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| author | Sonja Galovic <galovicsonja@gmail.com> | 2022-05-18 14:00:44 +0200 |
|---|---|---|
| committer | Sonja Galovic <galovicsonja@gmail.com> | 2022-05-18 14:00:44 +0200 |
| commit | f3e4093e5a29eadd61a00a3a05668e3e24b7d40d (patch) | |
| tree | 613df1a3ed2afa08f450267667732854dcd51134 /backend/microservice | |
| parent | 9233e6f193f68a0477e2900ac7a82928ab7f4adc (diff) | |
| parent | aa71097de7e95658c0cfa3e7d212f018aa917baf (diff) | |
Merge branch 'redesign' of http://gitlab.pmf.kg.ac.rs/igrannonica/neuronstellar into redesign
# Conflicts:
# frontend/src/app/_elements/column-table/column-table.component.html
# frontend/src/app/_pages/experiment/experiment.component.ts
Diffstat (limited to 'backend/microservice')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 6a863013..fd21f8ce 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 # # @@ -369,7 +370,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): - classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['mae','mse']) 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)) @@ -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']) 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 |