_degrees": 31.95,
+ "longitude_degrees": 35.932778,
+ "country": "Jordan",
+ "region": "North Africa, Middle East, West Asia",
+ "population_cdp": 3604459,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 11,
+ "city_name": "Amsterdam",
+ "city_name_cdp": "City of Amsterdam",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2161000,
+ "year_of_emission": 2016,
+ "latitude_degrees": 52.367,
+ "longitude_degrees": 4.9,
+ "country": "Netherlands",
+ "region": "Europe",
+ "population_cdp": 822272,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 14,
+ "city_name": "Arlington, VA",
+ "city_name_cdp": "City of Arlington, VA",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 1257464,
+ "year_of_emission": 2015,
+ "latitude_degrees": 38.881,
+ "longitude_degrees": -77.1372613,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 216700,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 16,
+ "city_name": "Athens",
+ "city_name_cdp": "City of Athens",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 1260355,
+ "year_of_emission": 2014,
+ "latitude_degrees": 37.984,
+ "longitude_degrees": 23.727806,
+ "country": "Greece",
+ "region": "Europe",
+ "population_cdp": 664046,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 17,
+ "city_name": "Atlanta",
+ "city_name_cdp": "City of Atlanta",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 3548215,
+ "year_of_emission": 2014,
+ "latitude_degrees": 33.755,
+ "longitude_degrees": -84.39,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 443775,
+ "population_year_cdp": 2013
+ },
+ {
+ "val": 18,
+ "city_name": "Auckland",
+ "city_name_cdp": "Auckland Council",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 9133523,
+ "year_of_emission": 2013,
+ "latitude_degrees": -36.841,
+ "longitude_degrees": 174.74,
+ "country": "New Zealand",
+ "region": "Oceania",
+ "population_cdp": 1569900,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 19,
+ "city_name": "Austin",
+ "city_name_cdp": "City of Austin",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 6439000,
+ "year_of_emission": 2013,
+ "latitude_degrees": 30.267,
+ "longitude_degrees": -97.733333,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 888204,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 21,
+ "city_name": "Baltimore",
+ "city_name_cdp": "City of Baltimore",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 4019044,
+ "year_of_emission": 2014,
+ "latitude_degrees": 39.283,
+ "longitude_degrees": -76.616667,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 614664,
+ "population_year_cdp": 2016
+ },
+ {
+ "val": 28,
+ "city_name": "Barreiro",
+ "city_name_cdp": "Barreiro",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 203441,
+ "year_of_emission": 2014,
+ "latitude_degrees": 38.667,
+ "longitude_degrees": -9.066667,
+ "country": "Portugal",
+ "region": "Europe",
+ "population_cdp": 78764,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 29,
+ "city_name": "Basel",
+ "city_name_cdp": "Basel-Stadt",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 783932,
+ "year_of_emission": 2016,
+ "latitude_degrees": 47.567,
+ "longitude_degrees": 7.6,
+ "country": "Switzerland",
+ "region": "Europe",
+ "population_cdp": 198206,
+ "population_year_cdp": 2016
+ },
+ {
+ "val": 30,
+ "city_name": "Batangas",
+ "city_name_cdp": "Batangas City",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 344419,
+ "year_of_emission": 2010,
+ "latitude_degrees": 13.83,
+ "longitude_degrees": 121,
+ "country": "Philippines",
+ "region": "Southeast Asia",
+ "population_cdp": 332458,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 32,
+ "city_name": "Belo Horizonte",
+ "city_name_cdp": "Municipality of Belo Horizonte",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2804787,
+ "year_of_emission": 2013,
+ "latitude_degrees": -19.917,
+ "longitude_degrees": -43.933333,
+ "country": "Brazil",
+ "region": "Latin America & Caribbean",
+ "population_cdp": 2502557,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 38,
+ "city_name": "Blacksburg",
+ "city_name_cdp": "Town of Blacksburg",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 124461.79,
+ "year_of_emission": 2015,
+ "latitude_degrees": 37.23,
+ "longitude_degrees": -80.417778,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 44215,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 39,
+ "city_name": "Bogor",
+ "city_name_cdp": "Bogor City Government",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 954020,
+ "year_of_emission": 2014,
+ "latitude_degrees": -6.597,
+ "longitude_degrees": 106.7972,
+ "country": "Indonesia",
+ "region": "Southeast Asia",
+ "population_cdp": 1047922,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 40,
+ "city_name": "Bogotá",
+ "city_name_cdp": "Bogotá Distrito Capital",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 10679585.14,
+ "year_of_emission": 2015,
+ "latitude_degrees": 4.711,
+ "longitude_degrees": -74.072222,
+ "country": "Colombia",
+ "region": "Latin America & Caribbean",
+ "population_cdp": 7878783,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 43,
+ "city_name": "Boston",
+ "city_name_cdp": "City of Boston",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 3603270,
+ "year_of_emission": 2014,
+ "latitude_degrees": 42.358,
+ "longitude_degrees": -71.063611,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 646000,
+ "population_year_cdp": 2013
+ },
+ {
+ "val": 44,
+ "city_name": "Boulder",
+ "city_name_cdp": "City of Boulder",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 741287,
+ "year_of_emission": 2012,
+ "latitude_degrees": 40.027,
+ "longitude_degrees": -105.251945,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 104810,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 45,
+ "city_name": "Bournemouth",
+ "city_name_cdp": "City of Bournemouth",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 350775,
+ "year_of_emission": 2013,
+ "latitude_degrees": 50.72,
+ "longitude_degrees": -1.88,
+ "country": "United Kingdom",
+ "region": "Europe",
+ "population_cdp": 191400,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 46,
+ "city_name": "Brasília",
+ "city_name_cdp": "City of Brasília",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 7739830,
+ "year_of_emission": 2012,
+ "latitude_degrees": -15.79,
+ "longitude_degrees": -47.88,
+ "country": "Brazil",
+ "region": "Latin America & Caribbean",
+ "population_cdp": 1409671,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 50,
+ "city_name": "Buenos Aires",
+ "city_name_cdp": "City of Buenos Aires",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 15561157,
+ "year_of_emission": 2014,
+ "latitude_degrees": -34.603,
+ "longitude_degrees": -58.381667,
+ "country": "Argentina",
+ "region": "Latin America & Caribbean",
+ "population_cdp": 3054267,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 52,
+ "city_name": "Burlington",
+ "city_name_cdp": "City of Burlington",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 368077,
+ "year_of_emission": 2010,
+ "latitude_degrees": 44.476,
+ "longitude_degrees": -73.211944,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 42284,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 53,
+ "city_name": "Calgary",
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+ "latitude_degrees": 47.61,
+ "longitude_degrees": -122.333056,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 662400,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 268,
+ "city_name": "Seoul",
+ "city_name_cdp": "Seoul Metropolitan Government",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 25082520,
+ "year_of_emission": 2013,
+ "latitude_degrees": 37.567,
+ "longitude_degrees": 126.966667,
+ "country": "South Korea",
+ "region": "East Asia",
+ "population_cdp": 10297138,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 276,
+ "city_name": "Somerville, MA",
+ "city_name_cdp": "City of Somerville, MA",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 461798,
+ "year_of_emission": 2014,
+ "latitude_degrees": 42.388,
+ "longitude_degrees": -71.1,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 78900,
+ "population_year_cdp": 2014
+ },
+ {
+ "val": 277,
+ "city_name": "Sorocaba",
+ "city_name_cdp": "Prefeitura de Sorocaba",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 980290,
+ "year_of_emission": 2012,
+ "latitude_degrees": -23.502,
+ "longitude_degrees": -47.458056,
+ "country": "Brazil",
+ "region": "Latin America & Caribbean",
+ "population_cdp": 644919,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 278,
