I'm trying to Implement basic MNIST Handwritten Digit recognition Model on STM32F769 DISC1 kit using STM Cube AI.
Here are the steps I followed.
- Took a standard MNIST Digit recognition model from here https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/digits_recognition_cnn/digits_recognition_cnn.ipynb
It is provided by google colab.
2. downloaded the .h5 file
3 Created the project using STM32CUBE MX, in that included the STM32 CUBE AI Software pack. Included the core and selected application as 'application template'
4 Using keeping other initializations default and selecting clock frequency 216 MHz, I generated the code.
5 kept main program untouched (where MX_X_CUBE_AI_Process(); is called in while(1) loop.
6 in app_x-cube-ai.c file made the modifications as told in 'Embedded Interface Client API' in documentation.
Here I have no device to actually record the handwritten digit, therefore I copied one of the training dataset sample manually from ipython notebook and saved it in array in app_x-cube-ai.c file. Note that my training was done for the values originally 0 to 255 normalized to 0 to 1.
as stated ,I saved the pixel values for one of the digit in an array called 'imgarray' which is equal to the size 28 x 28. the datatype of 'imgarray' i kept as ai_double.
7 on running the inference in debug mode, I found the output (which were stored in an array out_data[10].) unsatisfactory.
8 moreover, the most concerning this is that even if i changed the value of 'imgarray' with pixel values of different digit, the output result in the 'out_data' array REMAINS EXACTLY THE SAME
Is there anything that I'm doing wrong fundamentally.
Request my fellow developers on STCommunity to guide me.
