def update_lr()

in src/utils.py [0:0]

• 19 lines of code
• 7 McCabe index (conditional complexity)

    def update_lr(self, loss):
        '''
        update the learning rate based on the current loss value and historic loss values
        :param loss: the loss after the current iteration
        '''
        if loss > self.last_epoch_loss and self.decay < 1.0 and self.total_decay > self.max_decay:
            self.total_decay = self.total_decay * self.decay
            print(f"NewbobAdam: Decay learning rate (loss degraded from {self.last_epoch_loss} to {loss})."
                  f"Total decay: {self.total_decay}")
            # restore previous network state
            self.net.load(self.artifacts_dir, suffix="newbob")
            # decrease learning rate
            for param_group in self.param_groups:
                param_group['lr'] = param_group['lr'] * self.decay
        else:
            self.last_epoch_loss = loss
        # save last snapshot to restore it in case of lr decrease
        if self.decay < 1.0 and self.total_decay > self.max_decay:
            self.net.save(self.artifacts_dir, suffix="newbob")