Tip
To learn about code generation for deep learning, see Deep Learning Code Generation.
Use MATLAB® Runtime to deploy functions that can train a model. You can deploy MATLAB code that trains neural networks as described in Create Standalone Applications from the Command Line (MATLAB Compiler).
The following methods and functions are NOT supported in deployed mode:
Training progress dialog, nntraintool.
genFunction and gensim to generate MATLAB code or Simulink® blocks
view method
nctool, nftool, nnstart,
nprtool, ntstool
Plot functions (such as plotperform, plottrainstate, ploterrhist, plotregression, plotfit, and so on)
perceptron, newlind, and
elmannet 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 network net = fitnet(); disp('Training fitnet') % Train the network using the data in x and t net = train(net,x,t); % Predict the responses using the trained network y = net(x); % Measure the performance perf = perform(net,y,t)
Compile the script mynntraining.m by using the command line:
mcc -m 'mynntraining.m'
mcc invokes the MATLAB
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. The readme.txt created in your working folder has
more information about the deployment requirements.