You can export a Deep Learning Toolbox network or layer graph to TensorFlow and ONNX using the "exportNetworkToTensorFlow" and "exportONNXNetwork" functions, respectively. For more information about exporting networks to external deep learning platforms, please see the following documentation page:
Alternatively, you could export via the MATLAB Compiler SDK.
Using the MATLAB Compiler SDK, you can save the trained network as a MAT file, and write a MATLAB function that loads the network from the file, performs the desired computation, and returns the network's output.
You can then compile your MATLAB function into a shared library to be used in your C/C++, .NET, Java, or Python project.
You can find more information about MATLAB Compiler SDK in the following link:
Furthermore, the objects that MATLAB uses to represent neural networks are transparent, and you can therefore access all the information that describes your trained network.
For example, you will get an object of type "SeriesNetwork", which is a trained Convolutional Neural Network. You can then see the weights and biases of the trained network:
convnet.Layers(2).Weights
convnet.Layers(2).Bias
Then, using for example Caffe's MATLAB interface, you should be able to save a Convolutional Neural Network as a Caffe model. The code for the MATLAB interface is in the following link:
and includes a classification demo that shows you how to use the interface.
Please note that the above code is not developed or supported by MathWorks Technical Support. If you have any questions about how to use the code, please contact the project's developers.
