Created
December 12, 2019 15:19
-
-
Save robertleeplummerjr/e70b7d58e4e087ff843b3a0a1ebd6e49 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| const NeuralNetwork = require('./src/neural-network-gpu'); | |
| const NeuralNetworkGPU = require('./src/neural-network-gpu'); | |
| const { FeedForward } = require('./src/feed-forward'); | |
| const { input, feedForward, target } = require('./src/layer'); | |
| const { GPU } = require('gpu.js'); | |
| const { setup } = require('./src/utilities/kernel'); | |
| const { setLogInternalWeights, setLogInternalDeltas, setCheckWeights, setCheckDeltas } = require('./src/layer/debug/strict-watch'); | |
| const xorTrainingData = [ | |
| { input: [0, 1], output: [1] }, | |
| { input: [0, 0], output: [0] }, | |
| { input: [1, 1], output: [0] }, | |
| { input: [1, 0], output: [1] }]; | |
| const fs = require('fs'); | |
| try { | |
| fs.unlinkSync(`cpu-output.log`); | |
| } catch (e) {} | |
| try { | |
| fs.unlinkSync(`gpu-output.log`); | |
| } catch (e) {} | |
| let netType = null; | |
| function demoXOR(net) { | |
| const status = net.train(xorTrainingData, { | |
| iterations: 400, | |
| errorThresh: 0.01, | |
| log: true, | |
| logPeriod: 100, | |
| callbackPeriod: 1, | |
| callback: (v) => { | |
| switch (v.from) { | |
| case 'runInput': | |
| case 'calculateDeltas': | |
| case 'adjustWeights': | |
| fs.appendFileSync(`${netType}-output.log`, 'FROM ' + v.from + '\n' + JSON.stringify(net.toJSON(), null, 2)); | |
| } | |
| } | |
| }); | |
| console.log(status); | |
| console.log(net.run([0,1])); | |
| console.log(net.run([0,0])); | |
| console.log(net.run([1,1])); | |
| console.log(net.run([1,0])); | |
| } | |
| // demoXOR(new NeuralNetwork()); | |
| // demoXOR(new NeuralNetworkGPU()); | |
| const { random } = require('./src/layer/random'); | |
| const { add } = require('./src/layer/add'); | |
| const { multiply } = require('./src/layer/multiply'); | |
| const { sigmoid } = require('./src/layer/sigmoid'); | |
| let layerWeights = null; | |
| function setupWeights() { | |
| layerWeights = [ | |
| [ | |
| [ | |
| -0.05144326761364937, | |
| 0.039105065166950226 | |
| ], | |
| [ | |
| -0.1367069035768509, | |
| 0.14960336685180664 | |
| ], | |
| [ | |
| 0.14404159784317017, | |
| -0.01664450950920581 | |
| ] | |
| ], | |
| [ | |
| [0.0488249845802784], | |
| [-0.13172388076782227], | |
| [0.16559797525405884] | |
| ], | |
| [ | |
| [ | |
| -0.010835444554686546, | |
| 0.0649796798825264, | |
| 0.13607461750507355 | |
| ] | |
| ], | |
| [ | |
| [-0.1149999126791954] | |
| ] | |
| ]; | |
| } | |
| function feedForward2(settings, input) { | |
| const { height } = settings; | |
| const weights = random({ name: 'weights', height, width: input.height }); | |
| weights.weights = layerWeights.shift(); | |
| const biases = random({ name: 'biases', height }); | |
| biases.weights = layerWeights.shift(); | |
| return sigmoid(add(multiply(weights, input), biases)); | |
| } | |
| setupWeights(); | |
| setup(new GPU({ mode: netType = 'cpu' })); | |
| const cpuNet = new FeedForward({ | |
| inputLayer: () => input({ height: 2 }), | |
| hiddenLayers: [ | |
| inputLayer => feedForward2({ height: 3 }, inputLayer), | |
| inputLayer => feedForward2({ height: 1 }, inputLayer), | |
| ], | |
| outputLayer: inputLayer => target({ height: 1 }, inputLayer), | |
| praxisOpts: { | |
| decayRate: 0.99 | |
| } | |
| }); | |
| const cpuInitialize = cpuNet.initialize; | |
| cpuNet.initialize = function() { | |
| setLogInternalWeights(false); | |
| setLogInternalDeltas(false); | |
| cpuInitialize.call(this); | |
| setLogInternalWeights(true); | |
| setLogInternalDeltas(true); | |
| }; | |
| demoXOR(cpuNet); | |
| setLogInternalWeights(false); | |
| setLogInternalDeltas(false); | |
| setupWeights(); | |
| setup(new GPU({ mode: netType = 'gpu' })); | |
| const gpuNet = new FeedForward({ | |
| inputLayer: () => input({ height: 2 }), | |
| hiddenLayers: [ | |
| inputLayer => feedForward2({ height: 3 }, inputLayer), | |
| inputLayer => feedForward2({ height: 1 }, inputLayer), | |
| ], | |
| outputLayer: inputLayer => target({ height: 1 }, inputLayer), | |
| praxisOpts: { | |
| decayRate: 0.99 | |
| } | |
| }); | |
| const gpuInitialize = gpuNet.initialize; | |
| gpuNet.initialize = function() { | |
| gpuInitialize.call(this); | |
| setCheckWeights(checkDeviation); | |
| setCheckDeltas(checkDeviation); | |
| }; | |
| demoXOR(gpuNet); | |
| function checkDeviation(cpuResult, gpuResult) { | |
| const threshold = 0.001; | |
| if (cpuResult[0][0] instanceof Float32Array) { | |
| const z = cpuResult.length; | |
| const y = cpuResult[0].length; | |
| const x = cpuResult[0][0].length; | |
| for (let zIndex = 0; zIndex < z; zIndex++) { | |
| for (let yIndex = 0; yIndex < y; yIndex++) { | |
| for (let xIndex = 0; xIndex < x; xIndex++) { | |
| if (Math.abs(gpuResult[zIndex][yIndex][xIndex] - cpuResult[zIndex][yIndex][xIndex]) >= threshold) { | |
| throw new Error('deviation!'); | |
| } | |
| } | |
| } | |
| } | |
| } else if (cpuResult[0] instanceof Float32Array) { | |
| const y = cpuResult.length; | |
| const x = cpuResult[0].length; | |
| for (let yIndex = 0; yIndex < y; yIndex++) { | |
| for (let xIndex = 0; xIndex < x; xIndex++) { | |
| if (Math.abs(gpuResult[yIndex][xIndex] - cpuResult[yIndex][xIndex]) >= threshold) { | |
| throw new Error('deviation!'); | |
| } | |
| } | |
| } | |
| } else { | |
| const x = cpuResult.length; | |
| for (let xIndex = 0; xIndex < x; xIndex++) { | |
| if (Math.abs(gpuResult[xIndex] - cpuResult[xIndex]) >= threshold) { | |
| throw new Error('deviation!'); | |
| } | |
| } | |
| } | |
| return true; | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment