eval_model_fn,
loss_fn,
optimizer,
dataloader,
batch_preprocessing_fn,
log_interval=1)
test_loss[epoch - 1] = process_epoch('test',
epoch,
eval_model_fn,
loss_fn,
optimizer,
dataloader_test,
batch_preprocessing_fn,
log_interval=1)
# remember best loss
is_best = test_loss[epoch - 1] < best_test_loss
best_test_loss = min(test_loss[epoch - 1], best_test_loss)
save_checkpoint(
{
'epoch': epoch,
'args': args,
'state_dict': model.state_dict(),
'best_test_loss': best_test_loss,
'optimizer': optimizer.state_dict(),
'train_loss': train_loss,
'test_loss': test_loss,
}, is_best, checkpoint_name)
print('Done!')
)
test_loss[epoch - 1] = process_epoch(
"test",
epoch,
model,
loss_fn,
optimizer,
dataloader_test,
batch_preprocessing_fn=None,
log_interval=1,
)
# remember best loss
is_best = test_loss[epoch - 1] < best_test_loss
best_test_loss = min(test_loss[epoch - 1], best_test_loss)
save_checkpoint(
{
"epoch": epoch,
"args": args,
"state_dict": model.state_dict(),
"best_test_loss": best_test_loss,
"optimizer": optimizer.state_dict(),
"train_loss": train_loss,
"test_loss": test_loss,
},
is_best,
checkpoint_name,
)
print("Done!")
