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Н Новости

Сверточные нейронные сети. Создание нейросети для распознавания цифр на языке программирования Python

В современном мире искусственный интеллект и машинное обучение стремительно развиваются, меняя нашу повседневную жизнь и открывая новые горизонты в различных областях. Одной из ключевых технологий, лежащих в основе этих достижений, являются сверточные нейронные сети (Convolutional Neural Networks, CNN). Эти мощные алгоритмы позволяют эффективно обрабатывать и анализировать изображения, что находит применение в самых разных сферах: от медицинской диагностики до систем безопасности.

CNN подходит для классификации изображений, что делает её отличным выбором для задачи распознавания рукописных цифр.

CNN состоит из:

  1. Сверточные слои (Conv2D): Эти слои выполняют операции свертки, которые помогают модели извлекать ключевые признаки из изображений, такие как края, текстуры и формы.

  2. Слои подвыборки (MaxPooling2D): Эти слои уменьшают размерность данных, сохраняя при этом важные признаки, что помогает ускорить обучение и снизить риск переобучения.

  3. Полносвязные слои (Dense): Эти слои отвечают за классификацию, принимая вектор признаков от предыдущих слоев и принимая решение о классе (цифре) изображения.

В этой статье я расскажу о том, как создать свою собственную сверточную нейронную сеть, способную распознавать цифры на изображениях, будь то рукописные или машинописные. Мы рассмотрим основные принципы работы CNN, изучим архитектуру моделей, а также пошагово разберем процесс разработки и обучения нейросети.

Содержание:

  • Подготовка данных.

  • Создание проекта.

  • Импорт библиотек.

  • Задание параметров.

  • Функция для загрузки и предобработки изображений.

  • Загрузка и предобработка данных.

  • Создание модели нейросети.

  • Обучение модели.

  • Оценка модели.

  • Сохранение модели в формате Keras.

  • Функции для загрузки и предсказания изображения.

  • Пример использования.

  • Полный код нейросети.

  • Заключение.


Подготовка данных

Архитектура дата-сета:

data/

└── train/

├── 0/

│ ├── 0_1.png

│ ├── 0_2.png

│ └── 0_3.png

├── 1/

├── ...

└── 9/

└── test/

├── 0/

├── ...

└── 9/

train/ cодержит данные, используемые для обучения модели нейросети. Именно эти данные модель будет анализировать и использовать для того, чтобы "научиться" распознавать цифры. Каждая поддиректория (0, 1, 2, ..., 9) представляет собой класс цифры и содержит изображения, на которых изображена соответствующая цифра. Эти данные подаются на вход модели во время фазы обучения (model.fit), и модель настраивает свои параметры на основе этих данных.


test/ cодержит данные, используемые для тестирования производительности обученной модели. Эти данные модель не видит во время обучения и используется для оценки её способности обобщать на новые данные. Аналогично папке train, каждая поддиректория (0, 1, 2, ..., 9) представляет собой класс цифры и содержит изображения с соответствующей цифрой. Эти данные подаются на вход модели во время фазы оценки (model.evaluate), чтобы определить точность модели на новых, невидимых ранее данных. Разделение данных на train и test помогает избежать переобучения модели и позволяет честно оценить её производительность на новых данных, что является важным этапом в процессе машинного обучения. Это делает модель более надежной и способной обобщать, а не просто "запоминать" тренировочные данные.

Каждый файл должен содержать изображение одной цифры (0-9).
data/train/0/: фотографии рукописных нулей.
data/train/1/: фотографии рукописных единиц.
...
data/test/0/: фотографии рукописных нулей для тестирования.
data/test/1/: фотографии рукописных единиц для тестирования.
...

Минимальное количество:

Для каждого класса (0-9): рекомендуется иметь не менее 100 изображений. В идеале, чем больше, тем лучше. Например, 500-1000 изображений на класс даст более стабильные результаты.

Общий ориентир:

  • Тренировочный набор (train): 1000-5000 изображений для каждого класса.

  • Тестовый набор (test): 200-500 изображений для каждого класса.

Для обучения и тестирования моделей машинного обучения, включая нейросети, обычно используется стандартное соотношение данных. Наиболее распространённое соотношение - это 80/20 или 70/30. Это означает, что 80% (или 70%) данных используется для обучения модели (train), а оставшиеся 20% (или 30%) - для тестирования (test).


Создание проекта

Создаем новый проект и два .py файла: create_model.py и test_model.py

Скрипт create_model.py предназначен для создания и обучения модели сверточной нейронной сети (CNN), которая сможет распознавать цифры на изображениях, как рукописные, так и машинописные.

Основные функции и шаги в скрипте:

  • Импорт необходимых библиотек и модулей (TensorFlow, NumPy, PIL).

