I’m brand new to deep learning and I’m curious as to the principles that inform manipulation of the CNN architecture as well as tuning hyperparameters. I have a test case of 11383 (200x200x3) training samples of 5 classes which I am modifying to 200x200x1.  What would be the ideal starting point for architecture design/parameters as well as batch size and other hyperparameters based on information presented herein and my prior post?