isconfigured

(To be removed) Indicate if network inputs and outputs are configured

isconfigured 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

[flag,inputflags,outputflags] = isconfigured(net)


Description

[flag,inputflags,outputflags] = isconfigured(net) takes a neural network and returns three values,

flag	True if all network inputs and outputs are configured (have non-zero sizes)
inputflags	Vector of true/false values for each configured/unconfigured input
outputflags	Vector of true/false values for each configured/unconfigured output

Examples

Here are the flags returned for a new network before and after being configured:

net = feedforwardnet;
[flag,inputFlags,outputFlags] = isconfigured(net)
[x,t] = simplefit_dataset;
net = configure(net,x,t);
[flag,inputFlags,outputFlags] = isconfigured(net)

Version History

Introduced in R2010b

R2026a: isconfigured will be removed

isconfigured 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.