# 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 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 n a 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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