Its best to split your dataset into 60-20-20 (as train-validation-test). Use the 20% validation for early stopping and choosing the right learning rate. Once you have the best model - use the test 20% to compute the final Precision - Recall - F1 scores.
One way to choose the right learning rate - start high - and gradually decrease if your loss doesn’t decrease after a certain epoch. Also you need to play with your other hyper-parameters like momentum.