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Regression output layer


A regression layer computes the half-mean-squared-error loss for regression tasks.


Create a regression output layer using regressionLayer.


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Regression Output

Names of the responses, specified a cell array of character vectors or a string array. At training time, the software automatically sets the response names according to the training data. The default is {}.

Data Types: cell

Loss function the software uses for training, specified as 'mean-squared-error'.


Layer name, specified as a character vector or a string scalar. For Layer array input, the trainnet, trainNetwork, assembleNetwork, layerGraph, and dlnetwork functions automatically assign names to layers with the name "".

The RegressionOutputLayer object stores this property as a character vector.

Data Types: char | string

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

Number of outputs of the layer. The layer has no outputs.

Data Types: double

Output names of the layer. The layer has no outputs.

Data Types: cell


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Create a regression output layer with the name 'routput'.

layer = regressionLayer('Name','routput')
layer = 
  RegressionOutputLayer with properties:

             Name: 'routput'
    ResponseNames: {}

     LossFunction: 'mean-squared-error'

The default loss function for regression is mean-squared-error.

Include a regression output layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
layers = 
  5x1 Layer array with layers:

     1   ''   Image Input         28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution     25 12x12 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                ReLU
     4   ''   Fully Connected     1 fully connected layer
     5   ''   Regression Output   mean-squared-error

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Version History

Introduced in R2017a