Consider the following experiments to illustrate step 1.

The following training was performed on 10,000 records with a batch size of 8 and a learning rate of 0.008:

The following is the evaluation performed on the same dataset for a batch size of 50 and a learning rate of 0.008:

To perform step 2, we increased the learning rate to 0.6, to observe the results. Note that a learning rate beyond a certain limit will not help efficiency in any way. Our job is to find that limit:

You can observe that Accuracy is reduced to 82.40% and F1 Score is reduced to 20.7%. This indicates that F1 Score might be the evaluation parameter to be accounted for in this model. This is not true for all models, and we reach this conclusion after experimenting with a couple of batch sizes and learning rates. In a nutshell, you have to repeat the same process for your model's training and choose arbitrary values that yield the best results.