diff options
Diffstat (limited to 'frontend/src/app/_data/Model.ts')
| -rw-r--r-- | frontend/src/app/_data/Model.ts | 13 |
1 files changed, 7 insertions, 6 deletions
diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts index f527dc7f..898805d3 100644 --- a/frontend/src/app/_data/Model.ts +++ b/frontend/src/app/_data/Model.ts @@ -2,6 +2,7 @@ import { NgIf } from "@angular/common"; import { FolderFile } from "./FolderFile"; export default class Model extends FolderFile { + public lossFunction: LossFunction; constructor( name: string = 'Novi model', public description: string = '', @@ -12,7 +13,6 @@ export default class Model extends FolderFile { // Neural net training settings public type: ProblemType = ProblemType.Regression, public optimizer: Optimizer = Optimizer.Adam, - public lossFunction: LossFunction = LossFunctionRegression[0], public inputNeurons: number = 1, public hiddenLayers: number = 1, public batchSize: BatchSize = BatchSize.O3, @@ -28,11 +28,12 @@ export default class Model extends FolderFile { public randomOrder: boolean = true, public randomTestSet: boolean = true, public randomTestSetDistribution: number = 0.1, //0.1-0.9 (10% - 90%) JESTE OVDE ZAKUCANO 10, AL POSLATO JE KAO 0.1 BACK-U - public validationSize:number=0.1, + public validationSize: number = 0.1, public isPublic: boolean = false, public accessibleByLink: boolean = false ) { super(name, dateCreated, lastUpdated); + this.lossFunction = (this.type == ProblemType.Regression ? LossFunctionRegression[0] : (this.type == ProblemType.BinaryClassification ? LossFunctionBinaryClassification[0] : LossFunctionMultiClassification[0])); } } export class Layer { @@ -45,8 +46,8 @@ export class Layer { ) { } } export enum LearningRate { - LR1 = '0.00001', - LR2 = '0.0001', + // LR1 = '0.00001', + // LR2 = '0.0001', LR3 = '0.001', LR4 = '0.003', LR5 = '0.01', @@ -120,7 +121,7 @@ export enum LossFunction { SquaredHingeLoss = 'squared_hinge', HingeLoss = 'hinge', // multi-class classification loss functions - CategoricalCrossEntropy = 'categorical_crossentropy', + // CategoricalCrossEntropy = 'categorical_crossentropy', SparseCategoricalCrossEntropy = 'sparse_categorical_crossentropy', KLDivergence = 'kullback_leibler_divergence', @@ -134,7 +135,7 @@ export enum LossFunction { export const LossFunctionRegression = [LossFunction.MeanAbsoluteError, LossFunction.MeanSquaredError, LossFunction.MeanSquaredLogarithmicError] export const LossFunctionBinaryClassification = [LossFunction.BinaryCrossEntropy, LossFunction.SquaredHingeLoss, LossFunction.HingeLoss] -export const LossFunctionMultiClassification = [LossFunction.CategoricalCrossEntropy, LossFunction.SparseCategoricalCrossEntropy, LossFunction.KLDivergence] +export const LossFunctionMultiClassification = [/*LossFunction.CategoricalCrossEntropy,*/ LossFunction.SparseCategoricalCrossEntropy, LossFunction.KLDivergence] export enum Optimizer { Adam = 'Adam', |