losses[epoch - 1, counter] = loss
# Gather reconstruction losses
recon_loss_list = [
recon_loss_mean, recon_loss_mean_validate, recon_loss_mean_test
]
for counter, loss in enumerate(recon_loss_list):
recon_losses[epoch - 1, counter] = loss
# Save losses
torch.save({
'loss': losses,
'recon_loss': recon_losses,
}, args.losses_path + '_losses.pth')
# Save best weights (mean validation loss)
if recon_loss_mean_validate < cur_best_valid_recons:
cur_best_valid_recons = recon_loss_mean_validate
learn.save(model, args, 'reconstruction')
# Save best weights (mean validation loss)
if loss_mean_validate < cur_best_valid:
cur_best_valid = loss_mean_validate
learn.save(model, args, 'full')
early_stop = 0
elif args.early_stop > 0:
early_stop += 1
if early_stop > args.early_stop:
print('[Model stopped early]')
break
# Track on stuffs
print("*******" * 10)
print('* Useful & incredible tracking:')
t = Texttable()
t.add_rows(
