epoch += 1 if epoch > options['num_epochs']: train_complete = True training = False # Load latest best model lesion_model.load_state_dict(torch.load(jp(path_models, "checkpoint.pt"))) except KeyboardInterrupt: # If training is stopped, load last model print("Training was stopped, loading last model...") lesion_model.load_state_dict(torch.load(jp(path_models, "checkpoint.pt"))) plot_learning_curve(train_losses, val_losses, the_title="Learning curve", measure="Loss (" + options["loss"] + ")", early_stopping=True, filename=jp(path_results, "loss_plot.png")) plot_learning_curve(train_jaccs, val_jaccs, the_title="Jaccard plot", measure="Jaccard", early_stopping=False, filename=jp(path_results, "jaccard_plot.png")) options['max_epoch_reached'] = epoch #plot_learning_curve(train_losses, val_losses, the_title="Learning curve", measure = "Loss (" + options["loss"] + ")", early_stopping = True, filename = jp(path_results, "loss_plot.png")) #plot_learning_curve(train_jaccs, val_jaccs, the_title="Jaccard plot", measure = "Jaccard", early_stopping = False, filename = jp(path_results, "jaccard_plot.png")) #Plot learning curve
epoch += 1 if epoch > options['num_epochs']: train_complete = True training = False # Load latest best model lesion_model.load_state_dict(torch.load(jp(path_models, "checkpoint.pt"))) except KeyboardInterrupt: # If training is stopped, load last model print("Training was stopped, loading last model...") lesion_model.load_state_dict(torch.load(jp(path_models, "checkpoint.pt"))) plot_learning_curve(train_losses, val_losses, the_title="Learning curve", measure="Loss (" + options["loss"] + ")", early_stopping=True, filename=jp(path_results, "loss_plot.png")) #plot_learning_curve(train_jaccs, val_jaccs, the_title="Jaccard plot", measure = "Jaccard", early_stopping = False, filename = jp(path_results, "jaccard_plot.png")) plot_learning_curve(train_accs, val_accs, the_title="Accuracy plot", measure="Accuraca", early_stopping=False, filename=jp(path_results, "acc_plot.png")) options['max_epoch_reached'] = epoch #Plot learning curve if train_complete: plot_learning_curve(train_losses,