From 75f0fca5f9e7e74979f63d73741512ea2e58e9e6 Mon Sep 17 00:00:00 2001 From: Danijel Anđelković Date: Wed, 4 May 2022 22:33:10 +0200 Subject: Promenio nazive ulaza na ML u za treniranje modela. --- backend/microservice/api/newmlservice.py | 47 ++++++++++++++++++-------------- 1 file changed, 27 insertions(+), 20 deletions(-) (limited to 'backend/microservice/api') diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index ad848fd9..d84d9567 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -282,8 +282,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): # # Podela na test i trening skupove # - test=paramsExperiment["randomTestSetDistribution"] - randomOrder = paramsExperiment["randomOrder"] + test=paramsModel["randomTestSetDistribution"] + randomOrder = paramsModel["randomOrder"] if(randomOrder): random=123 else: @@ -329,8 +329,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): if(problem_type=='multi-klasifikacioni'): #print('multi') - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -341,12 +341,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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], kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],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 ###Kernel - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][i+1]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -356,7 +356,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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=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=5, activation=paramsModel['outputLayerActivationFunction']))#izlazni sloj @@ -386,8 +386,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): elif(problem_type=='binarni-klasifikacioni'): #print('*************************************************************************binarni') - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -398,12 +398,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],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) - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -412,12 +412,19 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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=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']))#izlazni sloj classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics =paramsModel['metrics']) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + print(x_train) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + print(y_train) + print('AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA') + + history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=float(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id'])) hist=history.history y_pred=classifier.predict(x_test) @@ -434,8 +441,8 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): return filepath,hist elif(problem_type=='regresioni'): - reg=paramsModel['regularisation'][0] - regRate=float(paramsModel['regularisationRate'][0]) + reg=paramsModel['layers'][0]['regularisation'] + regRate=float(paramsModel['layers'][0]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -446,12 +453,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog + classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][0]['neurons'], activation=paramsModel['layers'][0]['activationFunction'],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) - reg=paramsModel['regularisation'][i+1] - regRate=float(paramsModel['regularisationRate'][i+1]) + reg=paramsModel['layers'][i+1]['regularisation'] + regRate=float(paramsModel['layers'][i+1]['regularisationRate']) if(reg=='l1'): kernelreg=tf.keras.regularizers.l1(regRate) biasreg=tf.keras.regularizers.l1(regRate) @@ -461,7 +468,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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=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']) -- cgit v1.2.3