'Perform updates to learning rate and potential other states at the
end of an iteration
Parameters
time_step : int
number of training samples trained on so far, used to update
learning rate for \'invscaling\''

def iteration_ends(self, time_step):

    if (self.lr_schedule == 'invscaling'):
        self.learning_rate = (float(self.learning_rate_init) / ((time_step + 1) ** self.power_t))
