diff --git a/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html
new file mode 100755
index 00000000..ecce4686
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Classification/index.html
@@ -0,0 +1,19 @@
+
+
+
+
+ Neural Network
+
+
+
+
+
+
+
+ Neural Network Classification
+
+
+
+
+
+
\ 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/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js
index d21a1dad..47f82dd5 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_Simple-Regression/sketch.js
@@ -23,7 +23,7 @@ function setup(){
console.log(nn)
createTrainingData();
- nn.data.normalize();
+ nn.normalizeData();
const trainingOptions={
batchSize: 24,
@@ -39,7 +39,7 @@ async function finishedTraining(){
await Promise.all(
[...new Array(400).fill(null).map( async (item, idx) => {
let results = await nn.predict([idx]);
- let prediction = results.outputs
+ let prediction = results[0]
let x = idx
let y = prediction.value
fill(255, 0, 0);
diff --git a/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js
index cb93aac4..6d29adb9 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_XOR/sketch.js
@@ -15,17 +15,18 @@ function setup() {
inputs: 2,
outputs: 1,
learningRate: 0.25,
+ debug:true
// hiddenUnits: 2
}
model = ml5.neuralNetwork(options);
//model = ml5.neuralNetwork(2, 1);
- model.data.addData([0, 0], [0]);
- model.data.addData([1, 0], [1]);
- model.data.addData([0, 1], [1]);
- model.data.addData([1, 1], [0]);
- model.data.normalize();
- model.train({ epochs: 200 }, whileTraining, finishedTraining);
+ 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);
}
@@ -50,7 +51,11 @@ function finishedTraining() {
}
function gotResults(error, results) {
- console.log(results.values[0]);
+ if(error){
+ console.log(err)
+ return
+ }
+ console.log(results[0].value);
}
function draw() {
diff --git a/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js
index 9a9d0607..3fea81b1 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_basics/sketch.js
@@ -16,8 +16,7 @@ function setup() {
inputs: 3,
outputs: 2,
task: 'regression',
- // activationOutput: 'sigmoid',
- // activationHidden: 'sigmoid'
+ debug:true
};
// Create Neural Network
nn = ml5.neuralNetwork(options);
@@ -96,7 +95,7 @@ function trainModel() {
// output1: training_target[1],
// });
- nn.data.addData(training_input, training_target)
+ nn.addData(training_input, training_target)
}
@@ -105,7 +104,7 @@ function trainModel() {
batchSize: 12
}
// Train
- nn.data.normalize();
+ nn.normalizeData();
nn.train(trainingOptions, finishedTraining);
}
@@ -127,6 +126,7 @@ function predict() {
function gotResults(error, results) {
if (error) console.log(error);
if (results) {
- console.log(results.output);
+ console.log(results);
+ results.tensor.print()
}
}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net-training/data/co2stats.json b/p5js/NeuralNetwork/NeuralNetwork_co2net-training/data/co2stats.json
deleted file mode 100644
index d21bf662..00000000
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net-training/data/co2stats.json
+++ /dev/null
@@ -1,2105 +0,0 @@
-{
- "meta":"https://doi.pangaea.de/10.1594/PANGAEA.884141",
- "data":[
- {
- "val": 1,
- "city_name": "Abington",
- "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": "BrasÃlia",
- "city_name_cdp": "City of BrasÃlia",
- "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",
- "city_name_cdp": "City of Calgary",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 10448332,
- "year_of_emission": 2015,
- "latitude_degrees": 51.05,
- "longitude_degrees": -114.066667,
- "country": "Canada",
- "region": "North America",
- "population_cdp": 1203915,
- "population_year_cdp": 2015
- },
- {
- "val": 54,
- "city_name": "Canberra",
- "city_name_cdp": "Canberra",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 1757500,
- "year_of_emission": 2015,
- "latitude_degrees": -35.307,
- "longitude_degrees": 149.124417,
- "country": "Australia",
- "region": "Oceania",
- "population_cdp": 400000,
- "population_year_cdp": 2016
- },
- {
- "val": 55,
- "city_name": "Cape Town",
- "city_name_cdp": "City of Cape Town",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 9783734,
- "year_of_emission": 2012,
- "latitude_degrees": -33.925,
- "longitude_degrees": 18.423889,
- "country": "South Africa",
- "region": "Africa",
- "population_cdp": 3918830,
