MeanSquaredLossLayer

MeanSquaredLossLayer[]

represents a loss layer that computes the mean squared loss between its "Input" port and "Target" port.

Details and Options

  • MeanSquaredLossLayer exposes the following ports for use in NetGraph etc.:
  • "Input"a tensor of arbitrary rank
    "Target"a tensor of the same rank as "Input"
    "Loss"a real number
  • MeanSquaredLossLayer[…][<|"Input"->in,"Target"target|>] explicitly computes the output from applying the layer.
  • MeanSquaredLossLayer[…][<|"Input"->{in1,in2,…},"Target"->{target1,target2,…}|>] explicitly computes outputs for each of the ini and targeti.
  • MeanSquaredLossLayer is typically used inside NetGraph to construct a training network.
  • A MeanSquaredLossLayer[…] can be provided as the third argument to NetTrain when training a specific network.
  • When appropriate, MeanSquaredLossLayer is automatically used by NetTrain if an explicit loss specification is not provided.
  • MeanSquaredLossLayer["port"->shape] allows the shape of the given input "port" to be specified. Possible forms for shape include:
  • "Real"a single real number
    na vector of length n
    {n1,n2,…}a tensor of dimensions n1×n2×…
    "Varying"a variable-length vector
    {"Varying",n2,n3,…}a variable-length sequence of tensors of dimensions n2×n3×…

Examples

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Basic Examples  (3)

Create a MeanSquaredLossLayer:

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Create a MeanSquaredLossLayer that takes length-3 vectors:

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Apply the layer to data:

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Create a NetGraph containing a MeanSquaredLossLayer:

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Apply the net to input data:

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Scope  (4)

Applications  (1)

Properties & Relations  (2)

Possible Issues  (1)

See Also

MeanAbsoluteLossLayer  CrossEntropyLossLayer  NetGraph  NetTrain  SquaredEuclideanDistance

Introduced in 2016
(11.0)