genFunction

Generate MATLAB function for simulating shallow neural network

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Syntax

genFunction(net,pathname)
genFunction(___,'MatrixOnly','yes')
genFunction(___,'ShowLinks','no')

Description

This function generates a MATLAB® function for simulating a shallow neural network. genFunction does not support deep learning networks such as convolutional or LSTM networks. For more information on code generation for deep learning, see Deep Learning Code Generation.

example

genFunction(net,pathname) generates a complete stand-alone MATLAB function for simulating a neural network including all settings, weight and bias values, module functions, and calculations in one file. The result is a standalone MATLAB function file. You can also use this function with MATLAB Compiler™ and MATLAB Coder™ tools.

example

genFunction(___,'MatrixOnly','yes') overrides the default cell/matrix notation and instead generates a function that uses only matrix arguments compatible with MATLAB Coder tools. For static networks, the matrix columns are interpreted as independent samples. For dynamic networks, the matrix columns are interpreted as a series of time steps. The default value is 'no'.

genFunction(___,'ShowLinks','no') disables the default behavior of displaying links to generated help and source code. The default is 'yes'.

Examples

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This example shows how to create a MATLAB function and a MEX-function from a static neural network.

First, train a static network and calculate its outputs for the training data.

[x,t] = bodyfat_dataset;
bodyfatNet = feedforwardnet(10);
bodyfatNet = train(bodyfatNet,x,t);
y = bodyfatNet(x);

Next, generate and test a MATLAB function. Then the new function is compiled to a shared/dynamically linked library with mcc.

genFunction(bodyfatNet,'bodyfatFcn');
y2 = bodyfatFcn(x);
accuracy2 = max(abs(y-y2))
mcc -W lib:libBodyfat -T link:lib bodyfatFcn

Next, generate another version of the MATLAB function that supports only matrix arguments (no cell arrays), and test the function. Use the MATLAB Coder tool codegen to generate a MEX-function, which is also tested.

genFunction(bodyfatNet,'bodyfatFcn','MatrixOnly','yes');
y3 = bodyfatFcn(x);
accuracy3 = max(abs(y-y3))
 
x1Type = coder.typeof(double(0),[13 Inf]); % Coder type of input 1
codegen bodyfatFcn.m -config:mex -o bodyfatCodeGen -args {x1Type}
y4 = bodyfatodeGen(x);
accuracy4 = max(abs(y-y4))

This example shows how to create a MATLAB function and a MEX-function from a dynamic neural network.

First, train a dynamic network and calculate its outputs for the training data.

[x,t] = maglev_dataset;
maglevNet = narxnet(1:2,1:2,10);
[X,Xi,Ai,T] = preparets(maglevNet,x,{},t);
maglevNet = train(maglevNet,X,T,Xi,Ai);
[y,xf,af] = maglevNet(X,Xi,Ai);

Next, generate and test a MATLAB function. Use the function to create a shared/dynamically linked library with mcc.

genFunction(maglevNet,'maglevFcn');
[y2,xf,af] = maglevFcn(X,Xi,Ai);
accuracy2 = max(abs(cell2mat(y)-cell2mat(y2)))
mcc -W lib:libMaglev -T link:lib maglevFcn

Next, generate another version of the MATLAB function that supports only matrix arguments (no cell arrays), and test the function. Use the MATLAB Coder tool codegen to generate a MEX-function, which is also tested.

genFunction(maglevNet,'maglevFcn','MatrixOnly','yes');
x1 = cell2mat(X(1,:)); % Convert each input to matrix
x2 = cell2mat(X(2,:));
xi1 = cell2mat(Xi(1,:)); % Convert each input state to matrix
xi2 = cell2mat(Xi(2,:));
[y3,xf1,xf2] = maglevFcn(x1,x2,xi1,xi2);
accuracy3 = max(abs(cell2mat(y)-y3))
 
x1Type = coder.typeof(double(0),[1 Inf]); % Coder type of input 1
x2Type = coder.typeof(double(0),[1 Inf]); % Coder type of input 2
xi1Type = coder.typeof(double(0),[1 2]); % Coder type of input 1 states
xi2Type = coder.typeof(double(0),[1 2]); % Coder type of input 2 states
codegen maglevFcn.m -config:mex -o maglevNetCodeGen -args {x1Type x2Type xi1Type xi2Type}
[y4,xf1,xf2] = maglevNetCodeGen(x1,x2,xi1,xi2);
dynamic_codegen_accuracy = max(abs(cell2mat(y)-y4))

Input Arguments

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Neural network, specified as a network object.

Example: net = feedforwardnet(10);

Location and name of generated function file, specified as a character string. If you do not specify a file name extension of .m, it is automatically appended. If you do not specify a path to the file, the default location is the current working folder.

Example: 'myFcn.m'

Data Types: char

Extended Capabilities

See Also

Topics

Introduced in R2013b

Deep Learning Toolbox Documentation

Support