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SethTrowbridge merged 13 commits from network into master 2021-07-29 16:16:32 -04:00
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135
nn.test.js
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@ -1,113 +1,52 @@
import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts"; import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
import { Split, Forward, Backward } from "./nn.ts"; import { Split, Build, Label, Learn, Check } from "./nn.ts";
import { default as M } from "./m.ts";
import { default as Methods } from "./m.ts";
let training = []; let data = [
let stages = []; [ 0.10, 0.05, 0, 1],
[ 0.00, -0.06, 0, 1],
[ 0.99, 0.85, 1, 0],
[ 1.20, 1.05, 1, 0]
];
let columns = [2, 3];
let input, output;
let layers = []; let layers = [];
let typeA = [
[ 0.1, 0.05],
[ 0.0, -0.06]
];
let typeB = [
[ 0.99, 0.85],
[ 1.2, 1.05]
];
Deno.test("check forward/backward", ()=>
{
let matrix1 = [
[-0.43662948305036675, -0.368590640707799, -0.23227179558890843],
[-0.004292653969505622, 0.38670055222186317, -0.2478421495365568],
[0.738181366836224, 0.3389203747353555, 0.4920200816404332]
];
let matrix2 = [
[0.7098703863463034, 0.35485944251238033, 0.7642849892333241, 0.03046174288491077],
[-0.30655426258144347, 0.45509633551425077, -0.5013795222004322, -0.3421292736637427]
];
let input = [
[ 0.1, 0.05],
[ 0.0, -0.06],
[ 0.99, 0.85],
[ 1.2, 1.05]
];
let output = [
[1, 0],
[1, 0],
[0, 1],
[0, 1]
];
let layers = [matrix1, matrix2];
let stages = [];
for(let i=0; i<1000; i++)
{
stages = Forward(Methods.Mutate.Pad(input), layers);
Backward(stages, layers, output, 0.1);
}
stages = Forward(input, layers);
console.log(stages[stages.length-1]);
});
Deno.test("NN.Split", ()=> Deno.test("NN.Split", ()=>
{ {
let data = [ [input, output] = Split(data, columns);
[3, 2, 1, 0, 1], assert(input);
[6, 5, 4, 1, 0] assert(output);
] assertEquals(input.length, output.length, "data split into equal input and output");
let split = Split(data, [3, 4]);
console.log(split); assertEquals(input[0].length, 3, "padded input");
assertEquals(output[0].length, 2, "unpadded output");
}); });
Deno.test("NN.Build", ()=>
/*
Deno.test("NN.Label", ()=>
{ {
Label(training, typeA, [1, 0]); layers = Build(2, 5, 2);
Label(training, typeB, [0, 1]);
assertEquals(training.length, 2, "input and output sets created"); assertEquals(layers.length, 2, "correct number of matrices");
assertEquals(training[0].length, training[1].length, "both sets have same length"); assertEquals(layers[0][0].length, input[0].length, "input: padded input");
assertEquals(training[0][0].length, 3, "padded input component"); assertEquals(layers[0].length, 5, "input: unpadded output");
assertEquals(training[1][0].length, 2, "unchanged label vector");
assertEquals(layers[1][0].length, 6, "hidden: padded input");
assertEquals(layers[1].length, output[0].length, "hidden: unpadded output");
}); });
Deno.test("NN.Forward", ()=>
{
let layer1 = M.Create.Box([-1, -1, -1], [1, 1, 1], 2);
let layer2 = M.Create.Box([-1, -1, -1], [1, 1, 1], 1);
layers.push(layer1);
layers.push(layer2);
console.log(training[0]);
stages = Forward(training[0], layers);
console.log(stages);
});
Deno.test("NN.Backward", ()=>
{
let copy1 = M.Create.Clone(layers[0]);
let copy2 = M.Create.Clone(layers[1]);
for(let i=0; i<100; i++)
{
Backward(stages, layers, training[1], 0.1);
}
assert(layers[0][0][0] != copy1[0][0], "first matrix has changed");
assert(layers[1][0][0] != copy2[0][0], "second matrix has changed");
});
Deno.test("NN.Label", ()=> Deno.test("NN.Label", ()=>
{ {
let stages = Forward(training[0], layers); let labels = Label(input, layers);
console.log(stages[stages.length-1]); assertEquals(labels.length, output.length);
assertEquals(labels[0].length, output[0].length);
}); });
*/ Deno.test("NN.Learn", ()=>
{
let error = Learn(input, layers, output, 1000, 0.1);
assertEquals(error.length, output.length);
let total = 0;
let count = error.length*error[0].length;
error.forEach(row=> row.forEach(component=> total+=Math.abs(component)));
assert(total/count < 0.3);
});

14
nn.ts
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@ -38,10 +38,12 @@ const Split = (inTrainingSet:Cloud.M, inHeaderLabel:Cloud.V, inHeaderKeep:Cloud.
{ {
inTrainingSet[0].forEach( (item:number, index:number)=> inHeaderLabel.includes(index) ? false : inHeaderKeep.push(index) ); inTrainingSet[0].forEach( (item:number, index:number)=> inHeaderLabel.includes(index) ? false : inHeaderKeep.push(index) );
} }
inTrainingSet.forEach((row:Cloud.V) => inTrainingSet.forEach((row:Cloud.V):void =>
{ {
data.push( [...inHeaderKeep.map((i:number)=>row[i]), 1] ); let vectorData = [ ...inHeaderKeep.map((i:number)=>row[i]), 1];
label.push( inHeaderLabel.map((i:number)=>row[i]) ); let vectorLabel = inHeaderLabel.map((i:number)=>row[i])
data.push( vectorData );
label.push( vectorLabel );
}); });
return [ data, label ]; return [ data, label ];
}; };
@ -54,7 +56,6 @@ const Build = (...inLayers:Array<number>):N =>
{ {
output.push(rand( inLayers[i]+1, inLayers[i+1])); output.push(rand( inLayers[i]+1, inLayers[i+1]));
} }
output.push( rand( inLayers[i-1], inLayers[i]) );
return output; return output;
}; };
const Label = (inData:Cloud.M, inLayers:N):Cloud.M => const Label = (inData:Cloud.M, inLayers:N):Cloud.M =>
@ -72,7 +73,6 @@ const Learn = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M, inIterations:number
} }
return M.Batch.Subtract(stages[stages.length-1], inLabels); return M.Batch.Subtract(stages[stages.length-1], inLabels);
}; };
const Error = M.Batch.Subtract; const Check = (inData:Cloud.M, inLayers:N, inLabels:Cloud.M):Cloud.M => Learn(inData, inLayers, inLabels, 1, 0);
export { Split, Build, Label, Learn, Error, Forward, Backward }; export { Split, Build, Label, Learn, Check, Forward, Backward };
export type { Cloud };