neural_network.py
def step_annealing(self,current_iteration):
        """Reduces the learning rate by step_factor for in a step like fashion"""
        [reduction_interval, step_factor] = self.hyper_parameters
        num_epochs = np.floor((self.batch_size*current_iteration)/self.num_examples)
        return self.epsilon * step_factor**np.ceil(num_epochs/reduction_interval)
Reduces the learning rate by step_factor for in a step like fashion
Reduces the learning rate by step_factor for in a step like fashion
Reduces the learning rate by step_factor for in a step like fashion
def step_annealing(self,current_iteration):
        [reduction_interval, step_factor] = self.hyper_parameters
        num_epochs = np.floor((self.batch_size*current_iteration)/self.num_examples)
        return self.epsilon * step_factor**np.ceil(num_epochs/reduction_interval)
def step_annealing ( self , current_iteration ) : [ reduction_interval , step_factor ] = self . hyper_parameters num_epochs = np . floor ( ( self . batch_size * current_iteration ) / self . num_examples ) return self . epsilon * step_factor ** np . ceil ( num_epochs / reduction_interval )
Reduces the learning rate by step_factor for in a step like fashion
Reduces the learning rate by step_factor for in a step like fashion
"""Reduces the learning rate by step_factor for in a step like fashion"""
self
current_iteration