+ "city_name": "St Louis",
+ "city_name_cdp": "City of St Louis",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2481945.42,
+ "year_of_emission": 2014,
+ "latitude_degrees": 38.627,
+ "longitude_degrees": -90.197778,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 319294,
+ "population_year_cdp": 2010
+ },
+ {
+ "val": 279,
+ "city_name": "Stockholm",
+ "city_name_cdp": "City of Stockholm",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 1834343,
+ "year_of_emission": 2015,
+ "latitude_degrees": 59.329,
+ "longitude_degrees": 18.068611,
+ "country": "Sweden",
+ "region": "Europe",
+ "population_cdp": 923516,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 281,
+ "city_name": "Suwon",
+ "city_name_cdp": "Suwon City",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 3670140,
+ "year_of_emission": 2013,
+ "latitude_degrees": 37.267,
+ "longitude_degrees": 127.016667,
+ "country": "South Korea",
+ "region": "East Asia",
+ "population_cdp": 1221973,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 283,
+ "city_name": "Sydney",
+ "city_name_cdp": "City of Sydney",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 359064,
+ "year_of_emission": 2015,
+ "latitude_degrees": -33.865,
+ "longitude_degrees": 151.209444,
+ "country": "Australia",
+ "region": "Oceania",
+ "population_cdp": 205339,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 285,
+ "city_name": "Taipei City",
+ "city_name_cdp": "Taipei City Government",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 6493593,
+ "year_of_emission": 2014,
+ "latitude_degrees": 25.033,
+ "longitude_degrees": 121.633333,
+ "country": "Taiwan",
+ "region": "East Asia",
+ "population_cdp": 2704810,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 288,
+ "city_name": "Taoyuan",
+ "city_name_cdp": "Taoyuan City Hall",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 21143194.6,
+ "year_of_emission": 2014,
+ "latitude_degrees": 24.991,
+ "longitude_degrees": 121.314328,
+ "country": "Taiwan",
+ "region": "East Asia",
+ "population_cdp": 2153521,
+ "population_year_cdp": 2017
+ },
+ {
+ "val": 291,
+ "city_name": "Tokyo",
+ "city_name_cdp": "Tokyo Metropolitan Government",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 27611000,
+ "year_of_emission": 2014,
+ "latitude_degrees": 35.683,
+ "longitude_degrees": 139.683333,
+ "country": "Japan",
+ "region": "East Asia",
+ "population_cdp": 13513734,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 294,
+ "city_name": "Toronto",
+ "city_name_cdp": "City of Toronto",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 16151019,
+ "year_of_emission": 2013,
+ "latitude_degrees": 43.7,
+ "longitude_degrees": -79.4,
+ "country": "Canada",
+ "region": "North America",
+ "population_cdp": 2753100,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 296,
+ "city_name": "Tucson",
+ "city_name_cdp": "City of Tucson",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2571089,
+ "year_of_emission": 2012,
+ "latitude_degrees": 32.222,
+ "longitude_degrees": -110.926389,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 529845,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 297,
+ "city_name": "Turku",
+ "city_name_cdp": "City of Turku",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 421200,
+ "year_of_emission": 2013,
+ "latitude_degrees": 60.45,
+ "longitude_degrees": 22.266667,
+ "country": "Finland",
+ "region": "Europe",
+ "population_cdp": 186000,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 300,
+ "city_name": "Udine",
+ "city_name_cdp": "Comune di Udine",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 438005,
+ "year_of_emission": 2013,
+ "latitude_degrees": 46.067,
+ "longitude_degrees": 13.233333,
+ "country": "Italy",
+ "region": "Europe",
+ "population_cdp": 99528,
+ "population_year_cdp": 2013
+ },
+ {
+ "val": 302,
+ "city_name": "University City, MO",
+ "city_name_cdp": "University City, MO",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 266265,
+ "year_of_emission": 2005,
+ "latitude_degrees": 38.664,
+ "longitude_degrees": -90.327778,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 35371,
+ "population_year_cdp": 2010
+ },
+ {
+ "val": 305,
+ "city_name": "Vancouver",
+ "city_name_cdp": "City of Vancouver",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2389748,
+ "year_of_emission": 2014,
+ "latitude_degrees": 49.25,
+ "longitude_degrees": -123.1,
+ "country": "Canada",
+ "region": "North America",
+ "population_cdp": 603500,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 306,
+ "city_name": "Venezia",
+ "city_name_cdp": "Comune di Venezia",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 955471,
+ "year_of_emission": 2005,
+ "latitude_degrees": 45.438,
+ "longitude_degrees": 12.335833,
+ "country": "Italy",
+ "region": "Europe",
+ "population_cdp": 263104,
+ "population_year_cdp": 2016
+ },
+ {
+ "val": 307,
+ "city_name": "Vilnius",
+ "city_name_cdp": "Vilnius City Municipality",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 1994560,
+ "year_of_emission": 2013,
+ "latitude_degrees": 54.683,
+ "longitude_degrees": 25.283333,
+ "country": "Lithuania",
+ "region": "Europe",
+ "population_cdp": 542626,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 309,
+ "city_name": "Warsaw",
+ "city_name_cdp": "City of Warsaw",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 7930452,
+ "year_of_emission": 2014,
+ "latitude_degrees": 52.233,
+ "longitude_degrees": 21.016667,
+ "country": "Poland",
+ "region": "Europe",
+ "population_cdp": 1626514,
+ "population_year_cdp": 2016
+ },
+ {
+ "val": 311,
+ "city_name": "Wellington",
+ "city_name_cdp": "Wellington City Council",
+ "reporting_year_cdp": 2017,
+ "scope1_ghg_emissions_tons_co2e": 621179,
+ "year_of_emission": 2014,
+ "latitude_degrees": -41.289,
+ "longitude_degrees": 174.777222,
+ "country": "New Zealand",
+ "region": "Oceania",
+ "population_cdp": 209102,
+ "population_year_cdp": 2017
+ },
+ {
+ "val": 314,
+ "city_name": "Windsor, ON",
+ "city_name_cdp": "City of Windsor",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 2475703,
+ "year_of_emission": 2014,
+ "latitude_degrees": 42.283,
+ "longitude_degrees": -83,
+ "country": "Canada",
+ "region": "North America",
+ "population_cdp": 210891,
+ "population_year_cdp": 2011
+ },
+ {
+ "val": 315,
+ "city_name": "Winnipeg",
+ "city_name_cdp": "City of Winnipeg",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 5167453,
+ "year_of_emission": 1998,
+ "latitude_degrees": 49.899,
+ "longitude_degrees": -97.139167,
+ "country": "Canada",
+ "region": "North America",
+ "population_cdp": 718400,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 327,
+ "city_name": "Yilan",
+ "city_name_cdp": "Yilan County",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 6911264.55,
+ "year_of_emission": 2013,
+ "latitude_degrees": 24.751,
+ "longitude_degrees": 121.759167,
+ "country": "Taiwan",
+ "region": "East Asia",
+ "population_cdp": 458777,
+ "population_year_cdp": 2014
+ },
+ {
+ "val": 329,
+ "city_name": "Yokohama",
+ "city_name_cdp": "City of Yokohama",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 12572000,
+ "year_of_emission": 2013,
+ "latitude_degrees": 35.444,
+ "longitude_degrees": 139.638056,
+ "country": "Japan",
+ "region": "East Asia",
+ "population_cdp": 3719589,
+ "population_year_cdp": 2015
+ },
+ {
+ "val": 330,
+ "city_name": "Yonkers",
+ "city_name_cdp": "City of Yonkers",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 982940,
+ "year_of_emission": 2010,
+ "latitude_degrees": 40.941,
+ "longitude_degrees": -73.864444,
+ "country": "USA",
+ "region": "North America",
+ "population_cdp": 199766,
+ "population_year_cdp": 2013
+ },
+ {
+ "val": 331,
+ "city_name": "Zaragoza",
+ "city_name_cdp": "City of Zaragoza",
+ "reporting_year_cdp": 2016,
+ "scope1_ghg_emissions_tons_co2e": 1175162.74,
+ "year_of_emission": 2014,
+ "latitude_degrees": 41.65,
+ "longitude_degrees": -0.883333,
+ "country": "Spain",
+ "region": "Europe",
+ "population_cdp": 661108,
+ "population_year_cdp": 2015
+ }
+ ]
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/index.html b/p5js/NeuralNetwork/NeuralNetwork_co2net/index.html
new file mode 100755
index 00000000..2fcf465b
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_co2net/index.html
@@ -0,0 +1,69 @@
+
+
+
+
+ co2Net - Neural Network
+
+
+
+
+
+
+
+
+
+ co2Net - Neural Network
+ Urban Population vs. CO2 Emissions
+ Population and CO2 emissions (in tons of CO2 or CO2 equivalent).
