def train()

in src/trainer.py [0:0]

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

    def train(self):
        for epoch in range(self.config["epochs"]):
            t_start = time.time()
            loss_stats = {}
            for data in self.dataloader:
                loss_new = self.train_iteration(data)
                # logging
                for k, v in loss_new.items():
                    loss_stats[k] = loss_stats[k]+v if k in loss_stats else v
            for k in loss_stats:
                loss_stats[k] /= len(self.dataloader)
            self.optimizer.update_lr(loss_stats["accumulated_loss"])
            t_end = time.time()
            loss_str = "    ".join([f"{k}:{v:.4}" for k, v in loss_stats.items()])
            time_str = f"({time.strftime('%H:%M:%S', time.gmtime(t_end-t_start))})"
            print(f"epoch {epoch+1} " + loss_str + "        " + time_str)
            # Save model
            if self.config["save_frequency"] > 0 and (epoch + 1) % self.config["save_frequency"] == 0:
                self.save(suffix='epoch-' + str(epoch+1))
                print("Saved model")
        # Save final model
        self.save()