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| author | TAMARA JERINIC <tamara.jerinic@gmail.com> | 2022-04-16 21:52:40 +0200 |
|---|---|---|
| committer | TAMARA JERINIC <tamara.jerinic@gmail.com> | 2022-04-16 21:53:17 +0200 |
| commit | 66c147bc3154e531cfc78591a7451d904122fc1f (patch) | |
| tree | 594bfb029c004a69800938087dc1586e31067a24 /backend/microservice/api/newmlservice.py | |
| parent | 3a9bffc6da590fd1a98a0c885d608d40849cffd4 (diff) | |
Ispravljeno obaveštavanje backend-a o epohama.
Diffstat (limited to 'backend/microservice/api/newmlservice.py')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 585db480..a9bce3bb 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -252,7 +252,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): opt=tf.keras.optimizers.RMSprop(learning_rate=params['learningRate']) ###REGULARIZACIJA - #regularisation={'kernelType':'l1 ili l2 ili l1_l2','krenelRate':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'} + #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 @@ -279,7 +279,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): 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']) + filepath=os.path.join("temp/",paramsExperiment['_id']+"_"+paramsModel['_id']+".h5") if(problem_type=='multi-klasifikacioni'): #print('multi') classifier=tf.keras.Sequential() @@ -294,7 +294,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + 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 #plt.plot(hist['accuracy']) @@ -326,7 +326,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + 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 y_pred=classifier.predict(x_test) y_pred=(y_pred>=0.5).astype('int') @@ -352,7 +352,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.compile(loss =paramsModel["lossFunction"] , optimizer = paramsModel['optimizer'] , metrics =paramsModel['metrics']) - history=classifier.fit(x_train, y_train, epochs = paramsModel['epochs'],batch_size=paramsModel['batchSize']) + 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 y_pred=classifier.predict(x_test) #print(classifier.evaluate(x_test, y_test)) |