+ data: https://doi.pangaea.de/10.1594/PANGAEA.884141
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js
new file mode 100644
index 00000000..02f2e1a1
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js
@@ -0,0 +1,105 @@
+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Image classification using MobileNet and p5.js
+This example uses a callback pattern to create the classifier
+// x: population_cdp
+// y: scope1_ghg_emissions_tons_co2e
+=== */
+let nn;
+let data;
+
+let inputMeta;
+let outputMeta;
+
+// Options for Neural Network
+const options = {
+ inputs: ['population_cdp'],
+ outputs: ['scope1_ghg_emissions_tons_co2e'],
+ dataUrl:'data/co2stats.csv',
+ task:'regression',
+ debug: true
+};
+
+
+function setup() {
+ createCanvas(400, 400);
+ // background(0, 27, 68);
+ background(244, 244, 244);
+
+ // Step 1: Create Neural Network
+ nn = ml5.neuralNetwork(options, modelLoaded);
+
+}
+
+function modelLoaded(){
+ // console.log(nn.data.data.raw);
+ // co2 data and population can be log10 transformed
+ // nn.data.data = nn.data.data.map( item => {
+ // item.xs.population_cdp = Math.log10(item.xs.population_cdp)
+ // item.ys.scope1_ghg_emissions_tons_co2e = Math.log10(item.ys.scope1_ghg_emissions_tons_co2e)
+ // return item;
+ // })
+ nn.normalizeData();
+
+ const trainingOptions = {
+ epochs: 50,
+ batchSize:12
+ }
+ nn.train(trainingOptions, finishedTraining)
+}
+
+async function finishedTraining(){
+ inputMin = nn.data.data.inputMin;
+ inputMax = nn.data.data.inputMax;
+ outputMin = nn.data.data.outputMin;
+ outputMax = nn.data.data.outputMax;
+
+
+ nn.data.data.raw.forEach( item => {
+ const normx = map(item.xs.population_cdp, inputMin[0], inputMax[0], 0, width);
+ const normy = map(item.ys.scope1_ghg_emissions_tons_co2e, outputMin[0], outputMax[0], height, 0);
+ fill(0,255,255);
+ ellipse(normx, normy, 4, 4);
+ })
+
+ await Promise.all(
+ [
+ 100,
+ 50000,
+ 100000,
+ 500000,
+ 2500000,
+ 5000000,
+ 10000000,
+ 15000000,
+ ].map( (val, idx) => predict(val) )
+ )
+
+
+}
+
+async function predict(val){
+ // const input = Math.log10(val);
+ const input = val
+ const prediction = await nn.predict([input]);
+ const output = {x: val, y: prediction[0].value}
+ const x = map(output.x, inputMin[0], inputMax[0], 0, width);
+ const y = map(output.y, outputMin[0], outputMax[0], height, 0);
+
+ console.log(output)
+
+ rectMode(CENTER);
+ fill(255,0,0);
+ rect(x, y, 6, 6);
+ text(`pop:${output.x}`, x, y)
+ text(`tons_co2e:${output.y}`, x, y+10)
+
+
+}
+
+
diff --git a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData.json b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData.json
new file mode 100755
index 00000000..c1561232
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData.json
@@ -0,0 +1,39505 @@
+{
+ "entries": [
+ {
+ "b": 155,
+ "g": 183,
+ "label": "green-ish",
+ "r": 81,
+ "uid": "EjbbUhVExBSZxtpKfcQ5qzT7jDW2"
+ },
+ {
+ "b": 71,
+ "g": 22,
+ "label": "pink-ish",
+ "r": 249,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 33,
+ "g": 196,
+ "label": "orange-ish",
+ "r": 254,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 237,
+ "g": 147,
+ "label": "blue-ish",
+ "r": 170,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 225,
+ "g": 159,
+ "label": "blue-ish",
+ "r": 15,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 166,
+ "g": 235,
+ "label": "green-ish",
+ "r": 79,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 73,
+ "g": 250,
+ "label": "green-ish",
+ "r": 29,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 31,
+ "g": 245,
+ "label": "green-ish",
+ "r": 55,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 235,
+ "g": 230,
+ "label": "blue-ish",
+ "r": 221,
+ "uid": "CjIU4lEAoiPQXacjMVYSYz9bmpf1"
+ },
+ {
+ "b": 52,
+ "g": 182,
+ "label": "orange-ish",
+ "r": 196,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 51,
+ "g": 72,
+ "label": "brown-ish",
+ "r": 123,
+ "uid": "CjIU4lEAoiPQXacjMVYSYz9bmpf1"
+ },
+ {
+ "b": 2,
+ "g": 10,
+ "label": "red-ish",
+ "r": 209,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 160,
+ "g": 104,
+ "label": "pink-ish",
+ "r": 177,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 18,
+ "g": 207,
+ "label": "yellow-ish",
+ "r": 214,
+ "uid": "CjIU4lEAoiPQXacjMVYSYz9bmpf1"
+ },
+ {
+ "b": 24,
+ "g": 165,
+ "label": "orange-ish",
+ "r": 239,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 49,
+ "g": 155,
+ "label": "green-ish",
+ "r": 128,
+ "uid": "wHjQX3jIO9QdONvjUQDZAJd7e6p2"
+ },
+ {
+ "b": 208,
+ "g": 11,
+ "label": "purple-ish",
+ "r": 137,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 125,
+ "g": 74,
+ "label": "pink-ish",
+ "r": 205,
+ "uid": "wHjQX3jIO9QdONvjUQDZAJd7e6p2"
+ },
+ {
+ "b": 65,
+ "g": 126,
+ "label": "brown-ish",
+ "r": 179,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 162,
+ "g": 83,
+ "label": "blue-ish",
+ "r": 76,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 63,
+ "g": 231,
+ "label": "green-ish",
+ "r": 116,
+ "uid": "wHjQX3jIO9QdONvjUQDZAJd7e6p2"
+ },
+ {
+ "b": 195,
+ "g": 16,
+ "label": "purple-ish",
+ "r": 141,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 161,
+ "g": 210,
+ "label": "green-ish",
+ "r": 149,
+ "uid": "CjIU4lEAoiPQXacjMVYSYz9bmpf1"
+ },
+ {
+ "b": 21,
+ "g": 18,
+ "label": "purple-ish",
+ "r": 48,
+ "uid": "wHjQX3jIO9QdONvjUQDZAJd7e6p2"
+ },
+ {
+ "b": 59,
+ "g": 49,
+ "label": "red-ish",
+ "r": 250,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 22,
+ "g": 131,
+ "label": "yellow-ish",
+ "r": 58,
+ "uid": "8D6XGh0wtsQXVGs73Xtg14cYGEu1"
+ },
+ {
+ "b": 76,
+ "g": 113,
+ "label": "brown-ish",
+ "r": 201,
+ "uid": "CjIU4lEAoiPQXacjMVYSYz9bmpf1"
+ },
+ {
+ "b": 206,
+ "g": 6,
+ "label": "blue-ish",
+ "r": 60,
+ "uid": "fpqsSD6CvNNFQmRp9sJDdI1QJm32"
+ },
+ {
+ "b": 187,
+ "g": 117,
+ "label": "blue-ish",
+ "r": 58,
+ "uid": "wHjQX3jIO9QdONvjUQDZAJd7e6p2"
+ },
+ {
+ "b": 9,
+ "g": 117,
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diff --git a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData_small.json b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/data/colorData_small.json
new file mode 100644
index 00000000..0229ac78
--- /dev/null
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+ },
+ {
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+ },
+ {
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+ "uid": "gWrBr8lBzKM24vA5g1zbCv72kY22"
+ },
+ {
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+ "uid": "t2cJPFfnaWXcn21XXFDlGos4RyG3"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/index.html b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/index.html
new file mode 100644
index 00000000..a640a229
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/index.html
@@ -0,0 +1,21 @@
+
+
+
+
+ Color Classifier - Neural Network
+
+
+
+
+
+
+
+
+
+ Color Classifier - Neural Network
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js
new file mode 100644
index 00000000..988beac4
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js
@@ -0,0 +1,70 @@
+let neuralNetwork;
+let submitButton;
+
+let rSlider, gSlider, bSlider;
+let labelP;
+let lossP;
+
+function setup() {
+ // Crude interface
+ lossP = createP('loss');
+
+ createCanvas(100, 100);
+
+ labelP = createP('label');
+
+ rSlider = createSlider(0, 255, 255);
+ gSlider = createSlider(0, 255, 0);
+ bSlider = createSlider(0, 255, 255);
+
+ let nnOptions = {
+ dataUrl: 'data/colorData.json',
+ inputs: ['r', 'g', 'b'],
+ outputs: ['label'],
+ task: 'classification',
+ debug: true
+ };
+ neuralNetwork = ml5.neuralNetwork(nnOptions, modelReady);
+}
+
+function modelReady() {
+ neuralNetwork.normalizeData();
+ const trainingOptions = {
+ epochs: 20,
+ batchSize: 64
+ }
+ neuralNetwork.train(trainingOptions, whileTraining, finishedTraining);
+ // Start guessing while training!
