From eee8b1f3790b243be19b015a37a2acd4e882b31e Mon Sep 17 00:00:00 2001 From: Sonja Galovic Date: Wed, 4 May 2022 20:46:59 +0200 Subject: Omogucena izmena eksperimenta. Popravljen bag kod izbora izlazne kolone kad se menja izabran dataset (reset inputa i outputa). --- frontend/src/app/_pages/experiment/experiment.component.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'frontend/src/app/_pages/experiment') diff --git a/frontend/src/app/_pages/experiment/experiment.component.ts b/frontend/src/app/_pages/experiment/experiment.component.ts index 28552664..3c8d8651 100644 --- a/frontend/src/app/_pages/experiment/experiment.component.ts +++ b/frontend/src/app/_pages/experiment/experiment.component.ts @@ -120,7 +120,7 @@ export class ExperimentComponent implements AfterViewInit { columnTableChangedEvent() { //sta se desi kad se nesto promeni u column-table komponenti... - console.log("promenio se column-table"); + //console.log("promenio se column-table"); } setDataset(dataset: FolderFile) { -- cgit v1.2.3 From a5365ef62bc89130b4240e124b3a361edfb34391 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Wed, 4 May 2022 21:06:06 +0200 Subject: Dodao dugmad za brisanje kada korisnik pregleda liste ssvojih podataka, ispravio neke bagove oko grafa kada nema ulaznih kolona. Dodao kor. matricu u dataset. --- backend/api/api/Models/Dataset.cs | 1 + backend/api/api/Models/Model.cs | 3 ++- backend/api/api/Services/FillAnEmptyDb.cs | 6 +++--- frontend/src/app/_data/Dataset.ts | 6 +++--- frontend/src/app/_data/Experiment.ts | 9 ++++---- .../src/app/_elements/folder/folder.component.css | 8 +++++++ .../src/app/_elements/folder/folder.component.html | 12 ++++++++--- .../src/app/_elements/folder/folder.component.ts | 25 +++++++++++++++------- .../_elements/form-model/form-model.component.html | 4 +++- .../src/app/_elements/graph/graph.component.html | 2 +- .../src/app/_elements/graph/graph.component.ts | 2 +- .../_pages/experiment/experiment.component.html | 2 +- 12 files changed, 54 insertions(+), 26 deletions(-) (limited to 'frontend/src/app/_pages/experiment') diff --git a/backend/api/api/Models/Dataset.cs b/backend/api/api/Models/Dataset.cs index 7acd4382..beb66de9 100644 --- a/backend/api/api/Models/Dataset.cs +++ b/backend/api/api/Models/Dataset.cs @@ -28,6 +28,7 @@ namespace api.Models public int nullRows { get; set; } public bool isPreProcess { get; set; } + public float[][] cMatrix { get; set; } } } diff --git a/backend/api/api/Models/Model.cs b/backend/api/api/Models/Model.cs index d8921713..494ae9e9 100644 --- a/backend/api/api/Models/Model.cs +++ b/backend/api/api/Models/Model.cs @@ -27,7 +27,8 @@ namespace api.Models public string lossFunction { get; set; } //public int inputNeurons { get; set; } public int hiddenLayers { get; set; } - public int batchSize { get; set; } + public string batchSize { get; set; } + public string learningRate { get; set; } // na izlazu je moguce da bude vise neurona (klasifikacioni problem sa vise od 2 klase) public int outputNeurons { get; set; } public Layer[] layers { get; set; } diff --git a/backend/api/api/Services/FillAnEmptyDb.cs b/backend/api/api/Services/FillAnEmptyDb.cs index 99bbb91f..f05d8cd8 100644 --- a/backend/api/api/Services/FillAnEmptyDb.cs +++ b/backend/api/api/Services/FillAnEmptyDb.cs @@ -99,7 +99,7 @@ namespace api.Services model.optimizer = "Adam"; model.lossFunction = "mean_squared_error"; model.hiddenLayers = 5; - model.batchSize = 8; + model.batchSize = "8"; model.outputNeurons = 0; model.outputLayerActivationFunction = "sigmoid"; model.metrics = new string[] { }; @@ -210,7 +210,7 @@ namespace api.Services model.optimizer = "Adam"; model.lossFunction = "mean_absolute_error"; model.hiddenLayers = 4; - model.batchSize = 5; + model.batchSize = "8"; model.outputNeurons = 0; model.outputLayerActivationFunction = "relu"; model.metrics = new string[] { }; @@ -316,7 +316,7 @@ namespace api.Services model.optimizer = "Adam"; model.lossFunction = "sparse_categorical_crossentropy"; model.hiddenLayers = 3; - model.batchSize = 4; + model.batchSize = "64"; model.outputNeurons = 0; model.outputLayerActivationFunction = "softmax"; model.metrics = new string[] { }; diff --git a/frontend/src/app/_data/Dataset.ts b/frontend/src/app/_data/Dataset.ts index 7ae5c4ab..9efb9f9b 100644 --- a/frontend/src/app/_data/Dataset.ts +++ b/frontend/src/app/_data/Dataset.ts @@ -18,7 +18,7 @@ export default class Dataset extends FolderFile { public rowCount: number = 0, public nullRows: number = 0, public nullCols: number = 0, - public preview: string[][] = [[]] + public cMatrix: string[][] = [[]] ) { super(name, dateCreated, lastUpdated); } @@ -39,13 +39,13 @@ export class ColumnInfo { public max?: number, public q1?: number, public q3?: number, - ) { + ) { /*if (isNumber) this.columnType = ColumnType.numerical; else this.columnType = ColumnType.categorical;*/ } - + } export enum ColumnType { diff --git a/frontend/src/app/_data/Experiment.ts b/frontend/src/app/_data/Experiment.ts index 9ad57fc3..05336495 100644 --- a/frontend/src/app/_data/Experiment.ts +++ b/frontend/src/app/_data/Experiment.ts @@ -16,10 +16,12 @@ export default class Experiment { public lastUpdated: Date = new Date(), public modelIds: string[] = [], - + public encodings: ColumnEncoding[] = []//[{columnName: "", columnEncoding: Encoding.Label}] ) { } + + _columnsSelected: boolean = false; } export enum NullValueOptions { @@ -67,9 +69,8 @@ export enum Encoding { } export class ColumnEncoding { - constructor ( + constructor( public columnName: string, public encoding: Encoding - ) - {} + ) { } } \ No newline at end of file diff --git a/frontend/src/app/_elements/folder/folder.component.css b/frontend/src/app/_elements/folder/folder.component.css index 2340ee8a..62324d62 100644 --- a/frontend/src/app/_elements/folder/folder.component.css +++ b/frontend/src/app/_elements/folder/folder.component.css @@ -152,6 +152,14 @@ display: none; } +.hover-show { + display: none; +} + +.list-item:hover>.hover-show { + display: initial; +} + .list-add { display: flex; flex-direction: row; diff --git a/frontend/src/app/_elements/folder/folder.component.html b/frontend/src/app/_elements/folder/folder.component.html index 48b59dc8..bff066be 100644 --- a/frontend/src/app/_elements/folder/folder.component.html +++ b/frontend/src/app/_elements/folder/folder.component.html @@ -56,15 +56,15 @@
-
- - + +
@@ -74,6 +74,12 @@
{{file.lastUpdated | date}}
+
+ +
+
diff --git a/frontend/src/app/_elements/folder/folder.component.ts b/frontend/src/app/_elements/folder/folder.component.ts index 20ca1121..ffce3fb1 100644 --- a/frontend/src/app/_elements/folder/folder.component.ts +++ b/frontend/src/app/_elements/folder/folder.component.ts @@ -98,8 +98,8 @@ export class FolderComponent implements AfterViewInit { this.newFileSelected = false; this.listView = false; this.selectedFileChanged.emit(this.selectedFile); - this.displayFile(); this.selectTab(TabType.File); + this.displayFile(); } createNewFile() { @@ -117,9 +117,13 @@ export class FolderComponent implements AfterViewInit { _initialized: boolean = false; refreshFiles(selectedDatasetId: string | null) { + this.files = [] + this.filteredFiles.length = 0; + this.folders[TabType.NewFile] = []; + this.folders[TabType.File] = []; this.tabsToShow.forEach(tab => { this.folders[tab] = []; - }) + }); this.datasetsService.getMyDatasets().subscribe((datasets) => { this.folders[TabType.MyDatasets] = datasets; @@ -137,7 +141,6 @@ export class FolderComponent implements AfterViewInit { }); this.modelsService.getMyModels().subscribe((models) => { - console.log(models); this.folders[TabType.MyModels] = models; }); @@ -164,6 +167,7 @@ export class FolderComponent implements AfterViewInit { switch (this.type) { case FolderType.Dataset: this.formDataset!.uploadDataset((dataset: Dataset) => { + this.newFile = undefined; Shared.openDialog("Obaveštenje", "Uspešno ste dodali novi izvor podataka u kolekciju. Molimo sačekajte par trenutaka da se procesira."); this.refreshFiles(dataset._id); }, @@ -173,7 +177,7 @@ export class