Neural Networks for javaScript; a self-contained library
Written for general use in web-based aplications.
This is the openSource version.
###Example 1
We construct a 5-layers deep neural network, outputing a numeric 3-vector, and train it for a 1000 steps.
//generate Random data for demo
//input -> 5 x 50 matrix, 1x50 is one case
var input=getRandomBatchIn(5,50)
//output -> 5x3 matrix, 1x3 is one case
var output=getRandomBatchOut(5,3,{mean:10,variance:5});
var data={in:input,out:output};
//declare the net
var n=new net();
//specify its' structure
n.add({shape:[1,50],type:"input"});
n.add({shape:[50,10],type:"fc",activation:"sigmoid"});
n.add({shape:[10,10],type:"fc",activation:"reLu"});
n.add({shape:[10,10],type:"fc",activation:"sigmoid"});
n.add({shape:[10,10],type:"fc",activation:"sigmoid"});
n.add({shape:[10,3],type:"fc",activation:"id"});
//initialize neurons (uses variance normalization by layer length)
n.init(gaussian(0, 1));
//train for 1000 steps
for(var k=0;k<1000;k++) n.trainStep({data:data,cost:"meanSquare",stepSize:0.1});
//evaluate on all 5 cases
var prediction=[];
for(k in input){
prediction[k]=n.eval(input[k]);
forAll(prediction[k],function(x){return x.toFixed(2)});
}
//display results
console.log("Correct predictions");
console.log(output);
console.log("\nPredictions:");
console.log(prediction);###Example 2
We construct a 5-layers deep neural network, outputing a 3-class classification probability, and train it for a 1000 steps.
//generate Random data for demo
//input -> 5 x 50 matrix, 1x50 is one case
var input=getRandomBatchIn(5,50)
//output -> 5x3 matrix, 1x3 is one case
var output=getRandomClassBatch(5,3);
var data={in:input,out:output};
//declare the net
var n=new net();
//specify its' structure
n.add({shape:[1,50],type:"input"});
n.add({shape:[50,10],type:"fc",activation:"sigmoid"});
n.add({shape:[10,10],type:"fc",activation:"reLu"});
n.add({shape:[10,10],type:"fc",activation:"sigmoid"});
n.add({shape:[10,10],type:"fc",activation:"reLu"});
n.add({shape:[10,3],type:"fc",activation:"sigmoid"});
//initialize neurons (uses variance normalization by layer length)
n.init(gaussian(0, 1));
//train for 1000 steps
for(var k=0;k<1000;k++) n.trainStep({data:data,cost:"entropyBinary",stepSize:0.1});
//evaluate on all data
var classification=[];
for(k in input){
classification[k]=n.eval(input[k]);
forAll(classification[k],function(x){return x.toFixed(2)});
}
//display results
console.log("Correct predictions");
console.log(output);
console.log("\nPredictions:");
console.log(classification);
nNetJs by Žiga Sajovic is licensed under a Creative Commons Attribution 4.0 International License.