- "population_year_cdp": 2014
- },
- {
- "val": 56,
- "city_name": "Caracas",
- "city_name_cdp": "AlcaldÃa Metropolitana de Caracas",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 14364103,
- "year_of_emission": 2015,
- "latitude_degrees": 10.481,
- "longitude_degrees": -66.903611,
- "country": "Venezuela",
- "region": "Latin America & Caribbean",
- "population_cdp": 3518590,
- "population_year_cdp": 2015
- },
- {
- "val": 58,
- "city_name": "Cascais",
- "city_name_cdp": "Cascais",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 565382,
- "year_of_emission": 2010,
- "latitude_degrees": 38.7,
- "longitude_degrees": -9.416667,
- "country": "Portugal",
- "region": "Europe",
- "population_cdp": 208122,
- "population_year_cdp": 2014
- },
- {
- "val": 63,
- "city_name": "Chicago",
- "city_name_cdp": "City of Chicago",
- "reporting_year_cdp": 2017,
- "scope1_ghg_emissions_tons_co2e": 16951471,
- "year_of_emission": 2015,
- "latitude_degrees": 41.837,
- "longitude_degrees": -87.684722,
- "country": "USA",
- "region": "North America",
- "population_cdp": 2720546,
- "population_year_cdp": 2015
- },
- {
- "val": 65,
- "city_name": "Cleveland",
- "city_name_cdp": "City of Cleveland",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 7750563,
- "year_of_emission": 2010,
- "latitude_degrees": 41.482,
- "longitude_degrees": -81.669722,
- "country": "USA",
- "region": "North America",
- "population_cdp": 396815,
- "population_year_cdp": 2010
- },
- {
- "val": 68,
- "city_name": "Columbus",
- "city_name_cdp": "City of Columbus",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 5487400,
- "year_of_emission": 2015,
- "latitude_degrees": 39.983,
- "longitude_degrees": -82.983333,
- "country": "USA",
- "region": "North America",
- "population_cdp": 835957,
- "population_year_cdp": 2014
- },
- {
- "val": 70,
- "city_name": "Curitiba",
- "city_name_cdp": "Municipality of Curitiba",
- "reporting_year_cdp": 2016,
- "scope1_ghg_emissions_tons_co2e": 2686488,
- "year_of_emission": 2013,
- "latitude_degrees": -25.417,
- "longitude_degrees": -49.25,
- "country": "Brazil",
- "region": "Latin America & Caribbean",
- "population_cdp": 1751907,
- "population_year_cdp": 2010
- },
- {
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- "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-training/index.html b/p5js/NeuralNetwork/NeuralNetwork_co2net-training/index.html
deleted file mode 100755
index d3da7309..00000000
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net-training/index.html
+++ /dev/null
@@ -1,69 +0,0 @@
-
-
-
-
- co2Net - Neural Network
-
-
-
-
-
-
-
-
-
- co2Net - Neural Network
- Urban Population vs. CO2 Emissions
- Population and CO2 emissions (in tons of CO2 or CO2 equivalent) are log10 transformed for this analysis.
- data: https://doi.pangaea.de/10.1594/PANGAEA.884141
-
-
-
-
-
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net-training/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_co2net-training/sketch.js
deleted file mode 100644
index 01e6bb7b..00000000
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net-training/sketch.js
+++ /dev/null
@@ -1,101 +0,0 @@
-// 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);
- // 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.data.normalize();
-
- const trainingOptions = {
- epochs: 50,
- batchSize:24
- }
- nn.train(trainingOptions, finishedTraining)
-}
-
-async function finishedTraining(){
- inputMeta = nn.data.meta.inputTypes[0]
- outputMeta = nn.data.meta.outputTypes[0]
-
- nn.data.data.forEach( item => {
- const normx = map(item.xs.population_cdp, inputMeta.min, inputMeta.max, 0, width);
- const normy = map(item.ys.scope1_ghg_emissions_tons_co2e, outputMeta.min, outputMeta.max, 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.outputs.value}
- const x = map(output.x, inputMeta.min, inputMeta.max, 0, width);
- const y = map(output.y, outputMeta.min, outputMeta.max, height, 0);
-
-
- 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_co2net-training/data/co2stats.csv b/p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.csv
similarity index 100%
rename from p5js/NeuralNetwork/NeuralNetwork_co2net-training/data/co2stats.csv
rename to p5js/NeuralNetwork/NeuralNetwork_co2net/data/co2stats.csv
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.json b/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.json