+ classify();
+
+}
+
+function whileTraining(epoch, logs) {
+ lossP.html(`Epoch: ${epoch} - loss: ${logs.loss.toFixed(2)}`);
+}
+
+function finishedTraining(anything) {
+ console.log('done!');
+}
+
+function classify() {
+ let inputs = {
+ r: rSlider.value(),
+ g: gSlider.value(),
+ b: bSlider.value()
+ }
+ neuralNetwork.classify([inputs.r, inputs.g, inputs.b], gotResults);
+}
+
+function gotResults(error, results) {
+ if (error) {
+ console.error(error);
+ } else {
+ labelP.html(`label:${results[0].label}, confidence: ${results[0].confidence.toFixed(2)}`);
+ classify();
+ }
+}
+
+function draw() {
+ background(rSlider.value(), gSlider.value(), bSlider.value());
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_model/index.html b/p5js/NeuralNetwork/NeuralNetwork_load_model/index.html
new file mode 100644
index 00000000..a640a229
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_model/index.html
@@ -0,0 +1,21 @@
+
+
+
+
+ Color Classifier - Neural Network
+
+
+
+
+
+
+
+
+
+ Color Classifier - Neural Network
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.json b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.json
new file mode 100644
index 00000000..434f5300
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.json
@@ -0,0 +1 @@
+{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"sigmoid","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,3],"dtype":"float32"}},{"class_name":"Dense","config":{"units":9,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[3,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,9],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[9],"dtype":"float32"}]}]}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.weights.bin b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.weights.bin
new file mode 100644
index 00000000..01de5f03
Binary files /dev/null and b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model.weights.bin differ
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model_meta.json b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model_meta.json
new file mode 100644
index 00000000..45281b75
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_model/model/model_meta.json
@@ -0,0 +1 @@
+{"data":{"inputMin":[0,0,0],"inputMax":[255,255,255],"outputMin":0,"outputMax":1},"meta":{"inputUnits":3,"outputUnits":9,"inputs":{"b":{"dtype":"number"},"g":{"dtype":"number"},"r":{"dtype":"number"}},"outputs":{"label":{"dtype":"string","uniqueValues":["green-ish","pink-ish","orange-ish","blue-ish","brown-ish","red-ish","yellow-ish","purple-ish","grey-ish"],"legend":{"green-ish":[1,0,0,0,0,0,0,0,0],"pink-ish":[0,1,0,0,0,0,0,0,0],"orange-ish":[0,0,1,0,0,0,0,0,0],"blue-ish":[0,0,0,1,0,0,0,0,0],"brown-ish":[0,0,0,0,1,0,0,0,0],"red-ish":[0,0,0,0,0,1,0,0,0],"yellow-ish":[0,0,0,0,0,0,1,0,0],"purple-ish":[0,0,0,0,0,0,0,1,0],"grey-ish":[0,0,0,0,0,0,0,0,1]}}},"isNormalized":true}}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_model/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_load_model/sketch.js
new file mode 100644
index 00000000..2a36f624
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_model/sketch.js
@@ -0,0 +1,66 @@
+let neuralNetwork;
+let submitButton;
+
+let rSlider, gSlider, bSlider;
+let labelP;
+let lossP;
+
+function setup() {
+ // Crude interface
+ lossP = createP('loss');
+
+ createCanvas(100, 100);
+
+ labelP = createP('label');
+
+ rSlider = createSlider(0, 255, 255);
+ gSlider = createSlider(0, 255, 0);
+ bSlider = createSlider(0, 255, 255);
+
+ let nnOptions = {
+ inputs: ['r', 'g', 'b'],
+ outputs: ['label'],
+ task: 'classification',
+ debug: true
+ };
+ neuralNetwork = ml5.neuralNetwork(nnOptions);
+
+ // option 1: Load model explictly pointing to each file
+ const modelDetails = {
+ model: 'model/model.json',
+ metadata: 'model/model_meta.json',
+ weights: 'model/model.weights.bin'
+ }
+ neuralNetwork.load(modelDetails, modelReady);
+
+ // option 2: Load model just pointing to the model file
+ // neuralNetwork.load('model/model.json', modelReady);
+}
+
+function modelReady() {
+ console.log('model loaded!')
+ classify();
+};
+
+
+function classify() {
+ let inputs = {
+ r: rSlider.value(),
+ g: gSlider.value(),
+ b: bSlider.value()
+ }
+ neuralNetwork.classify(inputs, gotResults);
+}
+
+function gotResults(error, results) {
+ if (error) {
+ console.error(error);
+ } else {
+ labelP.html(`label:${results[0].label}, confidence: ${results[0].confidence.toFixed(2)}`);
+ classify();
+ }
+}
+
+function draw() {
+ background(rSlider.value(), gSlider.value(), bSlider.value());
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/data/xy-classifier.json b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/data/xy-classifier.json
new file mode 100644
index 00000000..23d8f54c
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/data/xy-classifier.json
@@ -0,0 +1 @@
+{"data":[{"xs":{"x":43,"y":44.56666564941406},"ys":{"label":"A"}},{"xs":{"x":43,"y":44.56666564941406},"ys":{"label":"A"}},{"xs":{"x":43,"y":44.56666564941406},"ys":{"label":"A"}},{"xs":{"x":98,"y":50.56666564941406},"ys":{"label":"A"}},{"xs":{"x":99,"y":50.56666564941406},"ys":{"label":"A"}},{"xs":{"x":91,"y":62.56666564941406},"ys":{"label":"A"}},{"xs":{"x":84,"y":105.56666564941406},"ys":{"label":"A"}},{"xs":{"x":50,"y":103.56666564941406},"ys":{"label":"A"}},{"xs":{"x":47,"y":96.56666564941406},"ys":{"label":"A"}},{"xs":{"x":79,"y":46.56666564941406},"ys":{"label":"A"}},{"xs":{"x":101,"y":45.56666564941406},"ys":{"label":"A"}},{"xs":{"x":160,"y":45.56666564941406},"ys":{"label":"A"}},{"xs":{"x":157,"y":81.56666564941406},"ys":{"label":"A"}},{"xs":{"x":155,"y":113.56666564941406},"ys":{"label":"A"}},{"xs":{"x":123,"y":135.56666564941406},"ys":{"label":"A"}},{"xs":{"x":65,"y":135.56666564941406},"ys":{"label":"A"}},{"xs":{"x":48,"y":135.56666564941406},"ys":{"label":"A"}},{"xs":{"x":40,"y":108.56666564941406},"ys":{"label":"A"}},{"xs":{"x":65,"y":59.56666564941406},"ys":{"label":"A"}},{"xs":{"x":89,"y":57.56666564941406},"ys":{"label":"A"}},{"xs":{"x":121,"y":64.56666564941406},"ys":{"label":"A"}},{"xs":{"x":122,"y":100.56666564941406},"ys":{"label":"A"}},{"xs":{"x":120,"y":103.56666564941406},"ys":{"label":"A"}},{"xs":{"x":75,"y":99.56666564941406},"ys":{"label":"A"}},{"xs":{"x":65,"y":89.56666564941406},"ys":{"label":"A"}},{"xs":{"x":78,"y":72.56666564941406},"ys":{"label":"A"}},{"xs":{"x":94,"y":72.56666564941406},"ys":{"label":"A"}},{"xs":{"x":99,"y":91.56666564941406},"ys":{"label":"A"}},{"xs":{"x":93,"y":124.56666564941406},"ys":{"label":"A"}},{"xs":{"x":85,"y":129.56666564941406},"ys":{"label":"A"}},{"xs":{"x":59,"y":120.56666564941406},"ys":{"label":"A"}},{"xs":{"x":54,"y":112.56666564941406},"ys":{"label":"A"}},{"xs":{"x":49,"y":75.56666564941406},"ys":{"label":"A"}},{"xs":{"x":49,"y":71.56666564941406},"ys":{"label":"A"}},{"xs":{"x":67,"y":55.56666564941406},"ys":{"label":"A"}},{"xs":{"x":98,"y":47.56666564941406