Hi @ptrblck - duly noted! 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).
I am confused how to change my eval (formerly training loop) to iterate over the new data set just one time to produce a binary prediction? Any advice you could give is greatly appreciated. Cheers!
epochs = 1
aggregated_losses = []

for x in range(epochs):
    x += 1
    y_pred = model(inputCatColumns, inputNumColumns)
    single_loss = loss_function(y_pred, train_outputs)
    aggregated_losses.append(single_loss)

    if x%25 == 1:
        print(f'epoch: {x:3} loss: {single_loss.item():10.8f}')

    optimizer.zero_grad()
    single_loss.backward()
    optimizer.step()
    #scheduler.step(x)

print(f'epoch: {x:3} loss: {single_loss.item():10.10f}')

with torch.no_grad():
  y_val = model(inputCatColumns, inputNumColumns)
  loss = loss_function(y_val, test_outputs)
  print(f'Loss: {loss:.8f}')