Hi Kyle,
GPU Coder only supports inference using deep learning networks currently. Training the network using coder products is not yet supported. Given that you are using pre-trained networks, you can leverage transfer learning capability in MATLAB to re-train the networks for your datasets.
Training the network can only be done using single precision in MATLAB currently. Can you elaborate on your requirements to train using double and half precision? I understand that half-precision is likely to improve the training time. Are there any other reasons?
For inference we do support quantized datatype with CuDNN and TensorRT libraries as documented in: https://www.mathworks.com/help/deeplearning/ug/quantize-a-residual-network-trained-for-image-classification-and-generate-cuda-code.html
https://www.mathworks.com/help/gpucoder/ug/tensorrt-target.html
With TensorRT library half-precision is also supported: https://www.mathworks.com/help/gpucoder/ug/code-generation-using-tensorrt.html#mw_c1f34b7f-7d7a-4fd8-ad98-be0d4212118e
~Sayan
