diff options
Diffstat (limited to 'backend/microservice')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 22 |
1 files changed, 18 insertions, 4 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 604e4d3c..219f8a20 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -155,7 +155,19 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): data.pop(col) # ### Enkodiranje +<<<<<<< HEAD encodings=paramsExperiment["encodings"] +======= + + from sklearn.preprocessing import LabelEncoder + kategorijskekolone=data.select_dtypes(include=['object']).columns + encoder=LabelEncoder() + for kolona in data.columns: + if(kolona in kategorijskekolone): + data[kolona]=encoder.fit_transform(data[kolona]) + ''' + encoding=paramsExperiment["encoding"] +>>>>>>> 7d57bb9 (Dodate su komponente za grafik.) datafront=dataset.copy() svekolone=datafront.columns kategorijskekolone=datafront.select_dtypes(include=['object']).columns @@ -207,6 +219,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): category_columns.append(col) encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5) encoder.fit_transform(data) + + ''' # # Input - output # @@ -301,7 +315,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) @@ -333,7 +347,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history @@ -359,7 +373,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=1)) - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =['accuracy','mae','mse']) history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history @@ -529,7 +543,7 @@ def manageH5(dataset,params,h5model): h5model.summary() #ann_viz(h5model, title="My neural network") - h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics']) + h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['accuracy','']) history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize']) |