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eloquentarduino / EloquentTinyML

Eloquent interface to Tensorflow Lite for Microcontrollers
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Problem in apply Conv2D on my own case when refer to your digits example. #50

Closed whubaichuan closed 2 years ago

whubaichuan commented 2 years ago

Hi, recently I am doing an interesting project about using Conv2D or Dense to detect 2-classification-issues. I refer to your digits example.

I see in your digits example, you train the model using N*8*8*1 data. But in DigitsExample.ino, you input the 1*64 data to test. If it was in the Keras environment, the test input should also be 1*8*8*1. Does that mean in Arduino environment, the EloquentTinyML will regards all the input in one dimension structure as the 1*8*8*1 structure according to the .h network file, as you said here.

Next, it's my own project. Actually, my training data is N*1024. Next, I tested two different types of models. One is Conv2D and Dense, and other is fully Dense. I am sure I have made optimize=False as told here.

  1. Conv2D and Dense doesn't work

where the input is 1*32*32*1 and the output is 2-classification. I refer to your digits example, have the model in Keras like this:

model = tf.keras.Sequential()
model.add(layers.Conv2D(8, (3, 3), activation='relu', input_shape=(32, 32, 1)))
model.add(layers.Flatten()) 
#Flatten works in your digits example although Flatten seems to be unsupport by TFLite. 
#But anyway, I also have tried layers.Reshape() here, which should be supported by TFLite.
model.add(layers.Dense(2))

In Keras training, I reshape my training data into N*32*32*1. In the Arduino project, I reshape my data into 1*1024, which is similar to your 1*64 in digits. But the Predicted proba are 0.0 , 0.0, which is so weird.

  1. fully Dense works

where the input is 1*1024 and the output is 2-classification. The model is:

model = tf.keras.models.Sequential([
tf.keras.layers.Dense(10, activation=tf.nn.relu,input_shape=(1024,)),
tf.keras.layers.Dense(2)
])

In Keras training, I reshape my training data into N*1024. In the Arduino project, I also reshape my data into 1*1024, which is similar to your 1*64 in digits. It works normally. The Predicted proba are 0.90 , 0.10, which is ok.

Now I am stuck with this tricky problem. I guess the reason is the data structure problem. But perhaps there are another aspects I haven't considered. Do you have another advice for me?

Looking forward to your reply.

Best wishes

eloquentarduino commented 2 years ago

Can I see the data set? Also, which version of TF are you using?

whubaichuan commented 2 years ago

@eloquentarduino hi, one example of data 1*1024 is :

float x_test[1024] = 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In keras training process, I use:

tf.__version__ = 2.6.4
tf.keras.__version__=2.6.0

In Arduino project, I use:

EloquentTinyMl version = 2.4.3

And the code in .ino is (Here I just take one input as the example) 

#include <EloquentTinyML.h>
#include <eloquent_tinyml/tensorflow.h>

#include "net.h"

#define N_INPUTS 1024
#define N_OUTPUTS 2
// in future projects
//you may need to tweak this value: it's a trial and error process
#define TENSOR_ARENA_SIZE 20*1024

Eloquent::TinyML::TensorFlow::TensorFlow<N_INPUTS, N_OUTPUTS, TENSOR_ARENA_SIZE> tf;

void setup() {
    Serial.begin(9600);
    tf.begin(model);
}

float x_test[1024] = 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

void loop() {

    float y_pred[2] = {0,0};
    int j = 0;

    for (j=0; j < 1; j=j+1)
    {

      tf.predict(x_test, y_pred);

      Serial.print("Predicted proba are: ");

      for (int i = 0; i < 2; i++) {
          Serial.print(y_pred[i]);
          Serial.print(i == 1 ? '\n' : ',');
      }

      delay(1000);
    }
}

Actually, I don't change the .ion code, and only change the model code in keras python environment. But in Arduino serial monitor Conv2D and Dense doesn't work, fully Dense works.

I guess it's the problem of input data stucture? or the TENSOR_ARENA_SIZE because of my data size? or the library version?

whubaichuan commented 2 years ago

@eloquentarduino solved the problem. We should be careful about the SRAM limitation and TENSOR_ARENA_SIZE.