Minimal and clean training and evaluation codes for baseline performances.
- Learning Rate Scheduling is implemented with torch.optim.lr_Scheduler
- Tensorboard visualization is added
- Early stopping is implemented
- Best parameters for validation accuracy is saved
- Confusion matrix for validation set is generated
- Optuna hyper parameter search framework is used to find best parameters
