lvqnet

(To be removed) Learning vector quantization neural network

lvqnet 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

lvqnet(hiddenSize,lvqLR,lvqLF)


Description

LVQ (learning vector quantization) neural networks consist of two layers. The first layer maps input vectors into clusters that are found by the network during training. The second layer merges groups of first layer clusters into the classes defined by the target data.

The total number of first layer clusters is determined by the number of hidden neurons. The larger the hidden layer the more clusters the first layer can learn, and the more complex mapping of input to target classes can be made. The relative number of first layer clusters assigned to each target class are determined according to the distribution of target classes at the time of network initialization. This occurs when the network is automatically configured the first time train is called, or manually configured with the function configure, or manually initialized with the function init is called.

lvqnet(hiddenSize,lvqLR,lvqLF) takes these arguments,

hiddenSize	Size of hidden layer (default = 10)
lvqLR	LVQ learning rate (default = 0.01)
lvqLF	LVQ learning function (default = 'learnlv1')

and returns an LVQ neural network.

The other option for the lvq learning function is learnlv2.

Examples

Train a Learning Vector Quantization Network

This example shows how to train an LVQ network to classify iris flowers.

[x,t] = iris_dataset;
net = lvqnet(10);
net.trainParam.epochs = 50;
net = train(net,x,t);

view(net)

y = net(x);
perf = perform(net,y,t)

perf = 
0.0489

classes = vec2ind(y);

Version History

Introduced in R2010b

R2026a: lvqnet will be removed

lvqnet 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.