FolderComponent implements AfterViewInit { break; case FolderType.Model: this.modelsService.addModel(this.formModel.newModel).subscribe(model => { - this.formModel.newModel = model; + this.newFile = undefined; Shared.openDialog("Obaveštenje", "Uspešno ste dodali novu konfiguraciju neuronske mreže u kolekciju."); this.refreshFiles(null); // todo select model }, (err) => { @@ -205,8 +209,8 @@ export class FolderComponent implements AfterViewInit { filteredFiles: FolderFile[] = []; searchTermsChanged() { - if (!this.files) return; this.filteredFiles.length = 0; + if (!this.files) return; this.filteredFiles.push(...this.files.filter((file) => file.name.toLowerCase().includes(this.searchTerm.toLowerCase()))); /*if (this.selectedFile) { if (!this.filteredFiles.includes(this.selectedFile)) { @@ -226,17 +230,22 @@ export class FolderComponent implements AfterViewInit { this.listView = !this.listView; } - deleteFile(file: FolderFile) { + deleteFile(file: FolderFile, event: Event) { + event.stopPropagation(); console.log('delete'); switch (this.type) { case FolderType.Dataset: this.datasetsService.deleteDataset(file).subscribe((response) => { - console.log(response); + console.log(this.files, this.filteredFiles) + this.filteredFiles.splice(this.filteredFiles.indexOf(file), 1); + console.log(this.files, this.filteredFiles) + this.refreshFiles(null); + console.log(this.files, this.filteredFiles) }); break; case FolderType.Model: this.modelsService.deleteModel(file).subscribe((response) => { - console.log(response); + this.refreshFiles(null); }); break; case FolderType.Experiment: diff --git a/frontend/src/app/_elements/form-model/form-model.component.html b/frontend/src/app/_elements/form-model/form-model.component.html index c0318012..bf4aa0e3 100644 --- a/frontend/src/app/_elements/form-model/form-model.component.html +++ b/frontend/src/app/_elements/form-model/form-model.component.html @@ -108,7 +108,9 @@
- + + +
diff --git a/frontend/src/app/_elements/graph/graph.component.html b/frontend/src/app/_elements/graph/graph.component.html index 3c01bc5e..a021431d 100644 --- a/frontend/src/app/_elements/graph/graph.component.html +++ b/frontend/src/app/_elements/graph/graph.component.html @@ -1,7 +1,7 @@
- {{ i == 0 ? (inputColumns ? inputColumns[j] : 'nepoznato') : (i > 0 && i + {{ i == 0 ? (inputColumns && inputColumns.length >= j ? inputColumns[j] : 'nepoznato') : (i > 0 && i < layers.length - 1 ? model!.layers[i-1].activationFunction : (i==layers.length - 1 ? 'out' : '')) }}
diff --git a/frontend/src/app/_elements/graph/graph.component.ts b/frontend/src/app/_elements/graph/graph.component.ts index c7f8d964..73c3b1ab 100644 --- a/frontend/src/app/_elements/graph/graph.component.ts +++ b/frontend/src/app/_elements/graph/graph.component.ts @@ -28,7 +28,7 @@ export class GraphComponent implements AfterViewInit { @Input() outputNodeColor: string = '#dfd7d7'; private ctx!: CanvasRenderingContext2D; - @Input() inputColumns?: string[] = ['Nije odabran eksperiment']; + @Input() inputColumns?: string[]; constructor() { } diff --git a/frontend/src/app/_pages/experiment/experiment.component.html b/frontend/src/app/_pages/experiment/experiment.component.html index baae864e..83c45405 100644 --- a/frontend/src/app/_pages/experiment/experiment.component.html +++ b/frontend/src/app/_pages/experiment/experiment.component.html @@ -32,7 +32,7 @@
- +
-- cgit v1.2.3 From 75f0fca5f9e7e74979f63d73741512ea2e58e9e6 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Wed, 4 May 2022 22:33:10 +0200 Subject: Promenio nazive ulaza na ML u za treniranje modela. --- backend/microservice/api/newmlservice.py | 47 +++++++++++++--------- .../column-table/column-table.component.ts | 2 +- .../src/app/_elements/folder/folder.component.ts | 5 ++- .../_pages/experiment/experiment.component.html | 4 +- .../app/_pages/experiment/experiment.component.ts | 36 +++++++++++++++-- 5 files changed, 65 insertions(+), 29 deletions(-) (limited to 'frontend/src/app/_pages/experiment') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index