MeanAbsoluteLossLayer

MeanAbsoluteLossLayer[]

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

Details and Options

  • MeanAbsoluteLossLayer exposes the following ports for use in NetGraph etc.:
  • "Input"an array of arbitrary rank
    "Target"an array of the same rank as "Input"
    "Loss"a real number
  • MeanAbsoluteLossLayer[…][<|"Input" -> in, "Target"target|>] explicitly computes the output from applying the layer.
  • MeanAbsoluteLossLayer[…][<|"Input"->{in1,in2,…},"Target"->{target1,target2,…}|>] explicitly computes outputs for each of the ini and targeti.
  • When given a NumericArray as input, the output will be a NumericArray.
  • MeanAbsoluteLossLayer is typically used inside NetGraph to construct a training network.
  • A MeanAbsoluteLossLayer[…] can be provided as the third argument to NetTrain when training a specific network.
  • MeanAbsoluteLossLayer["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,…}an array of dimensions n1×n2×…
    "Varying"a vector whose length is variable
    {"Varying",n2,n3,…}an array whose first dimension is variable and whose remaining dimensions are n2×n3×…
    NetEncoder[…]an encoder
    NetEncoder[{…,"Dimensions"{n1,…}}]an encoder mapped over an array of dimensions n1×…
  • Options[MeanAbsoluteLossLayer] gives the list of default options to construct the layer. Options[MeanAbsoluteLossLayer[…]] gives the list of default options to evaluate the layer on some data.
  • Information[MeanAbsoluteLossLayer[…]] gives a report about the layer.
  • Information[MeanAbsoluteLossLayer[…],prop] gives the value of the property prop of MeanAbsoluteLossLayer[…]. Possible properties are the same as for NetGraph.

Examples

open allclose all

Basic Examples  (3)

Create a MeanAbsoluteLossLayer layer:

Create a MeanAbsoluteLossLayer that takes length-3 vectors:

Apply the layer to data:

Create a NetGraph containing a MeanAbsoluteLossLayer:

Apply the net to input data:

Scope  (4)

Arguments  (1)

Create a MeanAbsoluteLossLayer:

Apply the MeanAbsoluteLossLayer to a pair of matrices:

Apply the MeanAbsoluteLossLayer to a pair of vectors:

Apply the MeanAbsoluteLossLayer to a pair of numbers:

Ports  (3)

Create a MeanAbsoluteLossLayer that assumes the input data are vectors of length 2:

Thread the layer across a batch of inputs:

Create a MeanAbsoluteLossLayer that takes two variable-length vectors:

Apply the layer to an input and target vector:

Thread the layer over a batch of input and target vectors:

Create a MeanAbsoluteLossLayer that takes two images as input:

Apply the layer to two dissimilar images:

Apply the layer to two dissimilar images:

Applications  (1)

Define a single-layer neural network that takes in scalar numeric values and produces scalar numeric values, and train this network using a MeanAbsoluteLossLayer:

Predict the value of a new input:

Properties & Relations  (2)

MeanAbsoluteLossLayer computes:

Compare the output of the layer and the definition on an example:

MeanAbsoluteLossLayer effectively computes a normalized version of ManhattanDistance:

Wolfram Research (2016), MeanAbsoluteLossLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html (updated 2019).

Text

Wolfram Research (2016), MeanAbsoluteLossLayer, Wolfram Language function, https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html (updated 2019).

CMS

Wolfram Language. 2016. "MeanAbsoluteLossLayer." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2019. https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html.

APA

Wolfram Language. (2016). MeanAbsoluteLossLayer. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html

BibTeX

@misc{reference.wolfram_2024_meanabsolutelosslayer, author="Wolfram Research", title="{MeanAbsoluteLossLayer}", year="2019", howpublished="\url{https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html}", note=[Accessed: 03-December-2024 ]}

BibLaTeX

@online{reference.wolfram_2024_meanabsolutelosslayer, organization={Wolfram Research}, title={MeanAbsoluteLossLayer}, year={2019}, url={https://reference.wolfram.com/language/ref/MeanAbsoluteLossLayer.html}, note=[Accessed: 03-December-2024 ]}