Colby_Tunick:

Since last post, I’ve made alot of progress (saved my models weights using torch.save(model.state_dict(), ‘.pth’) and have been able to load it in .eval() in a new file).


That sounds like a great progress!



 Colby_Tunick:

how to change my eval (formerly training loop) to iterate over the new data set just one time to produce a binary prediction?


I assume your model outputs a single logit, since you are dealing with a binary prediction?
If that’s the case, this dummy code might be a good starter:
model.eval()
preds = []
with torch.no_grad():
  y_val = model(inputCatColumns, inputNumColumns)
  preds.append(y_val > 0.) # if y_val are logits
  loss = loss_function(y_val, test_outputs)
  print(f'Loss: {loss:.8f}')
preds = torch.stack(preds)

Let me know, if that helps or if I misunderstood your question.