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-rw-r--r--backend/microservice/api/newmlservice.py155
1 files changed, 100 insertions, 55 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index f74f8386..647c3b79 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -268,73 +268,73 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
#
#
###OPTIMIZATORI
- """
- if(params['optimizer']=='adam'):
- opt=tf.keras.optimizers.Adam(learning_rate=params['learningRate'])
+
+ if(paramsModel['optimizer']=='Adam'):
+ opt=tf.keras.optimizers.Adam(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='adadelta'):
- opt=tf.keras.optimizers.Adadelta(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Adadelta'):
+ opt=tf.keras.optimizers.Adadelta(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='adagrad'):
- opt=tf.keras.optimizers.Adagrad(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Adagrad'):
+ opt=tf.keras.optimizers.Adagrad(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='adamax'):
- opt=tf.keras.optimizers.Adamax(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Adamax'):
+ opt=tf.keras.optimizers.Adamax(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='nadam'):
- opt=tf.keras.optimizers.Nadam(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Nadam'):
+ opt=tf.keras.optimizers.Nadam(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='sgd'):
- opt=tf.keras.optimizers.SGD(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Sgd'):
+ opt=tf.keras.optimizers.SGD(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='ftrl'):
- opt=tf.keras.optimizers.Ftrl(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Ftrl'):
+ opt=tf.keras.optimizers.Ftrl(learning_rate=float(paramsModel['learningRate']))
- elif(params['optimizer']=='rmsprop'):
- opt=tf.keras.optimizers.RMSprop(learning_rate=params['learningRate'])
+ elif(paramsModel['optimizer']=='Rmsprop'):
+ opt=tf.keras.optimizers.RMSprop(learning_rate=float(paramsModel['learningRate']))
###REGULARIZACIJA
#regularisation={'kernelType':'l1 ili l2 ili l1_l2','kernelRate':default=0.01 ili jedna od vrednosti(0.0001,0.001,0.1,1,2,3) ili neka koju je korisnik zadao,'biasType':'','biasRate':'','activityType','activityRate'}
- reg=params['regularisation']
-
- ###Kernel
- if(reg['kernelType']=='l1'):
- kernelreg=tf.keras.regularizers.l1(reg['kernelRate'])
- elif(reg['kernelType']=='l2'):
- kernelreg=tf.keras.regularizers.l2(reg['kernelRate'])
- elif(reg['kernelType']=='l1l2'):
- kernelreg=tf.keras.regularizers.l1_l2(l1=reg['kernelRate'][0],l2=reg['kernelRate'][1])
-
- ###Bias
- if(reg['biasType']=='l1'):
- biasreg=tf.keras.regularizers.l1(reg['biasRate'])
- elif(reg['biasType']=='l2'):
- biasreg=tf.keras.regularizers.l2(reg['biasRate'])
- elif(reg['biasType']=='l1l2'):
- biasreg=tf.keras.regularizers.l1_l2(l1=reg['biasRate'][0],l2=reg['biasRate'][1])
-
- ###Activity
- if(reg['kernelType']=='l1'):
- activityreg=tf.keras.regularizers.l1(reg['activityRate'])
- elif(reg['kernelType']=='l2'):
- activityreg=tf.keras.regularizers.l2(reg['activityRate'])
- elif(reg['kernelType']=='l1l2'):
- activityreg=tf.keras.regularizers.l1_l2(l1=reg['activityRate'][0],l2=reg['activityRate'][1])
- """
+
+
filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']+".h5")
if(problem_type=='multi-klasifikacioni'):
#print('multi')
+
+ reg=paramsModel['regularisation'][0]
+ regRate=float(paramsModel['regularisationRate'][0])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
+
classifier=tf.keras.Sequential()
-
- classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1], kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog
+
for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
- #print(i)
- classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
+ ###Kernel
+ reg=paramsModel['regularisation'][i+1]
+ regRate=float(paramsModel['regularisationRate'][i+1])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
+
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj
+
classifier.add(tf.keras.layers.Dense(units=5, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj
- classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics'])
+ classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt, metrics =paramsModel['metrics'])
history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize'],callbacks=callback(x_test, y_test,paramsModel['_id']))
@@ -358,15 +358,37 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
elif(problem_type=='binarni-klasifikacioni'):
#print('*************************************************************************binarni')
+ reg=paramsModel['regularisation'][0]
+ regRate=float(paramsModel['regularisationRate'][0])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
classifier=tf.keras.Sequential()
- classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog
+
for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
#print(i)
- classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
+ reg=paramsModel['regularisation'][i+1]
+ regRate=float(paramsModel['regularisationRate'][i+1])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#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 = opt , metrics =paramsModel['metrics'])
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
@@ -384,15 +406,38 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback):
return filepath,hist
elif(problem_type=='regresioni'):
+ reg=paramsModel['regularisation'][0]
+ regRate=float(paramsModel['regularisationRate'][0])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
classifier=tf.keras.Sequential()
- classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog
+
for i in range(paramsModel['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
#print(i)
- 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))
+ reg=paramsModel['regularisation'][i+1]
+ regRate=float(paramsModel['regularisationRate'][i+1])
+ if(reg=='l1'):
+ kernelreg=tf.keras.regularizers.l1(regRate)
+ biasreg=tf.keras.regularizers.l1(regRate)
+ activityreg=tf.keras.regularizers.l1(regRate)
+ elif(reg=='l2'):
+ kernelreg=tf.keras.regularizers.l2(regRate)
+ biasreg=tf.keras.regularizers.l2(regRate)
+ activityreg=tf.keras.regularizers.l2(regRate)
+
+ classifier.add(tf.keras.layers.Dense(units=paramsModel['hiddenLayerNeurons'], activation=paramsModel['hiddenLayerActivationFunctions'][i+1],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.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics'])
+ classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics'])
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
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-19 19:54:01 +0000