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| author | Ognjen Cirkovic <ciraboxkg@gmail.com> | 2022-04-09 14:27:06 +0200 |
|---|---|---|
| committer | Ognjen Cirkovic <ciraboxkg@gmail.com> | 2022-04-09 14:27:06 +0200 |
| commit | ac55fd20fb1cfa3090c67aa11f4901cac5ee8384 (patch) | |
| tree | f3f3e80381bcc2a3cf2b5224851b44372ffebee6 /backend/microservice/api/newmlservice.py | |
| parent | b230b345d07d31b5cee2bc331368a21a6f789cdb (diff) | |
| parent | bf4e38854847c133244cd70f89e899116a7d1a60 (diff) | |
Merge branch 'dev' of http://gitlab.pmf.kg.ac.rs/igrannonica/neuronstellar into dev
Diffstat (limited to 'backend/microservice/api/newmlservice.py')
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 25 |
1 files changed, 25 insertions, 0 deletions
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 02f2ad6d..a92307c5 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -222,6 +222,31 @@ def train(dataset, params, callback): classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj classifier.add(tf.keras.layers.Dense(units=5, activation=params['outputLayerActivationFunction']))#izlazni sloj + if(params['optimizer']=='Adam'): + opt=tf.keras.optimizers.Adam(learning_rate=params['learningRate']) + + elif(params['optimizer']=='Adadelta'): + opt=tf.keras.optimizers.Adadelta(learning_rate=params['learningRate']) + + elif(params['optimizer']=='Adagrad'): + opt=tf.keras.optimizers.Adagrad(learning_rate=params['learningRate']) + + elif(params['optimizer']=='Adamax'): + opt=tf.keras.optimizers.Adamax(learning_rate=params['learningRate']) + + elif(params['optimizer']=='Nadam'): + opt=tf.keras.optimizers.Nadam(learning_rate=params['learningRate']) + + elif(params['optimizer']=='SGD'): + opt=tf.keras.optimizers.SGD(learning_rate=params['learningRate']) + + elif(params['optimizer']=='Ftrl'): + opt=tf.keras.optimizers.Ftrl(learning_rate=params['learningRate']) + + elif(params['optimizer']=='RMSprop'): + opt=tf.keras.optimizers.RMSprop(learning_rate=params['learningRate']) + + classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics']) history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize']) |