diff options
Diffstat (limited to 'backend')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 48 |
1 files changed, 35 insertions, 13 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index bca729e4..07735077 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -291,15 +291,16 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): random=123 else: random=0 - x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) + #x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) #print(x_train,x_test) - + x, x_test, y, y_test = train_test_split(x, y, test_size=0.15, shuffle=True) + x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.15, shuffle=True) # # Treniranje modela # # ###OPTIMIZATORI - + print(paramsModel['optimizer']) if(paramsModel['optimizer']=='Adam'): opt=tf.keras.optimizers.Adam(learning_rate=3) @@ -315,13 +316,16 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): elif(paramsModel['optimizer']=='Nadam'): opt=tf.keras.optimizers.Nadam(learning_rate=float(paramsModel['learningRate'])) - elif(paramsModel['optimizer']=='Sgd'): + elif(paramsModel['optimizer']=='SGD'): + opt=tf.keras.optimizers.SGD(learning_rate=float(paramsModel['learningRate'])) + + if(paramsModel['optimizer']=='SGDMomentum'): opt=tf.keras.optimizers.SGD(learning_rate=float(paramsModel['learningRate'])) elif(paramsModel['optimizer']=='Ftrl'): opt=tf.keras.optimizers.Ftrl(learning_rate=float(paramsModel['learningRate'])) - elif(paramsModel['optimizer']=='Rmsprop'): + elif(paramsModel['optimizer']=='RMSprop'): opt=tf.keras.optimizers.RMSprop(learning_rate=float(paramsModel['learningRate'])) ###REGULARIZACIJA @@ -331,7 +335,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']+".h5") if(problem_type=='multi-klasifikacioni'): #print('multi') - print(paramsModel) + #print(paramsModel) reg=paramsModel['layers'][0]['regularisation'] regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): @@ -367,7 +371,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['accuracy','mae','mse']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history #plt.plot(hist['accuracy']) @@ -380,12 +384,30 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.save(filepath, save_format='h5') - + + accuracy=metrics.accuracy_score(y_test, y_pred) + macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro') + micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro') + macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro') + micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro') + macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro') + micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro') + + metrics= { + "accuracy" : float(accuracy), + "macro_averaged_precision" :float(macro_averaged_precision), + "micro_averaged_precision" : float(micro_averaged_precision), + "macro_averaged_recall" : float(macro_averaged_recall), + "micro_averaged_recall" : float(micro_averaged_recall), + "macro_averaged_f1" : float(macro_averaged_f1), + "micro_averaged_f1" : float(micro_averaged_f1) + } + #vizuelizacija u python-u #from ann_visualizer.visualize import ann_viz; #ann_viz(classifier, title="My neural network") - return filepath,hist,y_pred,y_test + return filepath,hist,metrics elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') @@ -421,7 +443,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt , metrics = ['accuracy','mae','mse']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -491,7 +513,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['accuracy','mae','mse']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) + history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) @@ -543,7 +565,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # # Metrike # - + ''' if(problem_type=="regresioni"): # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/ mse = float(sm.mean_squared_error(y_test,y_pred)) @@ -565,7 +587,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): "r2" : r2, "adj_r2" : adj_r2 } - ''' + elif(problem_type=="multi-klasifikacioni"): cr=sm.classification_report(y_test, y_pred) |