fitnet

(To be removed) Function fitting neural network

fitnet 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

net = fitnet(hiddenSizes)

net = fitnet(hiddenSizes,trainFcn)

Description

net = fitnet(hiddenSizes) returns a function fitting neural network with a hidden layer size of hiddenSizes.

net = fitnet(hiddenSizes,trainFcn) returns a function fitting neural network with a hidden layer size of hiddenSizes and training function, specified by trainFcn.

Examples

Construct and Train a Function Fitting Network

Load the training data.

[x,t] = simplefit_dataset;

The 1-by-94 matrix x contains the input values and the 1-by-94 matrix t contains the associated target output values.

Construct a function fitting neural network with one hidden layer of size 10.

net = fitnet(10);

View the network.

view(net)

The sizes of the input and output are zero. The software adjusts the sizes of these during training according to the training data.

Train the network net using the training data.

net = train(net,x,t);

View the trained network.

view(net)

You can see that the sizes of the input and output are 1.

Estimate the targets using the trained network.

y = net(x);

Assess the performance of the trained network. The default performance function is mean squared error.

perf = perform(net,y,t)

perf = 1.4639e-04

The default training algorithm for a function fitting network is Levenberg-Marquardt ( 'trainlm' ). Use the Bayesian regularization training algorithm and compare the performance results.

net = fitnet(10,'trainbr');
net = train(net,x,t);

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

perf = 3.3416e-10

The Bayesian regularization training algorithm improves the performance of the network in terms of estimating the target values.

Input Arguments

hiddenSizes — Size of the hidden layers

10 (default) | row vector

Size of the hidden layers in the network, specified as a row vector. The length of the vector determines the number of hidden layers in the network.

Example: For example, you can specify a network with 3 hidden layers, where the first hidden layer size is 10, the second is 8, and the third is 5 as follows: [10,8,5]

The input and output sizes are set to zero. The software adjusts the sizes of these during training according to the training data.

Data Types: single | double

trainFcn — Training function name

'trainlm' (default) | 'trainbr' | 'trainbfg' | 'trainrp' | 'trainscg' | ...

Training function name, specified as one of the following.

Training Function	Algorithm
'trainlm'	Levenberg-Marquardt
'trainbr'	Bayesian Regularization
'trainbfg'	BFGS Quasi-Newton
'trainrp'	Resilient Backpropagation
'trainscg'	Scaled Conjugate Gradient
'traincgb'	Conjugate Gradient with Powell/Beale Restarts
'traincgf'	Fletcher-Powell Conjugate Gradient
'traincgp'	Polak-Ribiére Conjugate Gradient
'trainoss'	One Step Secant
'traingdx'	Variable Learning Rate Gradient Descent
'traingdm'	Gradient Descent with Momentum
'traingd'	Gradient Descent

Example: For example, you can specify the variable learning rate gradient descent algorithm as the training algorithm as follows: 'traingdx'

Data Types: char

Output Arguments

net — Function fitting network

network object

Function fitting network, returned as a network object.

Tips

• Function fitting is the process of training a neural network on a set of inputs in order to produce an associated set of target outputs. After you construct the network with the desired hidden layers and the training algorithm, you must train it using a set of training data. Once the neural network has fit the data, it forms a generalization of the input-output relationship. You can then use the trained network to generate outputs for inputs it was not trained on.

Version History

Introduced in R2010b

R2026a: fitnet will be removed

fitnet 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.