TinyML Sınıflandırma: Wine Veri Kümesi

import numpy as np
from tensorflow.keras import Sequential, layers
from tensorflow.keras.utils import to_categorical
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

# veri kümesini eğitme, doğrulama, test'e yükleme ve bölme
X, y = load_wine(return_X_y=True)
y = to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.3)

input_dim = X_train.shape[1:]
output_dim = y.shape[1]

print('input_dim', input_dim)
print('output_dim', output_dim)

# create and train network
# you can customize the layers as you prefer
nn = Sequential()
nn.add(layers.Dense(units=50, activation='relu', input_shape=input_dim))
nn.add(layers.Dense(units=50, activation='relu'))
nn.add(layers.Dense(output_dim, activation='softmax'))

# use categorical_crossentropy for multi-class classification
nn.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
nn.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=100, verbose=0)

print('Doğruluk: %.1f' % nn.evaluate(X_test, y_test)[1])

from tinymlgen import port

print(port(nn, variable_name='wine_model', pretty_print=True, optimize=False))

#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 wine_model[] DATA_ALIGN_ATTRIBUTE = {
    0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00, 
    0x1c, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,
    .......
};
const int iris_model_len = 15228;

#include <EloquentTinyML.h>
#include "wine_model.h"

#define NUMBER_OF_INPUTS 13
#define NUMBER_OF_OUTPUTS 3
#define TENSOR_ARENA_SIZE 16*1024

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

float X_test[20][13] = {
    {1.340e+01, 4.600e+00, 2.860e+00, 2.500e+01, 1.120e+02, 1.980e+00,
    9.600e-01, 2.700e-01, 1.110e+00, 8.500e+00, 6.700e-01, 1.920e+00, 6.300e+02},
    {1.285e+01, 3.270e+00, 2.580e+00, 2.200e+01, 1.060e+02, 1.650e+00,
    6.000e-01, 6.000e-01, 9.600e-01, 5.580e+00, 8.700e-01, 2.110e+00, 5.700e+02},
    {1.334e+01, 9.400e-01, 2.360e+00, 1.700e+01, 1.100e+02, 2.530e+00,
    1.300e+00, 5.500e-01, 4.200e-01, 3.170e+00, 1.020e+00, 1.930e+00, 7.500e+02},
    {1.423e+01, 1.710e+00, 2.430e+00, 1.560e+01, 1.270e+02, 2.800e+00,
    3.060e+00, 2.800e-01, 2.290e+00, 5.640e+00, 1.040e+00, 3.920e+00, 1.065e+03},
    {1.483e+01, 1.640e+00, 2.170e+00, 1.400e+01, 9.700e+01, 2.800e+00,
    2.980e+00, 2.900e-01, 1.980e+00, 5.200e+00, 1.080e+00, 2.850e+00, 1.045e+03},
    {1.245e+01, 3.030e+00, 2.640e+00, 2.700e+01, 9.700e+01, 1.900e+00,
    5.800e-01, 6.300e-01, 1.140e+00, 7.500e+00, 6.700e-01, 1.730e+00, 8.800e+02},
    {1.430e+01, 1.920e+00, 2.720e+00, 2.000e+01, 1.200e+02, 2.800e+00,
    3.140e+00, 3.300e-01, 1.970e+00, 6.200e+00, 1.070e+00, 2.650e+00, 1.280e+03},
    {1.390e+01, 1.680e+00, 2.120e+00, 1.600e+01, 1.010e+02, 3.100e+00,
    3.390e+00, 2.100e-01, 2.140e+00, 6.100e+00, 9.100e-01, 3.330e+00, 9.850e+02},
    {1.165e+01, 1.670e+00, 2.620e+00, 2.600e+01, 8.800e+01, 1.920e+00,
    1.610e+00, 4.000e-01, 1.340e+00, 2.600e+00, 1.360e+00, 3.210e+00, 5.620e+02},
    {1.386e+01, 1.510e+00, 2.670e+00, 2.500e+01, 8.600e+01, 2.950e+00,
    2.860e+00, 2.100e-01, 1.870e+00, 3.380e+00, 1.360e+00, 3.160e+00, 4.100e+02},
    {1.377e+01, 1.900e+00, 2.680e+00, 1.710e+01, 1.150e+02, 3.000e+00,
    2.790e+00, 3.900e-01, 1.680e+00, 6.300e+00, 1.130e+00, 2.930e+00, 1.375e+03},
    {1.296e+01, 3.450e+00, 2.350e+00, 1.850e+01, 1.060e+02, 1.390e+00,
    7.000e-01, 4.000e-01, 9.400e-01, 5.280e+00, 6.800e-01, 1.750e+00, 6.750e+02},
    {1.305e+01, 5.800e+00, 2.130e+00, 2.150e+01, 8.600e+01, 2.620e+00,
    2.650e+00, 3.000e-01, 2.010e+00, 2.600e+00, 7.300e-01, 3.100e+00, 3.800e+02},
    {1.182e+01, 1.470e+00, 1.990e+00, 2.080e+01, 8.600e+01, 1.980e+00,
    1.600e+00, 3.000e-01, 1.530e+00, 1.950e+00, 9.500e-01, 3.330e+00, 4.950e+02},
    {1.164e+01, 2.060e+00, 2.460e+00, 2.160e+01, 8.400e+01, 1.950e+00,
    1.690e+00, 4.800e-01, 1.350e+00, 2.800e+00, 1.000e+00, 2.750e+00, 6.800e+02},
    {1.303e+01, 9.000e-01, 1.710e+00, 1.600e+01, 8.600e+01, 1.950e+00,
    2.030e+00, 2.400e-01, 1.460e+00, 4.600e+00, 1.190e+00, 2.480e+00, 3.920e+02},
    {1.176e+01, 2.680e+00, 2.920e+00, 2.000e+01, 1.030e+02, 1.750e+00,
    2.030e+00, 6.000e-01, 1.050e+00, 3.800e+00, 1.230e+00, 2.500e+00, 6.070e+02},
    {1.439e+01, 1.870e+00, 2.450e+00, 1.460e+01, 9.600e+01, 2.500e+00,
    2.520e+00, 3.000e-01, 1.980e+00, 5.250e+00, 1.020e+00, 3.580e+00, 1.290e+03},
    {1.420e+01, 1.760e+00, 2.450e+00, 1.520e+01, 1.120e+02, 3.270e+00,
    3.390e+00, 3.400e-01, 1.970e+00, 6.750e+00, 1.050e+00, 2.850e+00, 1.450e+03},
    {1.368e+01, 1.830e+00, 2.360e+00, 1.720e+01, 1.040e+02, 2.420e+00,
    2.690e+00, 4.200e-01, 1.970e+00, 3.840e+00, 1.230e+00, 2.870e+00, 9.900e+02}
};

uint8_t y_test[20] = {2, 2, 1, 0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 1, 1, 1, 1, 0, 0, 0};

void setup() {
    Serial.begin(115200);
    nn.begin(wine_model);
}

void loop() {
    for (uint8_t i = 0; i < 10; i++) {
        Serial.print("Örnek #");
        Serial.print(i + 1);
        Serial.print(": ");
        Serial.print("tahmin ");
        Serial.print(nn.predictClass(X_test[i]));
        Serial.print(" vs ");
        Serial.print(y_test[i]);
        Serial.println(" gerçek değer");
    }

    delay(10000);
}