},"ys":{"label":"A"}},{"xs":{"x":131,"y":51.56666564941406},"ys":{"label":"A"}},{"xs":{"x":131,"y":68.56666564941406},"ys":{"label":"A"}},{"xs":{"x":130,"y":92.56666564941406},"ys":{"label":"A"}},{"xs":{"x":117,"y":129.56666564941406},"ys":{"label":"A"}},{"xs":{"x":104,"y":141.56666564941406},"ys":{"label":"A"}},{"xs":{"x":72,"y":142.56666564941406},"ys":{"label":"A"}},{"xs":{"x":51,"y":121.56666564941406},"ys":{"label":"A"}},{"xs":{"x":44,"y":59.56666564941406},"ys":{"label":"A"}},{"xs":{"x":61,"y":27.566665649414062},"ys":{"label":"A"}},{"xs":{"x":109,"y":26.566665649414062},"ys":{"label":"A"}},{"xs":{"x":123,"y":26.566665649414062},"ys":{"label":"A"}},{"xs":{"x":149,"y":50.56666564941406},"ys":{"label":"A"}},{"xs":{"x":147,"y":70.56666564941406},"ys":{"label":"A"}},{"xs":{"x":147,"y":117.56666564941406},"ys":{"label":"A"}},{"xs":{"x":137,"y":137.56666564941406},"ys":{"label":"A"}},{"xs":{"x":91,"y":150.56666564941406},"ys":{"label":"A"}},{"xs":{"x":62,"y":143.56666564941406},"ys":{"label":"A"}},{"xs":{"x":57,"y":141.56666564941406},"ys":{"label":"A"}},{"xs":{"x":29,"y":61.56666564941406},"ys":{"label":"A"}},{"xs":{"x":63,"y":34.56666564941406},"ys":{"label":"A"}},{"xs":{"x":79,"y":30.566665649414062},"ys":{"label":"A"}},{"xs":{"x":90,"y":33.56666564941406},"ys":{"label":"A"}},{"xs":{"x":258,"y":247.56666564941406},"ys":{"label":"B"}},{"xs":{"x":288,"y":235.56666564941406},"ys":{"label":"B"}},{"xs":{"x":301,"y":235.56666564941406},"ys":{"label":"B"}},{"xs":{"x":319,"y":240.56666564941406},"ys":{"label":"B"}},{"xs":{"x":319,"y":244.56666564941406},"ys":{"label":"B"}},{"xs":{"x":345,"y":292.56666564941406},"ys":{"label":"B"}},{"xs":{"x":349,"y":315.56666564941406},"ys":{"label":"B"}},{"xs":{"x":323,"y":350.56666564941406},"ys":{"label":"B"}},{"xs":{"x":313,"y":345.56666564941406},"ys":{"label":"B"}},{"xs":{"x":262,"y":344.56666564941406},"ys":{"label":"B"}},{"xs":{"x":267,"y":244.56666564941406},"ys":{"label":"B"}},{"xs":{"x":322,"y":228.56666564941406},"ys":{"label":"B"}},{"xs":{"x":345,"y":229.56666564941406},"ys":{"label":"B"}},{"xs":{"x":347,"y":239.56666564941406},"ys":{"label":"B"}},{"xs":{"x":357,"y":286.56666564941406},"ys":{"label":"B"}},{"xs":{"x":357,"y":287.56666564941406},"ys":{"label":"B"}},{"xs":{"x":352,"y":329.56666564941406},"ys":{"label":"B"}},{"xs":{"x":352,"y":330.56666564941406},"ys":{"label":"B"}},{"xs":{"x":340,"y":344.56666564941406},"ys":{"label":"B"}},{"xs":{"x":331,"y":346.56666564941406},"ys":{"label":"B"}},{"xs":{"x":280,"y":340.56666564941406},"ys":{"label":"B"}},{"xs":{"x":277,"y":340.56666564941406},"ys":{"label":"B"}},{"xs":{"x":257,"y":292.56666564941406},"ys":{"label":"B"}},{"xs":{"x":257,"y":292.56666564941406},"ys":{"label":"B"}},{"xs":{"x":257,"y":268.56666564941406},"ys":{"label":"B"}},{"xs":{"x":258,"y":266.56666564941406},"ys":{"label":"B"}},{"xs":{"x":261,"y":266.56666564941406},"ys":{"label":"B"}},{"xs":{"x":259,"y":309.56666564941406},"ys":{"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\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/index.html b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/index.html
new file mode 100755
index 00000000..f7728d5b
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/index.html
@@ -0,0 +1,25 @@
+
+
+
+
+ Neural Network
+
+
+
+
+
+
+
+ Neural Network
+
+ Step 1: load data
+ Load Data:
+
+ Step 2: train the model
+ Train Model
+ Step 3: see the label change as you move across the canvas
+ the label is:
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/sketch.js
new file mode 100644
index 00000000..5064daac
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_load_saved_data/sketch.js
@@ -0,0 +1,91 @@
+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Image classification using MobileNet and p5.js
+This example uses a callback pattern to create the classifier
+=== */
+let nn;
+let resultsText;
+function setup() {
+ // Options for Neural Network
+ createCanvas(400, 400);
+ const options = {
+ inputs: ['x', 'y'],
+ outputs: ['label'],
+ task: 'classification',
+ debug: true
+ };
+ // Create Neural Network
+ nn = ml5.neuralNetwork(options);
+
+ // trainModel();
+ resultsText = select('#results');
+
+ // Train the model
+ let trainBtn = select('#trainBtn');
+ // trainBtn.position(10, 50);
+ trainBtn.mousePressed(function () {
+ trainModel();
+ });
+
+ // Save and download the model
+ // let saveBtn = createButton('Save Model');
+ // saveBtn.position(10, 90);
+ // saveBtn.mousePressed(function () {
+ // nn.save();
+ // });
+
+ // // Load the model from local files
+ // let loadLocalBtn = createButton('Load the model from local files');
+ // loadLocalBtn.position(10, 110);
+ // loadLocalBtn.mousePressed(function () {
+ // nn.load('model/model.json', function () {
+ // console.log('Model Loaded!');
+ // });
+ // });
+
+ // Load Data
+ let loadBtn = select('#load');
+ loadBtn.changed(function () {
+ nn.loadData(loadBtn.elt.files, function () {
+ console.log('Data Loaded!');
+ });
+ });
+}
+
+function draw(){
+ background(240);
+}
+
+function trainModel() {
+
+ const trainingOptions = {
+ epochs: 32,
+ batchSize: 12
+ }
+ // Train
+ nn.normalizeData();
+ nn.train(trainingOptions, finishedTraining);
+}
+
+// Training callback
+function finishedTraining() {
+ predict();
+}
+
+function predict() {
+ nn.predict({x:mouseX, y:mouseY}, gotResults);
+}
+
+function gotResults(error, results) {
+ if (error) console.log(error);
+ if (results) {
+ // console.log(results)
+ resultsText.html(`${results[0].label}`)
+ predict();
+ }
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html
new file mode 100755
index 00000000..db0d9c71
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html
@@ -0,0 +1,27 @@
+
+
+
+
+ Neural Network Pixel Prediction
+
+
+
+
+
+
+
+
+ Pixel Prediction
+
+
+
+ Training frequency:
+ add example
+
+
+ train model
+ Frequency prediction:
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/sketch.js
new file mode 100644
index 00000000..f4bff049
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/sketch.js
@@ -0,0 +1,132 @@
+// The brain
+let pixelBrain;
+
+// The video and pixel scale
+let video;
+let ready = false;
+let videoSize = 10;
+
+// For sound synthesis
+let playing = false;
+let frequency;
+let osc;
+
+// Going to normalize the data myself here
+let freqMax = 800;
+
+function setup() {
+ createCanvas(200, 200);
+ // Create the video and set resolution
+ video = createCapture(VIDEO, videoReady);
+ video.size(videoSize, videoSize);
+ video.hide();
+
+ // Inputs are total pixels times 3 (RGB)
+ let totalPixels = videoSize * videoSize * 3;
+ const options = {
+ inputs: totalPixels,
+ outputs: 1,
+ learningRate: 0.01,
+ debug: true,
+ }
+ // Create the model
+ pixelBrain = ml5.neuralNetwork(options);
+
+ // Buttons add trainin data and train model
+ select('#addExample').mousePressed(addExample);
+ select('#train').mousePressed(trainModel);
+}
+
+// Video is ready!
