Izbrisao console log.

6 files changed, 13 insertions, 23 deletions

diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index 826ac7cb..85d8fb71 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -130,7 +130,7 @@ def returnColumnsInfo(dataset):
         #print(NullRows)
         #print(len(NullRows))
         allNullRows=len(NullRows)
-        print(cMatrix.to_json(orient='index'))
+        #print(cMatrix.to_json(orient='index'))
         #json.loads()['data']
     return {'columnInfo':dict,'allNullColl':int(allNullCols),'allNullRows':int(allNullRows),'rowCount':int(rowCount),'colCount':int(colCount),'cMatrix':json.loads(cMatrix.to_json(orient='split'))['data']}
 
@@ -185,7 +185,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
         col=columnInfo[i]
         if(columnTypes[i]=="categorical"):
             data[col['columnName']]=data[col['columnName']].apply(str)
-            kategorijskekolone.append(col['coumnName'])
+            kategorijskekolone.append(col['columnName'])
     #kategorijskekolone=data.select_dtypes(include=['object']).columns
     print(kategorijskekolone)
     ###NULL
@@ -367,7 +367,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
         classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt, metrics =paramsModel['metrics'])
 
-        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
      
         hist=history.history
         #plt.plot(hist['accuracy'])
@@ -421,14 +421,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
         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']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
         y_pred=classifier.predict(x_test)
         y_pred=(y_pred>=0.5).astype('int')
@@ -473,11 +466,11 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
 
             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'])
+        classifier.add(tf.keras.layers.Dense(units=1,activation=paramsModel['outputLayerActivationFunction']))
 
         classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics'])
 
-        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
+        history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']))
         hist=history.history
         y_pred=classifier.predict(x_test)
         #print(classifier.evaluate(x_test, y_test))
@@ -647,7 +640,7 @@ def manageH5(dataset,params,h5model):
 
     h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics'])
 
-    history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize'])
+    history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=int(params['batchSize']))
     
     y_pred2=h5model.predict(x2)
      
diff --git a/frontend/src/app/_elements/folder/folder.component.ts b/frontend/src/app/_elements/folder/folder.component.ts
index d5a7a85c..6ca0faa8 100644
--- a/frontend/src/app/_elements/folder/folder.component.ts
+++ b/frontend/src/app/_elements/folder/folder.component.ts
@@ -233,15 +233,12 @@ export class FolderComponent implements AfterViewInit {
 
   deleteFile(file: FolderFile, event: Event) {
     event.stopPropagation();
-    console.log('delete');
+    //console.log('delete');
     switch (this.type) {
       case FolderType.Dataset:
         this.datasetsService.deleteDataset(<Dataset>file).subscribe((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:
diff --git a/frontend/src/app/_elements/graph/graph.component.ts b/frontend/src/app/_elements/graph/graph.component.ts
index 73c3b1ab..da2c7767 100644
--- a/frontend/src/app/_elements/graph/graph.component.ts
+++ b/frontend/src/app/_elements/graph/graph.component.ts
@@ -43,7 +43,7 @@ export class GraphComponent implements AfterViewInit {
     window.addEventListener('resize', () => { this.resize() });
     this.update();
     this.resize();
-    console.log(this.layers);
+    //console.log(this.layers);
   }
 
   layers: Node[][] = [];
diff --git a/frontend/src/app/_elements/metric-view/metric-view.component.ts b/frontend/src/app/_elements/metric-view/metric-view.component.ts
index 3840692a..6fd2f320 100644
--- a/frontend/src/app/_elements/metric-view/metric-view.component.ts
+++ b/frontend/src/app/_elements/metric-view/metric-view.component.ts
@@ -28,7 +28,7 @@ export class MetricViewComponent implements OnInit {
       myEpochs.push(epoch + 1);
       for (let key in metrics) {
         let value = metrics[key];
-        console.log(key, ':::', value, epoch);
+        //console.log(key, ':::', value, epoch);
         if (key === 'accuracy') {
           myAcc.push(parseFloat(value));
         }
diff --git a/frontend/src/app/_elements/notifications/notifications.component.ts b/frontend/src/app/_elements/notifications/notifications.component.ts
index d64530b9..5716c1e6 100644
--- a/frontend/src/app/_elements/notifications/notifications.component.ts
+++ b/frontend/src/app/_elements/notifications/notifications.component.ts
@@ -24,8 +24,8 @@ export class NotificationsComponent implements OnInit {
       this.signalRService.hubConnection.on("NotifyEpoch", (mName: string, mId: string, stat: string, totalEpochs: number, currentEpoch: number) => {
         const existingNotification = this.notifications.find(x => x.id === mId)
         const progress = ((currentEpoch + 1) / totalEpochs);
-        console.log("Ukupno epoha", totalEpochs, "Trenutna epoha:", currentEpoch);
-        console.log("stat:", stat);
+        //console.log("Ukupno epoha", totalEpochs, "Trenutna epoha:", currentEpoch);
+        //console.log("stat:", stat);
         if (!existingNotification)
           this.notifications.push(new Notification(`Treniranje modela: ${mName}`, mId, progress, true));
         else {
diff --git a/frontend/src/app/_elements/playlist/playlist.component.ts b/frontend/src/app/_elements/playlist/playlist.component.ts
index 7529b36b..7f476178 100644
--- a/frontend/src/app/_elements/playlist/playlist.component.ts
+++ b/frontend/src/app/_elements/playlist/playlist.component.ts
@@ -44,6 +44,6 @@ export class PlaylistComponent implements OnInit {
       }
     });
 
-    console.log(this.tableDatas);
+    //console.log(this.tableDatas);
   }
 }