hextop

(To be removed) Hexagonal layer topology function

hextop 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

hextop(dimensions)


Description

hextop calculates the neuron positions for layers whose neurons are arranged in an N-dimensional hexagonal pattern.

hextop(dimensions) takes one argument:

dimensions

Row vector of dimension sizes

and returns an N-by-S matrix of N coordinate vectors where N is the number of dimensions and S is the product of dimensions.

Examples

Display Layer with Hexagonal Pattern

This example shows how to display a two-dimensional layer with 40 neurons arranged in an 8-by-5 hexagonal pattern.

pos = hextop([8 5]);
plotsom(pos)

Version History

Introduced before R2006a

R2026a: hextop will be removed

hextop 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.