+function videoReady() {
+ ready = true;
+}
+
+function draw() {
+ background(0);
+ if (ready) {
+ // Render the low-res image
+ let w = width / videoSize;
+ video.loadPixels();
+ for (let x = 0; x < video.width; x++) {
+ for (let y = 0; y < video.height; y++) {
+ let index = (x + y * video.width) * 4;
+ let r = video.pixels[index + 0];
+ let g = video.pixels[index + 1];
+ let b = video.pixels[index + 2];
+ noStroke();
+ fill(r, g, b);
+ rect(x * w, y * w, w, w);
+ }
+ }
+ }
+
+}
+
+// Package the pixels as inputs to a neural network
+function getInputs() {
+ video.loadPixels();
+ // Create an array
+ let inputs = [];
+ for (let i = 0; i < video.width * video.height; i++) {
+ let index = i * 4;
+ // Manual normalization
+ inputs.push(video.pixels[index + 0] / 255);
+ inputs.push(video.pixels[index + 1] / 255);
+ inputs.push(video.pixels[index + 2] / 255);
+ }
+ return inputs;
+}
+
+
+// Add an example
+function addExample() {
+ let freq = parseFloat(select('#frequency').value());
+ video.loadPixels();
+ let inputs = getInputs();
+ // Manual normalization of frequency
+ pixelBrain.addData(inputs, [freq / freqMax]);
+}
+
+function trainModel() {
+ // Manually normalizing here!
+ // pixelBrain.normalizeData();
+ pixelBrain.train({ epochs: 50 }, finishedTraining);
+}
+
+// Training is done!
+function finishedTraining() {
+ console.log('done');
+
+ // Start sound
+ osc = new p5.Oscillator();
+ osc.setType('sine');
+ osc.amp(0.5);
+ osc.freq(440);
+ osc.start();
+ osc.amp(0.5);
+
+ // Start predicting
+ predict();
+}
+
+function predict() {
+ let inputs = getInputs();
+ pixelBrain.predict(inputs, gotFrequency);
+}
+
+function gotFrequency(error, results) {
+ if (error) {
+ console.error(error);
+ return;
+ }
+ // Manual "un-normalization"
+ frequency = parseFloat(results[0].value) * freqMax;
+
+ // Display frequency
+ select('#prediction').html(frequency.toFixed(2));
+ // Set frequency
+ osc.freq(parseFloat(frequency));
+ // Predict again
+ predict();
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/index.html b/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/index.html
new file mode 100644
index 00000000..98213dfd
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/index.html
@@ -0,0 +1,18 @@
+
+
+
+
+ Neural network with custom architecture
+
+
+
+
+
+
+
+
+ Neural network with custom architecture
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/sketch.js
new file mode 100644
index 00000000..57aa66d1
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_multiple-layers/sketch.js
@@ -0,0 +1,72 @@
+let nn;
+const nn_options = {
+ inputs: 1,
+ outputs: 1,
+ layers: [
+ ml5.tf.layers.dense({
+ units: 16,
+ inputShape: [1],
+ activation: 'relu',
+ }),
+ ml5.tf.layers.dense({
+ units: 16,
+ inputShape: [1],
+ activation: 'sigmoid',
+ }),
+ ml5.tf.layers.dense({
+ units: 1,
+ activation: 'sigmoid',
+ })
+ ],
+ debug: true
+}
+
+function setup() {
+ createCanvas(400, 400);
+ background(240);
+ nn = ml5.neuralNetwork(nn_options);
+ console.log(nn);
+ createTrainingData();
+
+ nn.normalizeData();
+ const train_options = {
+ epochs: 32
+ }
+ nn.train(train_options, finishedTraining);
+}
+
+function finishedTraining(){
+
+ nn.predict([10], function(err, result){
+ if(err){
+ console.log(err);
+ return
+ }
+ console.log(result)
+ })
+
+ nn.predict([390], function(err, result){
+ if(err){
+ console.log(err);
+ return
+ }
+ console.log(result)
+ })
+}
+
+function createTrainingData(){
+ for(let i = 0; i < 400; i++){
+ if(i%2){
+ const x = floor(random(0, width/2));
+ nn.addData([x], [0])
+ }else {
+ const x = floor(random(width/2, width));
+ nn.addData([x], [1])
+ }
+ }
+
+}
+
+// function draw(){
+
+// }
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_musical_face/index.html b/p5js/NeuralNetwork/NeuralNetwork_musical_face/index.html
new file mode 100755
index 00000000..9ecc0c04
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_face/index.html
@@ -0,0 +1,28 @@
+
+
+
+
+ Neural Network Sound Player
+
+
+
+
+
+
+
+
+ Face Prediction
+
+
+
+ Training frequency: 440
+
+ collect training data
+
+
+ train model
+ Frequency prediction:
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_musical_face/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_musical_face/sketch.js
new file mode 100644
index 00000000..16bd26b1
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_face/sketch.js
@@ -0,0 +1,149 @@
+
+// ml5 Face Detection Model
+let faceapi;
+let detections = [];
+
+// Video
+let video;
+
+// ml5 neural network
+let faceBrain;
+
+// Sound
+let osc;
+let freqMax = 800;
+
+// Keeping track of state
+let trained = false;
+let collecting = false;
+
+function setup() {
+ createCanvas(360, 270);
+
+ // Creat the video and start face tracking
+ video = createCapture(VIDEO);
+ video.size(width, height);
+ // Only need landmarks for this example
+ const faceOptions = { withLandmarks: true, withDescriptors: false };
+ faceapi = ml5.faceApi(video, faceOptions, faceReady);
+
+ // Make the neural network
+ const options = {
+ inputs: 68 * 2,
+ outputs: 1,
+ learningRate: 0.02,
+ debug: true,
+ }
+ faceBrain = ml5.neuralNetwork(options);
+
+ // Buttons
+ select('#collectData').mousePressed(collectData);
+ select('#train').mousePressed(trainModel);
+
+ // Sound
+ osc = new p5.Oscillator();
+ osc.setType('sine');
+}
+
+// Start detecting faces
+function faceReady() {
+ faceapi.detect(gotFaces);
+}
+
+// Got faces
+function gotFaces(error, result) {
+ if (error) {
+ console.log(error);
+ return;
+ }
+ detections = result;
+ faceapi.detect(gotFaces);
+}
+
+// Draw everything
+function draw() {
+ background(0);
+
+ // Just look at the first face and draw all the points
+ if (detections.length > 0) {
+ let points = detections[0].landmarks.positions;
+ for (let i = 0; i < points.length; i++) {
+ stroke(161, 95, 251);
+ strokeWeight(4);
+ point(points[i]._x, points[i]._y);
+ }
+ }
+
+ // If Collecting data
+ if (collecting) {
+ // Get slider frequency value
+ let freq = parseFloat(select('#frequency_slider').value());
+ select('#training_freq').html(freq);
+ osc.freq(freq);
+
+ // Get face inputs
+ let inputs = getInputs();
+ if (inputs) {
+ // Normalize frequency value
+ faceBrain.addData(inputs, [freq]);
+ }
+ }
+}
+
+function getInputs() {
+ // If there is a face, flatten all the positions into
+ // a normalized array.
+ if (detections.length > 0) {
+ let points = detections[0].landmarks.positions;
+ let inputs = [];
+ for (let i = 0; i < points.length; i++) {
+ inputs.push(points[i]._x);
+ inputs.push(points[i]._y);
+ }
+ return inputs;
+ }
+}
+// Start the sound and data collection
+function collectData() {
+ osc.start();
+ osc.amp(5);
+ collecting = true;
+}
+
+// Train the model
+function trainModel() {
+ // No longer collecting dataa
+ collecting = false;
+ osc.amp(0);
+
+ // Normalize face data
+ // (You might manually normalize according to width/height if relative canvas position matters)
+ faceBrain.normalizeData();
+ faceBrain.train({ epochs: 50 }, finishedTraining);
+}
+
+// Training is done!
+function finishedTraining() {
+ console.log('done');
+ osc.amp(0.5);
+ // Start predicting
+ predict();
+}
+
+// Predict a frequency
+function predict() {
+ let inputs = getInputs();
+ faceBrain.predict(inputs, gotFrequency);
+}
+
+// Got a result
+function gotFrequency(error, outputs) {
+ if (error) {
+ console.error(error);
+ return;
+ }
+ frequency = outputs[0].value;
+ osc.freq(frequency);
+ select('#prediction').html(frequency.toFixed(2));
+ predict();
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/index.html b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/index.html
new file mode 100755
index 00000000..42006827
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/index.html
@@ -0,0 +1,31 @@
+
+
+
+
+ Neural Network Musical Mouse
+
+
+
+
+
+
+
+
+ Neural Network Musical Mouse
+
+
+
+ Training frequency:
+ train
+
+
+ Frequency prediction:
+
+
+
+ Click in canvas to add training data.
