Deploy Training of Shallow Neural Networks
Tip
To learn about code generation for deep learning, seeDeep Learning Code Generation.
UseMATLAB®俄文ntimeto deploy functions that can train a model. You can deploy MATLAB code that trains neural networks as described inCreate Standalone Application from MATLAB(MATLAB Compiler).
The following methods and functions are NOT supported in deployed mode:
Training progress dialog.
genFunction
andgensim
to generate MATLAB code or Simulink®blocksview
methodnctool
,nftool
,nnstart
,nprtool
,ntstool
Plot functions (such as
plotperform
,plottrainstate
,ploterrhist
,plotregression
,plotfit
, and so on)perceptron
,newlind
, andelmannet
functions.
Here is an example of how you can deploy training of a network. Create a script to train a neural network, for example,mynntraining.m
:
% Create the predictor and response (target)x = [0.054 0.78 0.13 0.47 0.34 0.79 0.53 0.6 0.65 0.75 0.084 0.91 0.83 0.53 0.93 0.57 0.012 0.16 0.31 0.17 0.26 0.69 0.45 0.23 0.15 0.54]; t = [0.46 0.079 0.42 0.48 0.95 0.63 0.48 0.51 0.16 0.51 1 0.28 0.3];% Create and display the networknet = fitnet(); disp('Training fitnet')% Train the network using the data in x and tnet = train(net,x,t);% Predict the responses using the trained networky = net(x);% Measure the performanceperf = perform(net,y,t)
Compile the scriptmynntraining.m
by using the command line:
mcc-m'mynntraining.m'
mcc
invokes theMATLAB Compiler™to compile code at the prompt. The flag–m
compiles a MATLAB function and generates a standalone executable. The EXE file is now in your local computer in the working directory.
To run the compiled EXE application on computers that do not have MATLAB installed, you need to download and install MATLAB Runtime. Thereadme.txt
created in your working folder has more information about the deployment requirements.