ad848fd9..d84d9567 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -282,8 +282,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # # Podela na test i trening skupove # - test=paramsExperiment["randomTestSetDistribution"] - randomOrder = paramsExperiment["randomOrder"] + test=paramsModel["randomTestSetDistribution"] + randomOrder = paramsModel["randomOrder"] if(randomOrder): random=123 else: @@ -329,8 +329,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): if(problem_type=='multi-klasifikacioni'): #print('multi') - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -341,12 +341,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): activityreg=tf.keras.regularizers.l2(regRate) classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1], kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],input_dim=x_train.shape[1], kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja ###Kernel - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][i+1]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -356,7 +356,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): biasreg=tf.keras.regularizers.l2(regRate) activityreg=tf.keras.regularizers.l2(regRate) - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][i+1]['neurons'], activation=paramsModel['layers'][i+1]['activationFunction'],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=5, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj @@ -386,8 +386,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -398,12 +398,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): activityreg=tf.keras.regularizers.l2(regRate) classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja #print(i) - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -412,12 +412,19 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): kernelreg=tf.keras.regularizers.l2(regRate) biasreg=tf.keras.regularizers.l2(regRate) activityreg=tf.keras.regularizers.l2(regRate) - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][i+1]['neurons'], activation=paramsModel['layers'][i+1]['activationFunction'],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics']) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + print(x_train) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + print(y_train) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) @@ -434,8 +441,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): return filepath,hist elif(problem_type=='regresioni'): - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -446,12 +453,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): activityreg=tf.keras.regularizers.l2(regRate) classifier=tf.keras.Sequential() - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja #print(i) - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][i+1]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -461,7 +468,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): biasreg=tf.keras.regularizers.l2(regRate) activityreg=tf.keras.regularizers.l2(regRate) - classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][i+1]['neurons'], activation=paramsModel['layers'][i+1]['activationFunction'],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1),activation=paramsModel['outputLayerActivationFunction']) diff --git a/frontend/src/app/_elements/column-table/column-table.component.ts b/frontend/src/app/_elements/column-table/column-table.component.ts index 13828a2c..b99a3be0 100644 --- a/frontend/src/app/_elements/column-table/column-table.component.ts +++ b/frontend/src/app/_elements/column-table/column-table.component.ts @@ -223,7 +223,7 @@ export class ColumnTableComponent implements AfterViewInit { this.experiment.name = selectedName; //napravi odvojene dugmice za save i update -> za update nece da se otvara dijalog za ime this.experimentService.addExperiment(this.experiment).subscribe((response) => { - this.experiment = response; + this.experiment._id = response._id; this.okPressed.emit(); }); }); diff --git a/frontend/src/app/_elements/folder/folder.component.ts