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/sketch.js
new file mode 100644
index 00000000..23c3cec5
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/sketch.js
@@ -0,0 +1,88 @@
+// The neural network is the brain
+let brain;
+
+// Variables to track p5 oscillator
+let playing = false;
+let frequency;
+let osc;
+
+function setup() {
+ let canvas = createCanvas(400, 400);
+ // Only when clicking on the canvas
+ canvas.mousePressed(addData);
+
+ // Create ethe model
+ const options = {
+ inputs: ['x', 'y'], // TODO: support ['x', 'y']
+ outputs: ['freq'], // TODO: support ['freq]
+ debug: true,
+ }
+ brain = ml5.neuralNetwork(options);
+
+ // Train Model button
+ select('#train').mousePressed(trainModel);
+
+ background(0);
+}
+
+// Add a data record
+function addData() {
+ // Get frequency
+ let target = parseFloat(select('#frequency').value());
+ // TODO: support notePlayer.data.addData({x: mouseX, y: mouseY}, {frequency: target});
+ // Add data
+ brain.addData({x:mouseX, y:mouseY}, {freq:target});
+
+ // Draw circle to visualize training data
+ stroke(255);
+ noFill();
+ ellipse(mouseX, mouseY, 32);
+ fill(255);
+ textSize(16);
+ textAlign(CENTER, CENTER);
+ text(target, mouseX, mouseY);
+}
+
+// Train the model
+function trainModel() {
+ // ml5 will normalize data to a range between 0 and 1 for you.
+ brain.normalizeData();
+ // Train the model
+ // Epochs: one cycle through all the training data
+ brain.train({ epochs: 20 }, finishedTraining);
+}
+
+// When the model is trained
+function finishedTraining() {
+ // Start playing sound
+ osc = new p5.Oscillator();
+ osc.setType('sine');
+ osc.amp(0.5);
+ osc.freq(440);
+ osc.start();
+
+ // Predict a frequency
+ brain.predict([mouseX, mouseY], gotFrequency);
+}
+
+// Got a result
+function gotFrequency(error, outputs) {
+ if (error) {
+ console.error(error);
+ return;
+ }
+
+ // The output comes in an array
+ // There is only one output in this example, but it could be more!
+ frequency = parseFloat(outputs[0].value);
+
+ // Render frequency value
+ select('#prediction').html(frequency.toFixed(2));
+
+ // Adjust oscillator
+ osc.freq(parseFloat(frequency));
+
+ // Predict again
+ brain.predict([mouseX, mouseY], gotFrequency);
+}
+
diff --git a/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/index.html b/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/index.html
new file mode 100755
index 00000000..1fa41f72
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/index.html
@@ -0,0 +1,17 @@
+
+
+
+
+ Neural Network Pose Classifier
+
+
+
+
+
+
+
+
+ Pose Classifier
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/sketch.js
new file mode 100644
index 00000000..6186da82
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_pose_classifier/sketch.js
@@ -0,0 +1,121 @@
+// Video and PoseNet
+let video;
+let poseNet;
+let poses = [];
+
+// Neural Network
+let brain;
+
+// Interface
+let dataButton;
+let dataLabel;
+let trainButton;
+let classificationP;
+
+function setup() {
+ createCanvas(320, 240);
+ video = createCapture(VIDEO);
+ video.size(width, height);
+
+ // Create a new poseNet method with a single detection
+ poseNet = ml5.poseNet(video, modelReady);
+ // This sets up an event that fills the global variable "poses"
+ // with an array every time new poses are detected
+ poseNet.on('pose', function (results) {
+ poses = results;
+ });
+ // Hide the video element, and just show the canvas
+ video.hide();
+
+ classificationP = createP('waiting to train model');
+
+ // The interface
+ dataLabel = createSelect();
+ dataLabel.option('A');
+ dataLabel.option('B');
+
+ dataButton = createButton('add example');
+ dataButton.mousePressed(addExample);
+
+
+ trainButton = createButton('train model');
+ trainButton.mousePressed(trainModel);
+
+ // Create the model
+ let options = {
+ inputs: 34,
+ outputs: 2,
+ task: 'classification',
+ debug: true
+ }
+ brain = ml5.neuralNetwork(options);
+}
+
+// Train the model
+function trainModel() {
+ brain.normalizeData();
+ let options = {
+ epochs: 25
+ }
+ brain.train(options, finishedTraining);
+}
+
+// Begin prediction
+function finishedTraining() {
+ classify();
+}
+
+// Classify
+function classify() {
+ if (poses.length > 0) {
+ let inputs = getInputs();
+ brain.classify(inputs, gotResults);
+ }
+}
+
+function gotResults(error, results) {
+ // Log output
+ // console.log(results);
+ classificationP.html(`${results[0].label} (${floor(results[0].confidence * 100)})%`);
+ // Classify again
+ classify();
+}
+
+function getInputs() {
+ let keypoints = poses[0].pose.keypoints;
+ let inputs = [];
+ for (let i = 0; i < keypoints.length; i++) {
+ inputs.push(keypoints[i].position.x);
+ inputs.push(keypoints[i].position.y);
+ }
+ return inputs;
+}
+
+// Add a training example
+function addExample() {
+ if (poses.length > 0) {
+ let inputs = getInputs();
+ let target = dataLabel.value();
+ brain.addData(inputs, [target]);
+ }
+}
+
+// PoseNet ready
+function modelReady() {
+ console.log('model loaded');
+}
+
+// Draw PoseNet
+function draw() {
+ image(video, 0, 0, width, height);
+ strokeWeight(2);
+ // For one pose only (use a for loop for multiple poses!)
+ if (poses.length > 0) {
+ let pose = poses[0].pose;
+ for (let i = 0; i < pose.keypoints.length; i++) {
+ fill(213, 0, 143);
+ noStroke();
+ ellipse(pose.keypoints[i].position.x, pose.keypoints[i].position.y, 8);
+ }
+ }
+}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_titanic/data/titanic_cleaned.csv b/p5js/NeuralNetwork/NeuralNetwork_titanic/data/titanic_cleaned.csv
new file mode 100644
index 00000000..1ba52ab4
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_titanic/data/titanic_cleaned.csv
@@ -0,0 +1,1309 @@
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+lived,first,male,54,55
+lived,first,female,43,55
+died,first,male,50,56
+lived,first,female,39,56
+lived,third,male,32,56
+lived,third,male,32,56
+lived,third,male,16,56
+lived,third,male,20,56
+lived,third,male,26,56
+died,third,male,25,56
+lived,third,male,26,56
+died,third,male,28,56
+lived,first,male,49,57
+lived,first,female,30,57
+lived,first,male,31,57
+lived,first,female,17,57
+lived,first,male,30,58
+lived,first,female,35,58
+lived,first,female,16,58
+lived,first,female,44,58
+lived,first,female,22,59
+lived,first,female,45,59
+died,first,male,55,59
+lived,first,female,54,59
+died,first,male,24,60
+lived,first,female,18,60
+died,first,male,46,61
+lived,first,female,47,61
+died,first,male,51,61
+lived,first,male,21,61
+lived,first,female,22,62
+died,first,male,65,62
+lived,first,female,63,63
+lived,first,male,23,63
+lived,first,female,45,63
+lived,second,female,24,65
+lived,second,female,24,65
+died,second,male,49,65
+lived,second,female,48,65
+died,second,male,14,65
+died,first,male,29,67
+lived,first,female,22,67
+lived,first,female,24,69
+lived,first,female,24,69
+died,third,male,32,70
+died,third,male,15,70
+died,third,female,27,70
+died,third,female,28,70
+died,third,female,27,70
+died,third,female,35,70
+died,third,male,33,70
+died,third,male,28,70
+died,third,male,29,70
+died,third,male,16,70
+died,third,female,36,70
+died,first,male,70,71
+lived,first,female,36,71
+died,first,male,39,71
+lived,first,female,38,71
+died,second,male,18,74
+died,second,male,17,74
+died,second,male,18,74
+died,second,male,24,74
+died,second,male,32,74
+died,second,male,21,74
+died,second,male,21,74
+died,first,male,36,75
+died,first,male,46,75
+died,first,male,64,75
+lived,first,female,60,75
+lived,first,female,32,76
+lived,first,female,60,76
