tune.py
def optimization_loop(self, model):
        """Optimize the parameters based on suggestions."""
        for i in range(100):
            self.logger.info('Optimization Loop Count: %d', i)

            # assign suggestions to parameters and hyperparameters
            self.get_suggestions()

            # update model class
            self.model = model()
            self.update_parameters()
            self.model.build_model()

            # update training class
            train = TrainModel(self.model, n_epochs=200, batch_size=128)

            # run the training stuff
            self.acc = train.train_model()
            train.reset_model()

            # report to SigOpt
            self.report_observation()
Optimize the parameters based on suggestions.
Optimize the parameters based on suggestions.
Optimize the parameters based on suggestions .
def optimization_loop(self, model):
        for i in range(100):
            self.logger.info('Optimization Loop Count: %d', i)
            self.get_suggestions()
            self.model = model()
            self.update_parameters()
            self.model.build_model()
            train = TrainModel(self.model, n_epochs=200, batch_size=128)
            self.acc = train.train_model()
            train.reset_model()
            self.report_observation()
def optimization_loop ( self , model ) : for i in range ( 100 ) : self . logger . info ( 'Optimization Loop Count: %d' , i ) self . get_suggestions ( ) self . model = model ( ) self . update_parameters ( ) self . model . build_model ( ) train = TrainModel ( self . model , n_epochs = 200 , batch_size = 128 ) self . acc = train . train_model ( ) train . reset_model ( ) self . report_observation ( )
Optimize the parameters based on suggestions.
Optimize the parameters based on suggestions .
"""Optimize the parameters based on suggestions.""" # assign suggestions to parameters and hyperparameters # update model class # update training class # run the training stuff # report to SigOpt
self
model