b/frontend/src/app/_elements/folder/folder.component.ts index a99c6a9d..d5a7a85c 100644 --- a/frontend/src/app/_elements/folder/folder.component.ts +++ b/frontend/src/app/_elements/folder/folder.component.ts @@ -53,8 +53,9 @@ export class FolderComponent implements AfterViewInit { if (this.signalRService.hubConnection) { this.signalRService.hubConnection.on("NotifyDataset", (dName: string, dId: string) => { - this.refreshFiles(dId); - + if (this.type == FolderType.Dataset) { + this.refreshFiles(dId); + } }); } else { console.warn("Dataset-Load: No connection!"); diff --git a/frontend/src/app/_pages/experiment/experiment.component.html b/frontend/src/app/_pages/experiment/experiment.component.html index 83c45405..2b32db81 100644 --- a/frontend/src/app/_pages/experiment/experiment.component.html +++ b/frontend/src/app/_pages/experiment/experiment.component.html @@ -37,12 +37,12 @@
- +
- +
diff --git a/frontend/src/app/_pages/experiment/experiment.component.ts b/frontend/src/app/_pages/experiment/experiment.component.ts index 3c8d8651..c4d6063c 100644 --- a/frontend/src/app/_pages/experiment/experiment.component.ts +++ b/frontend/src/app/_pages/experiment/experiment.component.ts @@ -10,6 +10,8 @@ import { ModelsService } from 'src/app/_services/models.service'; import Model from 'src/app/_data/Model'; import Dataset from 'src/app/_data/Dataset'; import { ColumnTableComponent } from 'src/app/_elements/column-table/column-table.component'; +import { SignalRService } from 'src/app/_services/signal-r.service'; +import { MetricViewComponent } from 'src/app/_elements/metric-view/metric-view.component'; @Component({ selector: 'app-experiment', @@ -26,11 +28,11 @@ export class ExperimentComponent implements AfterViewInit { experiment: Experiment; dataset?: Dataset; @ViewChild("folderDataset") folderDataset!: FolderComponent; - @ViewChild("folderModel") folderModel!: FolderComponent; @ViewChild(ColumnTableComponent) columnTable!: ColumnTableComponent; + @ViewChild("folderModel") folderModel!: FolderComponent; + @ViewChild("metricView") metricView!: MetricViewComponent; - - constructor(private experimentsService: ExperimentsService, private modelsService: ModelsService) { + constructor(private experimentsService: ExperimentsService, private modelsService: ModelsService, private signalRService: SignalRService) { this.experiment = new Experiment("exp1"); } @@ -43,7 +45,11 @@ export class ExperimentComponent implements AfterViewInit { } trainModel() { - this.modelsService.trainModel((this.folderModel.selectedFile)._id, this.experiment._id).subscribe(() => { console.log("pocelo treniranje") }) + if (!this.modelToTrain) { + Shared.openDialog('Greška', 'Morate odabrati konfiguraciju neuronske mreže'); + } else { + this.modelsService.trainModel(this.modelToTrain._id, this.experiment._id).subscribe(() => { console.log("pocelo treniranje") }); + } } stepHeight = this.calcStepHeight(); @@ -65,8 +71,23 @@ export class ExperimentComponent implements AfterViewInit { this.stepsContainer.nativeElement.addEventListener('scroll', (event: Event) => { Shared.emitBGScrollEvent(this.stepsContainer.nativeElement.scrollTop); }); + + if (this.signalRService.hubConnection) { + this.signalRService.hubConnection.on("NotifyEpoch", (mName: string, mId: string, stat: string, totalEpochs: number, currentEpoch: number) => { + console.log(this.modelToTrain?._id, mId); + if (this.modelToTrain?._id == mId) { + stat = stat.replace(/'/g, '"'); + //console.log('JSON', this.trainingResult); + this.history.push(JSON.parse(stat)); + this.metricView.update(this.history); + } + }); + + } } + history: any[] = []; + updatePageIfScrolled() { if (this.scrolling) return; const currentPage = Math.round(this.stepsContainer.nativeElement.scrollTop / this.stepHeight) @@ -130,4 +151,11 @@ export class ExperimentComponent implements AfterViewInit { this.columnTable.loadDataset(this.dataset); } + + modelToTrain?: Model; + + setModel(model: FolderFile) { + const m = model; + this.modelToTrain = m; + } } -- cgit v1.2.3