feedforwardnet

(To be removed) Generate feedforward neural network

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feedforwardnet 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 = feedforwardnet(hiddenSizes,trainFcn)

Description

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

Feedforward networks consist of a series of layers. The first layer has a connection from the network input. Each subsequent layer has a connection from the previous layer. The final layer produces the network’s output.

You can use feedforward networks for any kind of input to output mapping. A feedforward network with one hidden layer and enough neurons in the hidden layers can fit any finite input-output mapping problem.

Specialized versions of the feedforward network include fitting and pattern recognition networks.

A variation on the feedforward network is the cascade forward network, which has additional connections from the input to every layer, and from each layer to all following layers.

example

Examples

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Open Live Script

This example shows how to use a feedforward neural network to solve a simple problem.

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 feedforward network with one hidden layer of size 10. 

net = feedforwardnet(10);

Train the network net using the training data.

net = train(net,x,t);

Figure Neural Network Training (24-Jan-2026 18:27:25) contains an object of type uigridlayout.

View the trained network.

view(net)

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

Input Arguments

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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

Training function name, specified as one of the following.

Training FunctionAlgorithm
'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

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Feedforward neural network, returned as a network object.

Version History

Introduced in R2010b

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See Also

| | | (Statistics and Machine Learning Toolbox) | (Statistics and Machine Learning Toolbox) | | |

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