deleted file mode 100644
index 74188fe9..00000000
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.json
+++ /dev/null
@@ -1 +0,0 @@
-{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":1,"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,1],"dtype":"float32"}},{"class_name":"Dense","config":{"units":1,"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_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":[1,1],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[1],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[1,1],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[1],"dtype":"float32"}]}]}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.weights.bin b/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.weights.bin
deleted file mode 100644
index 07b6db8a..00000000
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net/model/model.weights.bin
+++ /dev/null
@@ -1 +0,0 @@
-Öõz@ÙÀ"À_`œ@!@À¿
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js
index 243c8121..02f2e1a1 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_co2net/sketch.js
@@ -10,21 +10,21 @@ This example uses a callback pattern to create the classifier
// x: population_cdp
// y: scope1_ghg_emissions_tons_co2e
=== */
-let neuralNetwork;
+let nn;
let data;
-let predictions = [];
-const margins = 40;
+
+let inputMeta;
+let outputMeta;
// Options for Neural Network
const options = {
- input: 1,
- output: 1,
- // activation: 'sig'
+ inputs: ['population_cdp'],
+ outputs: ['scope1_ghg_emissions_tons_co2e'],
+ dataUrl:'data/co2stats.csv',
+ task:'regression',
+ debug: true
};
-function preload(){
- data = loadJSON('data/co2stats.json')
-}
function setup() {
createCanvas(400, 400);
@@ -32,209 +32,74 @@ function setup() {
background(244, 244, 244);
// Step 1: Create Neural Network
- neuralNetwork = ml5.neuralNetwork(options);
-
- // Step 2: Prepare the data
- prepData()
-
- // Step 3: inspect the data
- inspectData(data.normalized_input, data.normalized_target);
-
- // Step 4A: Inspect the data
- // trainModel()
-
- // Step 4B: Load a pre-trained model
- // visualize the predictions;
- // the population input needs to be log10 transformed
- neuralNetwork.load('model/model.json', function() {
- console.log('model loaded')
-
- for(let i = 500; i < 10000000; i*=2){
- predict(Math.log10(i));
- }
- // vancouver
- predict(Math.log10(603500));
- // NYC
- predict(Math.log10(8537673));
- });
+ nn = ml5.neuralNetwork(options, modelLoaded);
}
-// Inspect the input data
-function inspectData(_x, _y){
- fill(204, 204, 204);
- stroke(0, 27, 68);
- for(let i = 0; i < _x.length; i++){
- const xData = _x[i];
- const yData = _y[i];
- const x = mapToCanvas(xData, margins, width - margins)
- const y = mapToCanvas(yData, height - margins, margins);
- ellipse(x, y, 10, 10)
+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
}
-
- // create axes
- createAxes();
-
+ nn.train(trainingOptions, finishedTraining)
}
-function createAxes(){
- // y
- stroke(0);
- line(margins, margins, margins, height - margins);
- // x
- stroke(0);
- line(margins, height - margins, width - margins, height - margins);
+async function finishedTraining(){
+ inputMin = nn.data.data.inputMin;
+ inputMax = nn.data.data.inputMax;
+ outputMin = nn.data.data.outputMin;
+ outputMax = nn.data.data.outputMax;
- fill(0)
- for(let i = 0.1; i <= 1; i+=0.1){
- push()
- const x = mapToCanvas(i, margins, width - margins)
- const y = height - margins;
- translate(x, y);
- rotate(radians(-90));
- stroke(0);
- line(0,0, 10, 0);
- // rotate(radians(90));
- textAlign(RIGHT)
- const val = nfc(unNormalize(i, data.stats.x_min, data.stats.x_max), 1)
- noStroke();
- text( val, -2, 4)
- pop();
- }
- for(let i = 0.1; i <= 1; i+=0.1){
- push()
- const x = margins;
- const y = mapToCanvas(i, height-margins, margins)
- translate(x, y);
- stroke(0);
- line(0,0, 10, 0);
- // rotate(radians(90));
- textAlign(RIGHT)
- const val = nfc(unNormalize(i, data.stats.y_min, data.stats.y_max), 1)
- noStroke();
- text( val, -2, 4)
- pop();
- }
-}
-
-// Train the model
-function trainModel() {
- // Add training data
- // push x and y values
- for(let i = 0; i < data.training_input.length; i++){
- const xs = data.normalized_input[i];
- const ys = data.normalized_target[i];
- neuralNetwork.addData([xs],[ys]);
- }
- // Train
