competlayer

(To be removed) Competitive layer

competlayer will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

competlayer(numClasses,kohonenLR,conscienceLR)


Description

Competitive layers learn to classify input vectors into a given number of classes, according to similarity between vectors, with a preference for equal numbers of vectors per class.

competlayer(numClasses,kohonenLR,conscienceLR) takes these arguments,

numClasses	Number of classes to classify inputs (default = 5)
kohonenLR	Learning rate for Kohonen weights (default = 0.01)
conscienceLR	Learning rate for conscience bias (default = 0.001)

and returns a competitive layer with numClasses neurons.

Examples

Create and Train a Competitive Layer

This example shows how to train a competitive layer to classify 150 iris flowers into 6 classes.

inputs = iris_dataset;
net = competlayer(6);
net = train(net,inputs);

view(net)

outputs = net(inputs);
classes = vec2ind(outputs);

Version History

Introduced in R2010b

R2026a: competlayer will be removed

competlayer will be removed in a future release.

To train a neural network with tabular data, use one of these functions:

• fitrnet (Statistics and Machine Learning Toolbox) — Train regression neural network. Use this function for simple regression tasks.

• fitcnet (Statistics and Machine Learning Toolbox) — Train classification neural network. Use this function for simple classification tasks.

• trainnet — Train neural network. Use this function when the fitrnet and fitcnet functions do not provide the training options that you need for your task.

To train a neural network with time series data, use one of these options:

• Time Series Modeler app — Interactively design and train neural networks for time series data.

• trainnet — Train neural network. Use this function when you need more flexibility in the network architecture or training algorithm.

To design and customize your own neural network for these workflows, you can create a network using an array of deep learning layers or a dlnetwork object. To create and edit neural networks interactively and generate code, use the Deep Network Designer app. To learn more about neural network building workflows, see these examples and topics:

• Build Networks with Deep Network Designer

• List of Deep Learning Layers

• Example Deep Learning Network Architectures

• Multiple-Input and Multiple-Output Networks

• Define Custom Deep Learning Layers

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.