diff options
Diffstat (limited to 'backend/microservice')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 6a863013..f374e9d2 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -291,11 +291,12 @@ 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) #print(x_train,x_test) x, x_test, y, y_test = train_test_split(x, y, test_size=test, random_state=random, shuffle=True) x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.15, shuffle=True) - # # Treniranje modela # # @@ -507,9 +508,9 @@ 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)) - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['mae','mse','rmse']) 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 |