+lived,first,male,48,77
+lived,first,female,49,77
+lived,first,male,27,77
+died,first,male,54,77
+died,first,male,21,77
+lived,first,female,51,78
+lived,first,female,21,78
+lived,first,female,54,78
+lived,first,female,52,78
+lived,first,female,26,79
+died,first,male,36,79
+lived,first,female,76,79
+lived,first,male,60,79
+lived,first,female,48,79
+died,first,male,24,79
+died,first,male,46,79
+died,first,male,46,79
+lived,first,female,25,79
+lived,first,female,18,80
+died,first,male,52,80
+lived,first,female,39,80
+lived,first,female,38,80
+lived,first,female,62,80
+lived,first,male,53,82
+lived,first,male,4,82
+lived,first,female,54,82
+died,first,male,28,82
+lived,first,female,25,82
+lived,first,male,24,82
+lived,first,female,23,82
+lived,first,female,39,83
+died,first,male,37,83
+lived,first,female,64,83
+lived,first,female,23,83
+lived,first,female,24,83
+lived,first,female,56,83
+died,first,male,45,83
+lived,first,female,35,83
+lived,first,female,30,87
+lived,first,female,16,87
+lived,first,female,33,87
+lived,first,male,49,89
+lived,first,female,43,89
+lived,first,male,38,90
+lived,first,female,35,90
+died,first,male,44,90
+lived,first,female,33,90
+lived,first,female,37,90
+lived,first,male,25,91
+lived,first,female,19,91
+died,first,male,55,94
+lived,first,female,52,94
+died,first,male,23,94
+lived,first,female,30,94
+died,first,male,50,106
+lived,first,female,48,106
+lived,first,female,30,106
+lived,first,female,39,109
+died,first,male,18,109
+lived,first,female,17,109
+lived,first,female,32,111
+died,first,male,49,111
+lived,first,male,17,111
+lived,first,female,39,111
+lived,first,female,31,113
+lived,first,female,23,113
+died,first,male,58,113
+lived,first,male,11,120
+lived,first,female,14,120
+lived,first,male,36,120
+lived,first,female,36,120
+lived,first,male,50,134
+lived,first,female,31,134
+lived,first,female,41,135
+lived,first,male,6,135
+lived,first,male,45,135
+lived,first,female,40,135
+lived,first,female,31,135
+lived,first,female,35,136
+died,first,male,22,136
+lived,first,female,55,136
+lived,first,female,36,136
+died,first,male,27,137
+lived,first,female,26,137
+lived,first,female,58,147
+died,first,male,57,147
+lived,first,female,24,147
+lived,first,female,22,152
+lived,first,female,33,152
+died,first,male,38,153
+lived,first,female,58,153
+lived,first,female,40,153
+lived,first,female,30,165
+lived,first,female,31,165
+died,first,male,57,165
+lived,first,female,45,165
+lived,first,female,39,211
+lived,first,female,15,211
+lived,first,female,43,211
+lived,first,female,35,212
+died,first,male,33,212
+died,first,male,50,212
+died,first,male,27,212
+lived,first,female,50,212
+lived,first,female,29,222
+died,first,male,17,222
+died,first,male,67,222
+died,first,female,63,222
+died,first,male,47,228
+lived,first,female,18,228
+lived,first,female,42,228
+lived,first,female,38,228
+died,first,male,41,228
+died,first,male,24,248
+lived,first,female,50,248
+lived,first,female,27,248
+lived,first,female,45,262
+lived,first,female,36,262
+lived,first,male,13,262
+lived,first,female,18,262
+lived,first,female,21,262
+died,first,male,61,262
+lived,first,female,48,262
+lived,first,female,24,263
+lived,first,female,28,263
+lived,first,female,23,263
+died,first,male,19,263
+died,first,male,64,263
+lived,first,female,60,263
+lived,first,male,36,512
+lived,first,female,58,512
+lived,first,male,35,512
+lived,first,female,35,512
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_titanic/index.html b/p5js/NeuralNetwork/NeuralNetwork_titanic/index.html
new file mode 100644
index 00000000..f5de92d2
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_titanic/index.html
@@ -0,0 +1,31 @@
+
+
+
+
+ Titanic - Neural Network
+
+
+
+
+
+
+
+
+
+
+ Titanic - Neural Network
+
+ age:
+ fare:
+ fare_class:
+ sex:
+
+
+ submit
+
+ training....
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js
new file mode 100644
index 00000000..7661652d
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js
@@ -0,0 +1,61 @@
+let neuralNetwork;
+let submitButton;
+
+function setup() {
+ noCanvas();
+
+ let nnOptions = {
+ dataUrl: 'data/titanic_cleaned.csv',
+ inputs: ['fare_class','sex', 'age', 'fare'],
+ outputs: ['survived'],
+ task: 'classification',
+ debug: true
+ };
+
+ neuralNetwork = ml5.neuralNetwork(nnOptions, modelReady)
+ submitButton = select('#submit');
+ submitButton.mousePressed(classify);
+ submitButton.hide();
+}
+
+function modelReady() {
+ neuralNetwork.normalizeData();
+ neuralNetwork.train({ epochs: 50 }, whileTraining, finishedTraining);
+}
+
+function whileTraining(epoch, logs) {
+ console.log(`Epoch: ${epoch} - loss: ${logs.loss.toFixed(2)}`);
+}
+
+function finishedTraining() {
+ console.log('done!');
+ submitButton.show();
+ classify();
+}
+
+// TODO: normalize and encode values going into predict?
+function classify() {
+ let age = parseInt(select('#age').value());
+ let fare = parseInt(select('#fare').value());
+ let fare_class = select('#fare_class').value();
+ let sex = select('#sex').value();
+
+ // let inputs = {
+ // age: age,
+ // fare: fare,
+ // fare_class: fare_class,
+ // sex: sex
+ // };
+
+ let inputs = [fare_class, sex, age, fare];
+ neuralNetwork.classify(inputs, gotResults);
+}
+
+function gotResults(err, results) {
+ if (err) {
+ console.error(err);
+ } else {
+ console.log(results);
+ select('#result').html(`prediction: ${results[0].label}`);
+ }
+}
diff --git a/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/index.html b/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/index.html
new file mode 100755
index 00000000..d4d186dd
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/index.html
@@ -0,0 +1,34 @@
+
+
+
+
+ XY Classifier
+
+
+
+
+
+
+
+
+ XY Classifier
+
+
+
+ Label:
+ A
+ B
+
+ train
+
+
+ Classification:
+
+
+
+ Click in canvas to add training data.
+
+
+
+
+
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/sketch.js
new file mode 100644
index 00000000..ee17a4b6
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_xy_classifier/sketch.js
@@ -0,0 +1,68 @@
+// The neural network is the brain
+let brain;
+
+function setup() {
+ let canvas = createCanvas(400, 400);
+ // Only when clicking on the canvas
+ canvas.mousePressed(addData);
+
+ // Create ethe model
+ const options = {
+ inputs: ['x', 'y'], // TODO: support ['x', 'y']
+ outputs: ['label'], // TODO: support ['label']
+ debug: true,
+ task: 'classification'
+ }
+ brain = ml5.neuralNetwork(options);
+
+ // Train Model button
+ select('#train').mousePressed(trainModel);
+
+ background(0);
+}
+
+// Add a data record
+function addData() {
+ // Get frequency
+ let label = select('#label').value();
+ // Add data
+ brain.addData({x:mouseX, y:mouseY}, {label});
+
+ // Draw circle to visualize training data
+ stroke(255);
+ noFill();
+ ellipse(mouseX, mouseY, 32);
+ fill(255);
+ textSize(16);
+ textAlign(CENTER, CENTER);
+ text(label, mouseX, mouseY);
+}
+
+// Train the model
+function trainModel() {
+ // ml5 will normalize data to a range between 0 and 1 for you.
+ brain.normalizeData();
+ // Train the model
+ // Epochs: one cycle through all the training data
+ brain.train({ epochs: 20 }, finishedTraining);
+}
+
+// When the model is trained
+function finishedTraining() {
+ brain.classify([mouseX, mouseY], gotResults);
+}
+
+// Got a result
+function gotResults(error, results) {
+ if (error) {
+ console.error(error);
+ return;
+ }
+
+ // Show classification
+ select('#classification').html(results[0].label);
+
+ // Predict again
+ brain.classify([mouseX, mouseY], gotResults);
+}
+