When you say 10000 train datasets do you mean images? If so - then very simply if you are uploading 1000 images for each class (total 4 classes). Then you will have 2,400 images for training, 800 for validation and 800 for testing.
Yes you can start with 0.1 and gradually reduce it based on how your model is performing. It is better to go with Precision and Recall - Python already has functions to generate classification report. Also F1 gives you an average based on Precision and Recall (harmonic mean basically).
Read up on the various hyper-parameters - they matter a lot in the process of obtaining the best model.