- neuralNetwork.train(4000, whileTraining);
-}
+ 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);
+ })
-// Training callback
-function whileTraining(error, progress) {
- if (progress.status == 'training') {
- console.log(progress.epoch, progress.loss);
- } else if (progress.status == 'complete') {
- // Run prediction when complete
+ await Promise.all(
+ [
+ 100,
+ 50000,
+ 100000,
+ 500000,
+ 2500000,
+ 5000000,
+ 10000000,
+ 15000000,
+ ].map( (val, idx) => predict(val) )
+ )
- for(let i = 10000; i < 10000000; i*=2){
- predict(Math.log10(i));
- }
- }
-}
-
-function predict(val) {
- let input = [val];
- input = normalizeArray(input, data.stats.x_max, data.stats.x_min)
- neuralNetwork.predict(input, (err, results) => {
- const x = mapToCanvas(input[0], margins, width - margins );
- const y = mapToCanvas(results.output[0], height - margins, margins);
- predictions.push({x, y,});
-
- const xUnorm = Math.pow(10,val);
- const yUnorm = Math.pow(10, unNormalize(results.output[0],data.stats.y_min, data.stats.y_max ))
- console.log(`pop:${xUnorm}, emissions:${yUnorm}`)
-
- displayPredictions(x, y);
-
- });
-}
-
-
-// show the fitting points
-function displayPredictions(_x, _y){
- // show points
- fill(213, 0, 143);
- stroke(255, 128, 204);
- rectMode(CENTER);
- rect(_x, _y, 10, 10)
}
-function showFit(){
- if(predictions.length > 2){
- noFill();
- stroke(255, 0, 0);
- beginShape();
- predictions.forEach(item => {
- vertex(item.x, item.y)
- })
- endShape();
- }
-}
+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)
-// Normalize the array
-function normalizeArray(_arr, _min, _max){
- const output = _arr.map(item => {
- return normalize(item, _max, _min)
- })
-
- return 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)
-// Normalize value
-function normalize(_item, _min, _max){
- return (_item - _min) / (_max - _min)
+
}
-// UnNormalize the value
-function unNormalize(_item, _min, _max){
- return (_item * (_max - _min)) + _min;
-}
-
-// Translate normalized data to canvas
-function mapToCanvas(_val, _min, _max){
- return map(_val, 0, 1, _min, _max);
-}
-
-
-// Prepare the data
-function prepData(){
- // get the data array
- const stats = data.data;
-
- // store the x and y stats
- data.stats = {
- x_max:null,
- x_min:null,
- y_max:null,
- y_min:null
- }
-
- // step 1: add data to the training input
- data.training_input = stats.map(item => Math.log10(item.population_cdp))
- data.training_target = stats.map(item => Math.log10(item.scope1_ghg_emissions_tons_co2e))
-
- // step 2: get the min and max for x and y
- data.stats.x_max = max(data.training_input)
- data.stats.x_min = min(data.training_input)
- data.stats.y_max = max(data.training_target)
- data.stats.y_min = min(data.training_target)
-
- // step 3: get the normalized values
- data.normalized_input = normalizeArray(data.training_input, data.stats.x_max, data.stats.x_min)
- data.normalized_target = normalizeArray(data.training_target, data.stats.y_max, data.stats.y_min)
-}
\ No newline at end of file
diff --git a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js
index 591ecc1f..988beac4 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_color_classifier/sketch.js
@@ -28,7 +28,7 @@ function setup() {
}
function modelReady() {
- neuralNetwork.data.normalize();
+ neuralNetwork.normalizeData();
const trainingOptions = {
epochs: 20,
batchSize: 64
diff --git a/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html
new file mode 100755
index 00000000..c76027ae
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/index.html
@@ -0,0 +1,27 @@
+
+
+
+
+ Neural Network Sound Player
+
+
+
+
+
+
+
+
+ Pixel Prediction
+
+
+
+ Training frequency:
+
+
+
+
+ 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..24288100
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_lowres_pixels/sketch.js
@@ -0,0 +1,119 @@
+
+
+let pixelBrain;
+let video;
+let ready = false;
+let w;
+let playing = false;
+let frequency;
+let osc;
+
+function setup() {
+ createCanvas(200, 200);
+ video = createCapture(VIDEO, videoReady);
+ let res = 10;
+ video.size(res, res);
+ video.hide();
+ let totalPixels = res * res * 3;
+ const options = {
+ inputs: totalPixels,
+ outputs: 1,
+ hiddenUnits: floor(totalPixels / 2),
+ normalizationOptions: {
+ inputMin: [...new Array(totalPixels).fill(0)],
+ inputMax: [...new Array(totalPixels).fill(255)]
