ClassificationOutputLayer - (Not recommended) Classification output layer - MATLAB (original) (raw)

(Not recommended) Classification output layer

ClassificationOutputLayer objects are recommended. Use the trainnet function and set the loss function to "crossentropy" instead. For more information, see Version History.

Description

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

Properties

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Classification Output

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 the Classes option.

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

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

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'.

This property is read-only.

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

Layer

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork function automatically assigns names to layers with the name "".

The ClassificationOutputLayer object stores this property as a character vector.

Data Types: char | string

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

Data Types: double

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

Data Types: cell

Number of outputs of the layer, returned as 0. This layer has no outputs.

Data Types: double

Output names of the layer, returned as {}. This layer has no outputs.

Data Types: cell

Examples

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Create a 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

More About

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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]:

where N is the number of samples, K is the number of classes, wi is the weight for class i, tni is the indicator that sample n belongs to class i, and_yni_ is the output for sample_n_ for class i, which in this case, is the value from the softmax function. In other words, yni is the probability that the network associates observation n with class_i_.

References

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

Version History

Introduced in R2016a

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Starting in R2024a, ClassificationOutputLayer objects are not recommended, use the trainnet and set the loss function to "crossentropy" instead.

There are no plans to remove support for ClassificationOutputLayer objects. However, the trainnet function has these advantages and is recommended instead:

• trainnet supports dlnetwork objects, which support a wider range of network architectures that you can create or import from external platforms.
• trainnet enables you to easily specify loss functions. You can select from built-in loss functions or specify a custom loss function.
• trainnet outputs a dlnetwork object, which is a unified data type that supports network building, prediction, built-in training, visualization, compression, verification, and custom training loops.
• trainnet is typically faster than trainNetwork.

This table shows some typical usages of the trainNetwork function with ClassificationOutputLayer objects and how to update your code to use the trainnet function instead.

ClassNames will be removed. Use Classes instead. To update your code, replace all instances of ClassNames withClasses. 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.