The aim for NNoM is to help Embedded Engineers to develop and deploy Neural Network models onto the MCUs. NNoM is working closely with Keras. If you dont know Keras yet Getting started: 30 seconds to Keras
This guide will show you how to use NNoM for your very first step from an embedded engineer perspective.
You should:
- know C language and your target MCU enviroment.
- know a bit of python.
You must NOT:
- be a pro in TensorFlow / lite :-)
If you know nothing about Keras, you must check Getting started: 30 seconds to Keras first.
Lets say if we want to classify the MNIST hand writing dataset. This is what you normally do with Keras.
model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
model.add(Dense(10))Each operation in Keras are defined by "Layer", same as we did in NNoM. The terms are different from Tensorflow (That is why you must not be a PRO in Tensorflow >_<).
This model is with an input dimension 784, a hidden fully connected layer including 32 units and outputing 10 units(which is the number of classification(number 0~9)). The hidden layer is activated by ReLU activation (which keep all possitive values while set all nagtive values to 0).
After you have trained this model using the method in the Keras' guide, the model can now do prediction.
If you feed new image to it, it will tell you what is the wrtten number.
Please try to run a example in Keras or NNoM if you are still confusing.
After the model is trained, the weights and parameters are already functional. We can now convert it to C language files then put it in your MCU project.
The result of this step is a single
weights.hfile, which contains everything you need.
To conver the model, NNoM has provided an simple API generate_model()API to automaticly do the job.
Simply pass the model and the test dataset to it.
It will do all the magics for you.
generate_model(model, x_test, name='weights.h')When the conversion is finished, you will find a new weights.h under your working folder.
Simply copy the file to your MCU project, and call model = nnom_model_create(); inside you main().
Below is what you should do in practice.
#include "nnom.h"
#include "weights.h"
int main(void)
{
nnom_model_t *model;
model = nnom_model_create();
model_run(model);
}Then, your model is now running on you MCU.
If you have supported printf on your MCU, you should see the compiling info on your consoles.
Compiling logging similar to this:
Start compiling model...
Layer(#) Activation output shape ops(MAC) mem(in, out, buf) mem blk lifetime
-------------------------------------------------------------------------------------------------
#1 Input - - ( 28, 28, 1) ( 784, 784, 0) 1 - - - - - - -
#2 Conv2D - ReLU - ( 28, 28, 12) 84k ( 784, 9408, 36) 1 1 3 - - - - -
#3 MaxPool - - ( 14, 14, 12) ( 9408, 2352, 0) 1 2 3 - - - - -
#4 UpSample - - ( 28, 28, 12) ( 2352, 9408, 0) 1 2 2 - - - - -
#5 Conv2D - - ( 14, 14, 12) 254k ( 2352, 2352, 432) 1 1 2 1 1 - - -
#6 Conv2D - - ( 28, 28, 12) 1.01M ( 9408, 9408, 432) 1 1 2 1 1 - - -
#7 Add - - ( 28, 28, 12) ( 9408, 9408, 0) 1 1 1 1 1 - - -
#8 MaxPool - - ( 14, 14, 12) ( 9408, 2352, 0) 1 1 1 2 1 - - -
#9 Conv2D - - ( 14, 14, 12) 254k ( 2352, 2352, 432) 1 1 1 2 1 - - -
#10 AvgPool - - ( 7, 7, 12) ( 2352, 588, 168) 1 1 1 1 1 1 - -
#11 AvgPool - - ( 14, 14, 12) ( 9408, 2352, 336) 1 1 1 1 1 1 - -
#12 Add - - ( 14, 14, 12) ( 2352, 2352, 0) 1 1 - 1 1 1 - -
#13 MaxPool - - ( 7, 7, 12) ( 2352, 588, 0) 1 1 1 2 - 1 - -
#14 UpSample - - ( 14, 14, 12) ( 588, 2352, 0) 1 1 - 2 - 1 - -
#15 Add - - ( 14, 14, 12) ( 2352, 2352, 0) 1 1 1 1 - 1 - -
#16 MaxPool - - ( 7, 7, 12) ( 2352, 588, 0) 1 1 1 1 - 1 - -
#17 Conv2D - - ( 7, 7, 12) 63k ( 588, 588, 432) 1 1 1 1 - 1 - -
#18 Add - - ( 7, 7, 12) ( 588, 588, 0) 1 1 1 - - 1 - -
#19 Concat - - ( 7, 7, 24) ( 1176, 1176, 0) 1 1 1 - - - - -
#20 Dense - ReLU - ( 96, 1, 1) 112k ( 1176, 96, 2352) 1 1 1 - - - - -
#21 Dense - - ( 10, 1, 1) 960 ( 96, 10, 192) 1 1 1 - - - - -
#22 Softmax - - ( 10, 1, 1) ( 10, 10, 0) 1 - 1 - - - - -
#23 Output - - ( 10, 1, 1) ( 10, 10, 0) 1 - - - - - - -
-------------------------------------------------------------------------------------------------
Memory cost by each block:
blk_0:9408 blk_1:9408 blk_2:9408 blk_3:9408 blk_4:2352 blk_5:588 blk_6:0 blk_7:0
Total memory cost by network buffers: 40572 bytes
Compling done in 76 ms
You can now use the model to predict your data.
- Firstly, filling the input buffer
nnom_input_buffer[]with your own data(image, signals) which is defined inweights.h. - Secondly, call
model_run(model);to do your prediction. - Thirdly, read your result from
nnom_output_buffer[]. The maximum number is the results.
Now, please do check NNoM examples for more fancy methods.
To be continue..