+ },
+ // activationHidden: 'relu',
+ learningRate: 0.01,
+ debug: true,
+ }
+ pixelBrain = ml5.neuralNetwork(options);
+ select('#addExample').mousePressed(addExample);
+ select('#train').mousePressed(trainModel);
+ w = width / res;
+ osc = new p5.Oscillator();
+ osc.setType('sine');
+ osc.amp(0.5);
+ osc.freq(440);
+}
+
+function videoReady() {
+ ready = true;
+}
+
+function draw() {
+ background(0);
+ if (ready) {
+ 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);
+ }
+ }
+ }
+
+}
+
+function getInputs() {
+ video.loadPixels();
+ let inputs = [];
+ for (let i = 0; i < video.width * video.height; i++) {
+ let index = i * 4;
+ inputs.push(video.pixels[index + 0]);
+ inputs.push(video.pixels[index + 1]);
+ inputs.push(video.pixels[index + 2]);
+ }
+ return inputs;
+}
+
+let firstTime = true;
+function addExample() {
+ if (firstTime) {
+ osc.start();
+ firstTime = false;
+ }
+
+ let freq = select('#frequency').value();
+ osc.freq(parseFloat(freq));
+ video.loadPixels();
+ let inputs = getInputs();
+ pixelBrain.addData(inputs, [parseFloat(freq)]);
+}
+
+function trainModel() {
+ osc.amp(0);
+ pixelBrain.normalizeData();
+ const trainingOptions = {
+ epochs: 50
+ }
+ pixelBrain.train(trainingOptions, finishedTraining);
+}
+
+function finishedTraining() {
+ console.log('done');
+ osc.amp(0.5);
+ predict();
+}
+
+function predict() {
+ let inputs = getInputs();
+ pixelBrain.predict(inputs, gotFrequency);
+}
+
+function gotFrequency(error, results) {
+ if (error) {
+ console.error(error);
+ } else {
+ frequency = parseFloat(results[0].value);
+ select('#prediction').html(frequency.toFixed(2));
+ osc.freq(parseFloat(frequency));
+ predict();
+ }
+}
+
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_mouse/index.html b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/index.html
new file mode 100755
index 00000000..1e3de00a
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/index.html
@@ -0,0 +1,31 @@
+
+
+
+
+ Neural Network Sound Player
+
+
+
+
+
+
+
+
+ Neural Network Sound Player
+
+
+
+ Training frequency:
+
+
+
+ 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..d25b3cf6
--- /dev/null
+++ b/p5js/NeuralNetwork/NeuralNetwork_musical_mouse/sketch.js
@@ -0,0 +1,62 @@
+let notePlayer;
+let playing = false;
+let frequency;
+let osc;
+
+function setup() {
+ createCanvas(400, 400).mousePressed(addData);;
+ const options = {
+ inputs: 2, // what about allowing ['x', 'y']?
+ outputs: 1, // what about allowing ['x', 'y']?
+ debug: true,
+ }
+ background(0);
+ notePlayer = ml5.neuralNetwork(options);
+ select('#train').mousePressed(trainModel);
+}
+
+function addData() {
+
+ let freq = select('#frequency').value();
+ stroke(255);
+ noFill();
+ ellipse(mouseX, mouseY, 32);
+ fill(255);
+ textSize(16);
+ console.log(freq);
+ textAlign(CENTER, CENTER);
+ text(freq, mouseX, mouseY);
+ notePlayer.data.addData([mouseX, mouseY], [parseFloat(freq)]);
+}
+
+function trainModel() {
+ notePlayer.normalizeData();
+ const trainingOptions = {
+ batchSize: 24,
+ epochs: 20
+ }
+ notePlayer.train(trainingOptions, finishedTraining);
+}
+
+function finishedTraining() {
+ console.log('done');
+ osc = new p5.Oscillator();
+ osc.setType('sine');
+ osc.amp(0.5);
+ osc.freq(440);
+ osc.start();
+ notePlayer.predict([mouseX, mouseY], gotFrequency);
+}
+
+function gotFrequency(error, results) {
+ if (error) {
+ console.error(error);
+ } else {
+ frequency = parseFloat(results[0].value);
+ console.log(results);
+ select('#prediction').html(frequency.toFixed(2));
+ osc.freq(parseFloat(frequency));
+ notePlayer.predict([mouseX, mouseY], gotFrequency);
+ }
+}
+
diff --git a/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js b/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js
index 075a121a..7661652d 100644
--- a/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js
+++ b/p5js/NeuralNetwork/NeuralNetwork_titanic/sketch.js
@@ -6,7 +6,7 @@ function setup() {
let nnOptions = {
dataUrl: 'data/titanic_cleaned.csv',
- inputs: ['fare_class', 'sex', 'age', 'fare'],
+ inputs: ['fare_class','sex', 'age', 'fare'],
outputs: ['survived'],
task: 'classification',
debug: true
@@ -19,7 +19,7 @@ function setup() {
}
function modelReady() {
- neuralNetwork.data.normalize();
+ neuralNetwork.normalizeData();
neuralNetwork.train({ epochs: 50 }, whileTraining, finishedTraining);
}