ClassificationOutputLayer

Classification layer

Description

A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.

Creation

Create a classification layer using classificationLayer.

Properties

Classification Output

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). The default value is 'auto'.

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. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char | string

NumInputs — Number of inputs

1 (default)

Number of inputs of the layer. This layer accepts a single input only.

Data Types: double

InputNames — Input names

{'in'} (default)

Input names of the layer. 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'
      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   ''   Convolution             20 5x5 convolutions with stride [1  1] and padding [0  0  0  0]
     3   ''   ReLU                    ReLU
     4   ''   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

Classification Output Layer

A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.

For typical classification networks, the classification layer must follow the 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]:

$$\text{loss}=-{\displaystyle \sum _{i=1}^N{\displaystyle \sum }_{j=1}^K}\text{}{\text{t}}_{ij}\ln y_{ij},$$

where N is the number of samples, K is the number of classes, $$t_{ij}$$ is the indicator that the ith sample belongs to the jth class, and $$y_{ij}$$ is the output for sample i for class j, which in this case, is the value from the softmax function. That is, it is the probability that the network associates the ith input with class j.

Compatibility Considerations

ClassNames property will be removed

Not recommended starting in R2018b

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.

References

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