ClassificationOutputLayer

Classification layer

Description

A classification layer computes the cross-entropy loss for classification and weighted classification tasks with mutually exclusive classes.

Creation

Create a classification layer using classificationLayer.

Properties

Classification Output

ClassWeights — Class weights for weighted cross-entropy loss

'none' (default) | vector of positive numbers

Class weights for weighted cross-entropy loss, specified as a vector of positive numbers or 'none'.

For vector class weights, each element represents the weight for the corresponding class in the Classes property. To specify a vector of class weights, you must also specify the classes using 'Classes'.

If the ClassWeights property is 'none', then the layer applies unweighted cross-entropy loss.

Classes — Classes of the output layer

"auto" (default) | categorical vector | string array | cell array of character vectors

Classes of the output layer, specified as a categorical vector, string array, cell array of character vectors, or "auto". If Classes is "auto", then the software automatically sets the classes at training time. If you specify the string array or cell array of character vectors str, then the software sets the classes of the output layer to categorical(str,str).

Data Types: char | categorical | string | cell

OutputSize — Size of the output

'auto' (default) | positive integer

This property is read-only.

Size of the output, specified as a positive integer. This value is the number of labels in the data. Before the training, the output size is set to 'auto'.

LossFunction — Loss function for training

'crossentropyex'

This property is read-only.

Loss function for training, specified as 'crossentropyex', which stands for Cross Entropy Function for k Mutually Exclusive Classes.

Layer

Name — Layer name

"" (default) | character vector | string scalar

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 ClassificationOutputLayer object stores this property as a character vector.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

This property is read-only.

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

Data Types: double

InputNames — Input names

{'in'} (default)

This property is read-only.

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

Data Types: cell

NumOutputs — Number of outputs

0 (default)

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

Data Types: double

OutputNames — Output names

{} (default)

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

Data Types: cell

Examples

Create Classification Layer

Create a classification layer with the name 'output'.

layer = classificationLayer('Name','output')

layer = 
  ClassificationOutputLayer with properties:

            Name: 'output'
         Classes: 'auto'
    ClassWeights: 'none'
      OutputSize: 'auto'

   Hyperparameters
    LossFunction: 'crossentropyex'

Include a classification output layer in a Layer array.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer]

layers = 
  7x1 Layer array with layers:

     1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution         20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     5   ''   Fully Connected         10 fully connected layer
     6   ''   Softmax                 softmax
     7   ''   Classification Output   crossentropyex

Create Weighted Classification Layer

Create a weighted classification layer for three classes with names "cat", "dog", and "fish", with weights 0.7, 0.2, and 0.1, respectively.

classes = ["cat" "dog" "fish"];
classWeights = [0.7 0.2 0.1];

layer = classificationLayer( ...
    'Classes',classes, ...
    'ClassWeights',classWeights)

layer = 
  ClassificationOutputLayer with properties:

            Name: ''
         Classes: [cat    dog    fish]
    ClassWeights: [3x1 double]
      OutputSize: 3

   Hyperparameters
    LossFunction: 'crossentropyex'

Include a weighted classification output layer in a Layer array.

numClasses = numel(classes);

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer('Classes',classes,'ClassWeights',classWeights)]

layers = 
  7x1 Layer array with layers:

     1   ''   Image Input             28x28x1 images with 'zerocenter' normalization
     2   ''   2-D Convolution         20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   2-D Max Pooling         2x2 max pooling with stride [2  2] and padding [0  0  0  0]
     5   ''   Fully Connected         3 fully connected layer
     6   ''   Softmax                 softmax
     7   ''   Classification Output   Class weighted crossentropyex with 'cat' and 2 other classes

More About

Classification Output Layer

A classification layer computes the cross-entropy loss for classification and weighted classification tasks with mutually exclusive classes.

For typical classification networks, the classification layer usually follows a softmax layer. In the classification layer, trainNetwork takes the values from the softmax function and assigns each input to one of the K mutually exclusive classes using the cross entropy function for a 1-of-K coding scheme [1]:

$$\text{loss}=-\frac{1}{N}{\displaystyle \sum _{n=1}^N{\displaystyle \sum }_{i=1}^K}\text{}{w}_i{\text{t}}_{ni}\ln y_{ni},$$

where N is the number of samples, K is the number of classes, $$w_i$$ is the weight for class i, $$t_{ni}$$ is the indicator that the nth sample belongs to the ith class, and $$y_{ni}$$ is the output for sample n for class i, which in this case, is the value from the softmax function. In other words, $$y_{ni}$$ is the probability that the network associates the nth input with class i.

References

[1] Bishop, C. M. Pattern Recognition and Machine Learning. Springer, New York, NY, 2006.

Version History

Introduced in R2016a

R2018b: ClassNames property will be removed

ClassNames will be removed. Use Classes instead. To update your code, replace all instances of ClassNames with Classes. There are some differences between the properties that require additional updates to your code.

The ClassNames property of the output layer is a cell array of character vectors. The Classes property is a categorical array. To use the value of Classes with functions that require cell array input, convert the classes using the cellstr function.