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cntk:local-response-normalization

cntk:local-response-normalization(
   $operand as cntk:variable,
   $depth-radius as xs:unsignedLong,
   $bias as xs:double,
   $alpha as xs:double,
   $beta as xs:double,
   [$name as xs:string]
) as cntk:function

Summary

Local Response Normalization layer. See Section 3.3 of the paper: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep- convolutional-neural-networks.pdf

Parameters
$operand The operand of the operation. Input of the Local Response Normalization.
$depth-radius The radius on the channel dimension to apply the normalization.
$bias A bias term to avoid divide by zero.
$alpha The alpha term of the above equation.
$beta The beta term of the above equation.
$name The name of the Function instance in the network.

Example

  xquery version "1.0-ml";

  let $input-variable1 := cntk:input-variable(cntk:shape((2,2,3)), "float")
  let $model := cntk:local-response-normalization($input-variable1, 1, 0.1, 1.0, 0.75)

  let $input-value1 := cntk:sequence(cntk:shape((2,2,3)),
    json:to-array(json:to-array((3, 5, 2, 6, 4, 2, 8, 3, 1, 6, 4, 7, 7, 3, 5, 9, 3, 5, 2, 6, 4, 2, 8, 3))),
    fn:true(), cntk:cpu(), "float")
  let $pair1 := json:to-array(($input-variable1, $input-value1))

  let $output-variable := cntk:function-output($model)
  let $output-value := cntk:evaluate($model, $pair1, $output-variable, cntk:cpu())
  return (
  "Value Shape : ", xdmp:quote(cntk:value-shape($output-value)), "
", 
  "Variable Owner : ", fn:replace(xdmp:quote(cntk:variable-owner($output-variable)), "Input\d*", "Input"), "
", 
  "Result : ", cntk:value-to-array($output-variable, $output-value))

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