When we increase the batch size, the number of iterations will eventually reduce, hence the number of evaluations will also be reduced. This can overfit the data for a large batch size. A batch size of 1 is as useless as a batch size based on an entire dataset. So, you need to experiment with values starting from a safe arbitrary point. 

A very small learning rate will lead to a very small convergence rate to the target. This can also impact the training time. If the learning rate is very large, this will cause divergent behavior in the model. We need to increase the learning rate until we observe the evaluation metrics getting better. There is an implementation of a cyclic learning rate in the fast.ai and Keras libraries; however, a cyclic learning rate is not implemented in DL4J.