I am building an
LSTM for price prediction using
Keras. I am using Bayesian optimization to find the right hyperparameters. With every test I make, Bayesian optimization is always finding that the best
2 from a possible range of
[2, 4, 8, 32, 64], and always better results with no hidden layers. I have 5 features and ~1280 samples for the test I am trying.
Why is this the case? Is the lack of hidden layers due to the fact that I do not have many inputs and samples?
Also, literature always seems to suggest that a batch size of 2 is very low and a middle ground for the optimal
batch_size is 32. How can I interpret this result?