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add training set and tests

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This commit is contained in:
TreetopFlyer 2021-07-28 14:08:46 -04:00
parent 0d06d9e158
commit 329c5f5f81
2 changed files with 77 additions and 13 deletions
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67
nn.test.js
View File

@ -1,14 +1,61 @@
import { assert, assertEquals } from "https://deno.land/std@0.102.0/testing/asserts.ts";
import * as NN from "./nn.ts";
import { Label, Forward, Backward } from "./nn.ts";
import { default as M } from "./m.ts";
Deno.test("NN.Observe", ()=>
{
console.log(NN.Observe([[[1, 2, 3]]], [[[0.4, 0.5, 0.6]]]));
});
Deno.test("NN.Train", ()=>
{
const stages = [[[1, 2, 3]]];
const layers = [[[0.4, 0.5, 0.6]]];
const input = [
[ 0.1, 0.05],
[ 0.0, -0.06]
[ 0.99, 0.85],
[ 1.2, 1.05]
];
console.log(NN.Train(stages, layers, [[0, 0, 1]], 0.1));
const training = [];
const stages = [];
const layers = [];
Deno.test("NN.Label", ()=>
{
Label(training,
[
[ 0.1, 0.05],
[ 0.0, -0.06]
],
[1]);
Label(training,
[
[ 0.99, 0.85],
[ 1.2, 1.05]
],
[0]);
stages.push(training[0]);
console.log(training);
assertEquals(training.length, 2, "input and output sets created");
assertEquals(training[0].length, training[1].length, "both sets have same length");
assertEquals(training[0][0].length, 3, "padded input component");
assertEquals(training[1][0].length, 1, "unchanged label vector");
});
Deno.test("NN.Backward", ()=>
{
layers.push(M.Create.Box([0, 0, 0], [1, 1, 1], 4));
layers.push(M.Create.Box([0, 0, 0, 0, 0], [1, 1, 1, 1, 1], 1));
let copy1 = M.Create.Clone(layers[0]);
let copy2 = M.Create.Clone(layers[1]);
for(let i=0; i<1000; i++)
{
Backward(stages, layers, training[1], 0.1);
}
assert(layers[0][0][0] != copy1[0][0][0], "first matrix has changed");
assert(layers[1][0][0] != copy2[0][0][0], "second matrix has changed");
});
Deno.test("NN.Forward", ()=>
{
console.log(Forward(stages, layers));
console.log(training[1]);
});

27
nn.ts
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@ -1,6 +1,22 @@
import { default as M, Cloud } from "./m.ts";
export type N = Array<Array<Array<number>>>
const Observe = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>):Cloud.M =>
const Label = (inSet:any, inData:Cloud.M, inLabel:Cloud.V):N =>
{
if(!inSet){inSet = [[], []];}
if(inSet.length == 0){inSet.push([]);}
if(inSet.length == 1){inSet.push([]);}
inData.forEach((row:Cloud.V) =>
{
row.push(1);
inSet[0].push(row);
inSet[1].push(inLabel);
});
return inSet;
};
const Forward = (inStages:N, inLayers:N):Cloud.M =>
{
let i:number;
let process = (index:number):Cloud.M => M.Batch.Sigmoid(M.Batch.Affine(inStages[index], inLayers[index]));
@ -12,16 +28,17 @@ const Observe = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>):Cloud.M =>
inStages[i+1] = process(i);
return inStages[i+1];
};
const Train = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>, inGoals:Cloud.M, inRate:number):void =>
const Backward = (inStages:N, inLayers:N, inGoals:Cloud.M, inRate:number):void =>
{
let i:number;
let errorBack:Cloud.M = M.Batch.Subtract(Observe(inStages, inLayers), inGoals);
let errorBack:Cloud.M = M.Batch.Subtract(Forward(inStages, inLayers), inGoals);
for(i=inLayers.length-1; i>=0; i++)
for(i=inLayers.length-1; i>=0; i--)
{
let layerMatrix:Cloud.M = inLayers[i];
let layerInput:Cloud.M = inStages[i];
let layerOutput:Cloud.M = inStages[i+1];
let errorScaled:Cloud.M = M.Batch.Multiply(errorBack, M.Batch.Derivative(layerOutput));
errorBack = M.Batch.Affine(errorScaled, M.Create.Transpose(layerMatrix));
@ -32,5 +49,5 @@ const Train = (inStages:Array<Cloud.M>, inLayers:Array<Cloud.M>, inGoals:Cloud.M
}
};
export { Observe, Train };
export { Label, Forward, Backward };
export type { Cloud };