  • Определение параметров изображения (например, высота и ширина).

  • Функция для загрузки и предобработки изображений из папок train и test.

  • Загрузка и предобработка данных.

  • Создание архитектуры модели CNN с использованием слоев Conv2D, MaxPooling2D, Flatten и Dense.

  • Компиляция модели с указанием оптимизатора, функции потерь и метрик.

  • Обучение модели на тренировочных данных.

  • Оценка модели на тестовых данных.

  • Сохранение обученной модели в формате Keras.

Скрипт test_model.py предназначен для загрузки сохраненной модели и выполнения предсказаний на новых изображениях, а также для проверки точности предсказаний модели.

Основные функции и шаги в скрипте:

  • Импорт необходимых библиотек и модулей (TensorFlow, NumPy, PIL).

  • Загрузка обученной модели из файла, сохраненного в create_model.py.

  • Функция для загрузки и предобработки нового изображения для предсказания.

  • Функция для выполнения предсказания на загруженном изображении с использованием модели.

  • Пример использования функции предсказания для нового изображения.

  • Вывод предсказанной цифры и сравнение с ожидаемой цифрой.


Импорт библиотек (create_model.py).

# Импорт модулей
import tensorflow as tf  # pip install tensorflow
import numpy as np  # pip install numpy
from PIL import Image  # pip install pillow
import os

TensorFlow. Мощная библиотека для машинного обучения и глубокого обучения, созданная Google. В этом проекте она используется для создания и тренировки нейронной сети. Мы используем TensorFlow для создания модели нейронной сети, определения слоев модели, компиляции и тренировки модели. TensorFlow предоставляет гибкость, масштабируемость и множество инструментов для построения и тренировки нейронных сетей. Он также поддерживает высокоуровневый API Keras, который упрощает процесс разработки.

NumPy. Библиотека для работы с многомерными массивами и высокоуровневыми математическими функциями. Она широко используется в научных вычислениях и анализе данных. В этом проекте NumPy используется для преобразования изображений в массивы, нормализации данных и работы с массивами данных. NumPy обеспечивает эффективную работу с большими массивами данных и предоставляет множество полезных функций для их обработки, что делает его идеальным для работы с данными в машинном обучении.

Pillow (Python Imaging Library, PIL). Библиотека для работы с изображениями. Она предоставляет инструменты для открытия, манипулирования и сохранения различных форматов изображений. В этом проекте Pillow используется для загрузки изображений, преобразования их в градации серого и изменения их размера до нужных параметров. Pillow является стандартной библиотекой для работы с изображениями в Python, предоставляя широкий набор инструментов и поддержку множества форматов изображений.

os. Встроенная библиотека Python, которая предоставляет функции для взаимодействия с операционной системой. В этом проекте os используется для навигации по файловой системе, получения списка файлов в директории и создания путей к файлам. os предоставляет все необходимые функции для работы с файлами и директориями, делая код платформенно-независимым.

Эти импорты обеспечивают все необходимые инструменты для выполнения задач по загрузке, предобработке данных и созданию нейронной сети.


Задание параметров (create_model.py).

# Параметры
img_height = 28  # Высота изображений в пикселях
img_width = 28  # Ширина изображений в пикселях

Этот блок кода определяет параметры, которые будут использоваться для предобработки изображений.

Эти параметры задают размер изображений, к которому они будут приведены перед тем, как быть переданными в нейросеть. Все изображения будут изменены до размера 28x28 пикселей. Единый размер изображений важен для консистентности входных данных модели, что помогает модели лучше обучаться и обрабатывать данные.

Почему именно 28x28 пикселей? Достаточно маленький, чтобы быть вычислительно эффективным, но при этом достаточно большой, чтобы содержать важные детали для распознавания.


Функция для загрузки и предобработки изображений (create_model.py).

# Функция для загрузки и предобработки изображений
def load_images_from_folder(folder):
    images = []
    labels = []
    for label in range(10):
        path = os.path.join(folder, str(label))
        for filename in os.listdir(path):
            img_path = os.path.join(path, filename)
            img = Image.open(img_path).convert('L')
            img = img.resize((img_width, img_height))
            img = np.asarray(img)
            img = img / 255.0
            images.append(img)
            labels.append(label)
    return np.array(images), np.array(labels)

Эта функция загружает изображения из указанной папки, предобрабатывает их и возвращает массивы изображений и меток.

images = []
labels = []

images: список для хранения предобработанных изображений.
labels: список для хранения меток (классов) изображений.

for label in range(10):
path = os.path.join(folder, str(label))

Итерируемся по числам от 0 до 9, что соответствует 10 классам изображений. Генерируем путь к папке с изображениями каждого класса.

