  The performance of neural network classifiers is determined by a number of
hyperparameters, including learning rate, batch size, and depth. A number of
attempts have been made to explore these parameters in the literature, and at
times, to develop methods for optimizing them. However, exploration of
parameter spaces has often been limited. In this note, I report the results of
large scale experiments exploring these different parameters and their
interactions.
