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deeplearn 15 #2

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96 changes: 52 additions & 44 deletions package-lock.json
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2 changes: 1 addition & 1 deletion package.json
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
"version": "0.1.0",
"private": true,
"dependencies": {
"deeplearn": "0.3.12",
"deeplearn": "0.3.15",
"react": "^16.1.1",
"react-dom": "^16.1.1",
"react-scripts": "1.0.17"
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44 changes: 25 additions & 19 deletions src/neuralNetwork.js
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Expand Up @@ -4,14 +4,15 @@ import {
Graph,
Session,
SGDOptimizer,
NDArrayMathGPU,
ENV,
NDArrayMath,
CostReduction,
} from 'deeplearn';

// Encapsulates math operations on the CPU and GPU.
const math = new NDArrayMathGPU();

class ColorAccessibilityModel {
// Encapsulates math operations on the CPU and GPU.
math = ENV.math;

// Runs training.
session;

Expand Down Expand Up @@ -47,27 +48,32 @@ class ColorAccessibilityModel {
this.predictionTensor = this.createFullyConnectedLayer(graph, fullyConnectedLayer, 3, 2);
this.costTensor = graph.meanSquaredCost(this.targetTensor, this.predictionTensor);

this.session = new Session(graph, math);
this.session = new Session(graph, this.math);

this.prepareTrainingSet(trainingSet);
}

prepareTrainingSet(trainingSet) {
math.scope(() => {
const { rawInputs, rawTargets } = trainingSet;
const oldMath = ENV.math;
const safeMode = false;
const math = new NDArrayMath('cpu', safeMode);
ENV.setMath(math);

const inputArray = rawInputs.map(v => Array1D.new(this.normalizeColor(v)));
const targetArray = rawTargets.map(v => Array1D.new(v));
const { rawInputs, rawTargets } = trainingSet;

const shuffledInputProviderBuilder = new InCPUMemoryShuffledInputProviderBuilder([ inputArray, targetArray ]);
const [ inputProvider, targetProvider ] = shuffledInputProviderBuilder.getInputProviders();
const inputArray = rawInputs.map(v => Array1D.new(this.normalizeColor(v)));
const targetArray = rawTargets.map(v => Array1D.new(v));

// Maps tensors to InputProviders.
this.feedEntries = [
{ tensor: this.inputTensor, data: inputProvider },
{ tensor: this.targetTensor, data: targetProvider },
];
});
const shuffledInputProviderBuilder = new InCPUMemoryShuffledInputProviderBuilder([ inputArray, targetArray ]);
const [ inputProvider, targetProvider ] = shuffledInputProviderBuilder.getInputProviders();

// Maps tensors to InputProviders.
this.feedEntries = [
{ tensor: this.inputTensor, data: inputProvider },
{ tensor: this.targetTensor, data: targetProvider },
];

ENV.setMath(oldMath);
}

train(step, computeCost) {
Expand All @@ -77,7 +83,7 @@ class ColorAccessibilityModel {

// Train one batch.
let costValue;
math.scope(() => {
this.math.scope(() => {
const cost = this.session.train(
this.costTensor,
this.feedEntries,
Expand All @@ -98,7 +104,7 @@ class ColorAccessibilityModel {
predict(rgb) {
let classifier = [];

math.scope(() => {
this.math.scope(() => {
const mapping = [{
tensor: this.inputTensor,
data: Array1D.new(this.normalizeColor(rgb)),
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