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eloquentarduino / EloquentTinyML

Eloquent interface to Tensorflow Lite for Microcontrollers
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cannot allocate tensors with own generated model for Sine example #22

Closed Vensim closed 3 years ago

Vensim commented 3 years ago

The output of the serial comms is. image

What I've tried : Increasing and decreasing Tensor arena size, still same error.

Haven't done much changes to the model generation other than outputting straight to a c file :

# Port to C
c_code = port(model, pretty_print = True)
c_file = open("sine_model.h", "w")
c_file.write(c_code)

Here are my files and the model that's being used. Python script :

import math
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tinymlgen import port

def get_model():
    SAMPLES = 1000
    np.random.seed(1337)
    x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)
    # shuffle and add noise
    np.random.shuffle(x_values)
    y_values = np.sin(x_values)
    y_values += 0.1 * np.random.randn(*y_values.shape)

    # split into train, validation, test
    TRAIN_SPLIT =  int(0.6 * SAMPLES)
    TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)
    x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
    y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])

    # create a NN with 2 layers of 16 neurons
    model = tf.keras.Sequential()
    model.add(layers.Dense(8, activation='relu', input_shape=(1,)))
    model.add(layers.Dense(16, activation='relu'))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    model.fit(x_train, y_train, epochs=200, batch_size=16,
                        validation_data=(x_validate, y_validate))
    return model

def test_model(model, verbose=False):
    x_test = np.random.uniform(low=0, high=2*math.pi, size=100)
    y_test = np.sin(x_test)
    y_pred = model.predict(x_test)
    print('MAE', np.abs(y_pred - y_test).mean())

model = get_model()
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = converter.convert()
test_model(model)

# Save the model to disk
open("sine_model_quantized.tflite", "wb").write(tflite_model)

# Port to C
c_code = port(model, pretty_print = True)
c_file = open("sine_model.h", "w")
c_file.write(c_code)

Arduino IDE code :

#include <EloquentTinyML.h>
// sine_model.h contains the array you exported from the previous step
// with either xxd or tinymlgen
#include "sine_model.h"

#define NUMBER_OF_INPUTS 1
#define NUMBER_OF_OUTPUTS 1
// in future projects you may need to tweak this value.
// it's a trial and error process
#define TENSOR_ARENA_SIZE 2*1024

Eloquent::TinyML::TfLite<NUMBER_OF_INPUTS, NUMBER_OF_OUTPUTS, TENSOR_ARENA_SIZE> ml;

void setup() {
    Serial.begin(115200);
    ml.begin(model_data);
}

void loop() {
    // pick up a random x and predict its sine
    float x = 3.14 * random(100) / 100;
    float y = sin(x);
    float input[1] = { x };
    float predicted = ml.predict(input);

    Serial.print("sin(");
    Serial.print(x);
    Serial.print(") = ");
    Serial.print(y);
    Serial.print("\t predicted: ");
    Serial.println(predicted);
    delay(1000);
}

sine_model.h


#ifdef __has_attribute
#define HAVE_ATTRIBUTE(x) __has_attribute(x)
#else
#define HAVE_ATTRIBUTE(x) 0
#endif
#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))
#else
#define DATA_ALIGN_ATTRIBUTE
#endif

const unsigned char model_data[] DATA_ALIGN_ATTRIBUTE = {
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const int model_data_len = 2384;
Vensim commented 3 years ago

files.zip

Vensim commented 3 years ago

Model output fixed upon inspection of Issue 17's comments.

Replace

c_code = port(model, pretty_print=True)

with

c_code = port(model, pretty_print=True, optimize=False)

Sent request.

whubaichuan commented 1 year ago

@eloquentarduino I suggest changing the code in the original file. It seems lots of people ask the same question including me at the beginning. Thanks.

Vensim commented 1 year ago

@eloquentarduino Is this the same issue as was posted here? I see on updated example of Sine , optimize=False is missing, did adding it fix it for you?

eloquentarduino commented 1 year ago

Publishing EloquentTinyML==2.4.4 with optimize=False in every Python example. I will migrate the tinymlgen.port function into my new everywhereml package.