network tests passing
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nn.test.js
135
nn.test.js
@ -1,113 +1,52 @@
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import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
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import { Split, Forward, Backward } from "./nn.ts";
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import { default as M } from "./m.ts";
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import { default as Methods } from "./m.ts";
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import { Split, Build, Label, Learn, Check } from "./nn.ts";
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let training = [];
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let stages = [];
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let data = [
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[ 0.10, 0.05, 0, 1],
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[ 0.00, -0.06, 0, 1],
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[ 0.99, 0.85, 1, 0],
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[ 1.20, 1.05, 1, 0]
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];
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let columns = [2, 3];
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let input, output;
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let layers = [];
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let typeA = [
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[ 0.1, 0.05],
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[ 0.0, -0.06]
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];
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let typeB = [
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[ 0.99, 0.85],
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[ 1.2, 1.05]
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];
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Deno.test("check forward/backward", ()=>
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{
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let matrix1 = [
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[-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
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[-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
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[0.738181366836224, 0.3389203747353555, 0.4920200816404332]
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];
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let matrix2 = [
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[0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
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[-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
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];
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let input = [
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[ 0.1, 0.05],
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[ 0.0, -0.06],
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[ 0.99, 0.85],
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[ 1.2, 1.05]
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];
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let output = [
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[1, 0],
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[1, 0],
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[0, 1],
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[0, 1]
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];
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let layers = [matrix1, matrix2];
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let stages = [];
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for(let i=0; i<1000; i++)
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{
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stages = Forward(Methods.Mutate.Pad(input), layers);
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Backward(stages, layers, output, 0.1);
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}
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stages = Forward(input, layers);
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console.log(stages[stages.length-1]);
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});
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Deno.test("NN.Split", ()=>
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{
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let data = [
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[3, 2, 1, 0, 1],
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[6, 5, 4, 1, 0]
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]
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let split = Split(data, [3, 4]);
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console.log(split);
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[input, output] = Split(data, columns);
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assert(input);
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assert(output);
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assertEquals(input.length, output.length, "data split into equal input and output");
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assertEquals(input[0].length, 3, "padded input");
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assertEquals(output[0].length, 2, "unpadded output");
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});
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/*
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Deno.test("NN.Label", ()=>
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Deno.test("NN.Build", ()=>
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{
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Label(training, typeA, [1, 0]);
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Label(training, typeB, [0, 1]);
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assertEquals(training.length, 2, "input and output sets created");
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assertEquals(training[0].length, training[1].length, "both sets have same length");
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assertEquals(training[0][0].length, 3, "padded input component");
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assertEquals(training[1][0].length, 2, "unchanged label vector");
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layers = Build(2, 5, 2);
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assertEquals(layers.length, 2, "correct number of matrices");
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assertEquals(layers[0][0].length, input[0].length, "input: padded input");
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assertEquals(layers[0].length, 5, "input: unpadded output");
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assertEquals(layers[1][0].length, 6, "hidden: padded input");
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assertEquals(layers[1].length, output[0].length, "hidden: unpadded output");
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});
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Deno.test("NN.Forward", ()=>
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{
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let layer1 = M.Create.Box([-1, -1, -1], [1, 1, 1], 2);
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let layer2 = M.Create.Box([-1, -1, -1], [1, 1, 1], 1);
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layers.push(layer1);
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layers.push(layer2);
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console.log(training[0]);
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stages = Forward(training[0], layers);
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console.log(stages);
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});
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Deno.test("NN.Backward", ()=>
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{
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let copy1 = M.Create.Clone(layers[0]);
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let copy2 = M.Create.Clone(layers[1]);
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for(let i=0; i<100; i++)
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{
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Backward(stages, layers, training[1], 0.1);
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}
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assert(layers[0][0][0] != copy1[0][0], "first matrix has changed");
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assert(layers[1][0][0] != copy2[0][0], "second matrix has changed");
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});
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Deno.test("NN.Label", ()=>
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{
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let stages = Forward(training[0], layers);
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console.log(stages[stages.length-1]);
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let labels = Label(input, layers);
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assertEquals(labels.length, output.length);
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assertEquals(labels[0].length, output[0].length);
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});
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*/
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Deno.test("NN.Learn", ()=>
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{
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let error = Learn(input, layers, output, 1000, 0.1);
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assertEquals(error.length, output.length);
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let total = 0;
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let count = error.length*error[0].length;
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error.forEach(row=> row.forEach(component=> total+=Math.abs(component)));
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assert(total/count < 0.3);
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});
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14
nn.ts
14
nn.ts
@ -38,10 +38,12 @@ const Split = (inTrainingSet:Cloud.M, inHeaderLabel:Cloud.V, inHeaderKeep:Cloud.
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{
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inTrainingSet[0].forEach( (item:number, index:number)=> inHeaderLabel.includes(index) ? false : inHeaderKeep.push(index) );
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}
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inTrainingSet.forEach((row:Cloud.V) =>
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inTrainingSet.forEach((row:Cloud.V):void =>
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{
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data.push( [...inHeaderKeep.map((i:number)=>row[i]), 1] );
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label.push( inHeaderLabel.map((i:number)=>row[i]) );
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let vectorData = [ ...inHeaderKeep.map((i:number)=>row[i]), 1];
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let vectorLabel = inHeaderLabel.map((i:number)=>row[i])
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data.push( vectorData );
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label.push( vectorLabel );
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});
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return [ data, label ];
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};
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@ -54,7 +56,6 @@ const Build = (...inLayers:Array<number>):N =>
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{
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output.push(rand( inLayers[i]+1, inLayers[i+1]));
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}
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output.push( rand( inLayers[i-1], inLayers[i]) );
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return output;
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};
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const Label = (inData:Cloud.M, inLayers:N):Cloud.M =>
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@ -72,7 +73,6 @@ const Learn = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M, inIterations:number
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}
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return M.Batch.Subtract(stages[stages.length-1], inLabels);
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};
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const Error = M.Batch.Subtract;
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const Check = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M):Cloud.M => Learn(inData, inLayers, inLabels, 1, 0);
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export { Split, Build, Label, Learn, Error, Forward, Backward };
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export type { Cloud };
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export { Split, Build, Label, Learn, Check, Forward, Backward };
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