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cntk:rms-prop-learner

cntk:rms-prop-learner(
   $parameters as cntk:parameter*,
   $lr as cntk:schedule,
   $gamma as xs:double,
   $inc as xs:double,
   $dec as xs:double,
   $max as xs:double,
   $min as xs:double,
   [$unit-gain as xs:boolean],
   [$alo as map:map]
) as cntk:schedule

Summary

Constructs an RMSProp learner.

Parameters
$parameters
$lr Learning rate schedule.
$gamma
$inc
$dec
$max
$min
$unit-gain Unit gain.
$alo Additional learning options.

Example

  xquery version "1.0-ml";

  let $shape := (3, 3, 3)
  let $parameter1 := cntk:parameter(cntk:shape($shape), "float", cntk:random-initializer-with-rank(cntk:uniform-initializer(5.5), 4, 4), cntk:cpu(), "parameter1")
  let $parameter2 := cntk:parameter-from-value(cntk:value(cntk:shape($shape), json:to-array((1 to 27)), cntk:cpu(), "float", fn:false()), "parameter2")
  let $parameter3 := cntk:parameter-from-scalar(cntk:shape($shape), "float", 5.5, cntk:cpu(), "parameter3")
  let $lr := cntk:learning-rate-schedule-from-constant(0.01, 100)
  let $learner := cntk:rms-prop-learner(($parameter1, $parameter2, $parameter3), $lr, 0.1, 5.5, 0.5, 7.7, 1.1)
  return fn:replace(xdmp:quote($learner), "0x[0-9A-Za-z]*", "Learner")
  => cntk:learner(Name(cntk:learner))

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