Hi Marisa,
Please find the answers to your questions below:
- It is not possible to generate code for 1d CNNs or any other 1d layers currently with the coder products.
- In terms of a workaround to use 2d CNNs with 1d sequence data, we have an example that showcases that workflow for audio signals: https://www.mathworks.com/help/deeplearning/ug/deep-learning-speech-recognition.html The main idea is to convert the audio signal to an auditory spectogram image, so that the 2d CNNs can process that image representing the original 1d signal data. Depending on what data you have, a similar approach can be taken to convert the 1d data to equivalent 2d data to feed to 2d CNNs.
I am however curious to know why you want to use 1d CNNs? Does using lstmLayer/bilstmLayer/gruLayer not provide good results? While CMSIS-NN library only supports lstmLayer currently amongst the three mentioned above, you can generate plain C-code without using any libraries utilizing the "none" targetLibrary and deploy the generated code to STM32F427 board. Here is an example for that workflow: https://www.mathworks.com/help/coder/ug/generate-code-for-lstm-network-and-deploy-on-cortex-m.html Library-free codegeneration supports a wider range of layers as compared to CMSIS-NN library. You can invoke coder.getDeepLearningLayers("TargetLibrary", "none") to get the list of all supported layers.
3. It is a typo that mentions "C++" in the build steps of "CMSIS-NN" library. The library only supports "C" code. We will fix the post soon. Thank you for bringing this issue to our attention.
Hope this helps!
~Sayan
