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   $output as cntk.variable,
   $targets as cntk.variable,
   [$name as String]
) as cntk.function


This operation computes the sum of the squared difference between elements in the two input matrices. The result is a scalar (i.e., one by one matrix). This is often used as a training criterion.

$output The output values from the network.
$targets It is usually a one-hot vector where the hot bit corresponds to the label index.
$name The name of the function instance in the network.


  var inputVariable1 = cntk.inputVariable(cntk.shape([3]), "float", fn.false(),
    fn.false(), "feature")
  var inputVariable2 = cntk.inputVariable(cntk.shape([3]), "float", fn.false(),
    fn.false(), "feature")
  cntk.squaredError(inputVariable2, inputVariable2, "xz[t^+^sK*")
  => cntk.function(Composite SquaredError (Input(Name(feature), Shape([3]),
  Dynamic Axes([Sequence Axis(Default Dynamic Axis), Batch Axis(Default Batch Axis)]))))

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