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Create a regression output layer


routputlayer = regressionLayer
routputlayer = regressionLayer('Name',Name)


routputlayer = regressionLayer returns a regression output layer for a neural network as a RegressionOutputLayer object.

Predict responses of a trained regression network using predict. Normalizing the responses often helps stabilizing and speeding up training of neural networks for regression. For more information, see Train Convolutional Neural Network for Regression.


routputlayer = regressionLayer('Name',Name) returns a regression layer with the name specified by Name.


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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   ''   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

Input Arguments

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Layer name, specified as the comma-separated pair consisting of 'Name' and a character vector. If you do not specify a name, then the software initially specifies the default value '', and automatically assigns the name 'regressionoutputlayer' at training time.

Example: 'Name','routput'

Data Types: char

Output Arguments

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Regression output layer, returned as a RegressionOutputLayer object.

Introduced in R2017a

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