https://issues.apache.org/jira/browse/FLINK-1994
There are two ways to set the effective learning rate:
Method 1) Several pre-baked ways to calculate the effective learning rate,
set as a switch. E.g.:
val effectiveLearningRate = optimizationMethod match
{
// original effective learning rate method for backward compatability
case 0 => learningRate/Math.sqrt(iteration)
// These come straight from sklearn
case 1 => learningRate
case 2 => 1 / (regularizationConstant * iteration)
case 3 => learningRate / Math.pow(iteration, 0.5) ...
}
Method2) Make the calculation definable by the user. E.g. introduce a
function to the class which maybe overridden.
This is a classic trade-off between ease of use and functionality. Method 1
is easier for novice users/users who are migrating from sklearn. Method2
will be more extensible- letting users write any old effective learning
rate calculation they want.
I am leaning toward method 1 because how many people really are writing out
their own custom effective learning rate (as long as there is a fairly good
number of 'prebaked' calculators available, and because if someone really
wants to add a method, it simply requires adding another case.
I want to open this up in case anyone has an opinion, just in case.
Best,
tg
Trevor Grant
Data Scientist
https://github.com/rawkintrevo
http://stackexchange.com/users/3002022/rawkintrevo
*"Fortunate is he, who is able to know the causes of things." -Virgil*
