Greetings,
I traing MLPRegressors using small datasets, usually with 10-50
observations. The default batch_size=min(200, n_samples) for the adam
optimizer, and because my n_samples is always < 200, it is eventually
batch_size=n_samples. According to the theory, stochastic gradient-based
optimizers like adam perform better in the small batch regime. Considering
the above, what would be a good batch_size value in my case (e.g. 4)? Is
there any rule of thump to select the batch_size when the n_samples is
small or must the choice be based on trial and error?
--
======================================================================
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
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