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allow on-the-fly regularization changes #139

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allow on-the-fly regularization changes #139

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17 changes: 7 additions & 10 deletions src/nn.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ export class Activations {
};
}

/** Build-in regularization functions */
/** Built-in regularization functions */
export class RegularizationFunction {
public static L1: RegularizationFunction = {
output: w => Math.abs(w),
Expand Down Expand Up @@ -176,12 +176,10 @@ export class Link {
* @param regularization The regularization function that computes the
* penalty for this weight. If null, there will be no regularization.
*/
constructor(source: Node, dest: Node,
regularization: RegularizationFunction, initZero?: boolean) {
constructor(source: Node, dest: Node, initZero?: boolean) {
this.id = source.id + "-" + dest.id;
this.source = source;
this.dest = dest;
this.regularization = regularization;
if (initZero) {
this.weight = 0;
}
Expand All @@ -204,7 +202,6 @@ export class Link {
export function buildNetwork(
networkShape: number[], activation: ActivationFunction,
outputActivation: ActivationFunction,
regularization: RegularizationFunction,
inputIds: string[], initZero?: boolean): Node[][] {
let numLayers = networkShape.length;
let id = 1;
Expand All @@ -230,7 +227,7 @@ export function buildNetwork(
// Add links from nodes in the previous layer to this node.
for (let j = 0; j < network[layerIdx - 1].length; j++) {
let prevNode = network[layerIdx - 1][j];
let link = new Link(prevNode, node, regularization, initZero);
let link = new Link(prevNode, node, initZero);
prevNode.outputs.push(link);
node.inputLinks.push(link);
}
Expand Down Expand Up @@ -333,7 +330,7 @@ export function backProp(network: Node[][], target: number,
* derivatives.
*/
export function updateWeights(network: Node[][], learningRate: number,
regularizationRate: number) {
regularization: RegularizationFunction, regularizationRate: number) {
for (let layerIdx = 1; layerIdx < network.length; layerIdx++) {
let currentLayer = network[layerIdx];
for (let i = 0; i < currentLayer.length; i++) {
Expand All @@ -350,16 +347,16 @@ export function updateWeights(network: Node[][], learningRate: number,
if (link.isDead) {
continue;
}
let regulDer = link.regularization ?
link.regularization.der(link.weight) : 0;
let regulDer = regularization ?
regularization.der(link.weight) : 0;
if (link.numAccumulatedDers > 0) {
// Update the weight based on dE/dw.
link.weight = link.weight -
(learningRate / link.numAccumulatedDers) * link.accErrorDer;
// Further update the weight based on regularization.
let newLinkWeight = link.weight -
(learningRate * regularizationRate) * regulDer;
if (link.regularization === RegularizationFunction.L1 &&
if (regularization === RegularizationFunction.L1 &&
link.weight * newLinkWeight < 0) {
// The weight crossed 0 due to the regularization term. Set it to 0.
link.weight = 0;
Expand Down
10 changes: 6 additions & 4 deletions src/playground.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -341,15 +341,17 @@ function makeGUI() {
function() {
state.regularization = regularizations[this.value];
parametersChanged = true;
reset();
state.serialize();
userHasInteracted();
});
regularDropdown.property("value",
getKeyFromValue(regularizations, state.regularization));

let regularRate = d3.select("#regularRate").on("change", function() {
state.regularizationRate = +this.value;
parametersChanged = true;
reset();
state.serialize();
userHasInteracted();
});
regularRate.property("value", state.regularizationRate);

Expand Down Expand Up @@ -913,7 +915,7 @@ function oneStep(): void {
nn.forwardProp(network, input);
nn.backProp(network, point.label, nn.Errors.SQUARE);
if ((i + 1) % state.batchSize === 0) {
nn.updateWeights(network, state.learningRate, state.regularizationRate);
nn.updateWeights(network, state.learningRate, state.regularization, state.regularizationRate);
}
});
// Compute the loss.
Expand Down Expand Up @@ -956,7 +958,7 @@ function reset(onStartup=false) {
let outputActivation = (state.problem === Problem.REGRESSION) ?
nn.Activations.LINEAR : nn.Activations.TANH;
network = nn.buildNetwork(shape, state.activation, outputActivation,
state.regularization, constructInputIds(), state.initZero);
constructInputIds(), state.initZero);
lossTrain = getLoss(network, trainData);
lossTest = getLoss(network, testData);
drawNetwork(network);
Expand Down