for filename in os.listdir(path):
img_path = os.path.join(path, filename)

Проходим по каждому файлу в папке.
Генерируем полный путь к каждому изображению.

img = Image.open(img_path).convert('L')
img = img.resize((img_width, img_height))
img = np.asarray(img)
img = img / 255.0

Открываем изображение и преобразуем его в градации серого с помощью convert('L').
Изменяем размер изображения до заданных параметров (28x28 пикселей).
Преобразуем изображение в массив NumPy для дальнейшей обработки.
Нормализуем значения пикселей в диапазоне от 0 до 1, разделяя их на 255.

images.append(img)
labels.append(label)

Добавляем предобработанное изображение в список images.
Добавляем соответствующую метку (номер класса) в список labels.

return np.array(images), np.array(labels)

Преобразуем списки images и labels в массивы NumPy и возвращаем их.


Загрузка и предобработка данных (create_model.py).

# Загрузка и предобработка данных
x_train, y_train = load_images_from_folder('data/train')
x_test, y_test = load_images_from_folder('data/test')

Этот блок кода выполняет загрузку и предобработку данных для обучения и тестирования модели нейронной сети.

Вызов функции load_images_from_folder для загрузки обучающих данных:

  • x_train: Массив изображений, используемых для обучения модели.

  • y_train: Массив меток (классов), соответствующих обучающим изображениям.

  • 'data/train': Путь к директории, содержащей обучающие изображения, разделенные по папкам для каждого класса (0-9).

Вызов функции load_images_from_folder для загрузки тестовых данных:

  • x_test: Массив изображений, используемых для тестирования модели.

  • y_test: Массив меток (классов), соответствующих тестовым изображениям.

  • 'data/test': Путь к директории, содержащей тестовые изображения, разделенные по папкам для каждого класса (0-9).

Функция load_images_from_folder загружает и предобрабатывает изображения, выполняя такие действия, как изменение размера, преобразование в градации серого и нормализация. Полученные массивы данных (x_train, y_train, x_test, y_test) затем используются для обучения и тестирования модели.

Эти данные обеспечивают основу для обучения нейронной сети распознаванию рукописных цифр, и их правильная предобработка играет ключевую роль в достижении высоких результатов точности модели.

# Убедимся, что данные имеют правильные формы перед reshape
print(f'Количество train-изобр.: {x_train.shape[0]}, высота/ширина: {x_train.shape[1]}x{x_train.shape[2]}px')
print(f'Количество test-изобр.: {x_test.shape[0]}, высота/ширина: {x_test.shape[1]}x{x_test.shape[2]}px')

x_train = x_train.reshape(-1, img_height, img_width, 1)
x_test = x_test.reshape(-1, img_height, img_width, 1)

Этот блок кода выполняет проверку формы данных после загрузки и предобработки, а затем изменяет форму данных для использования в модели нейронной сети.

Проверка формы данных:
print(f'Количество train-изобр.: {x_train.shape[0]}, высота/ширина: {x_train.shape[1]}x{x_train.shape[2]}px')
print(f'Количество test-изобр.: {x_test.shape[0]}, высота/ширина: {x_test.shape[1]}x{x_test.shape[2]}px')

Эти строки выводят информацию о количестве изображений и их размерах (высота и ширина) в тренировочном (x_train) и тестовом (x_test) наборах данных. Это полезно для проверки, что данные загружены корректно и имеют ожидаемые размеры (28x28 пикселей).

Изменение формы данных:
x_train = x_train.reshape(-1, img_height, img_width, 1)
x_test = x_test.reshape(-1, img_height, img_width, 1)

reshape изменяет форму массивов данных, добавляя четвертое измерение, которое представляет канал (в данном случае, один канал для градаций серого).
-1 указывает, что размер первого измерения (количество изображений) вычисляется автоматически, исходя из общего количества элементов и указанных размеров (высота, ширина, каналы).
Теперь данные имеют форму (количество изображений, 28, 28, 1), что соответствует требуемому формату входных данных для сверточных нейронных сетей в TensorFlow/Keras.
Пример для тренировочного набора данных (x_train):
Перед reshape: (количество изображений, 28, 28)
После reshape: (количество изображений, 28, 28, 1)
Эти изменения важны для корректной обработки данных нейронной сетью, так как сверточные слои ожидают данные в формате (batch_size, height, width, channels).


Создание модели нейросети (create_model.py).

# Создание модели нейросети с использованием Input
model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(img_height, img_width, 1)),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

Этот блок кода создает архитектуру модели нейронной сети для распознавания рукописных цифр с использованием библиотеки TensorFlow/Keras.

Создание модели с использованием Sequential:

model = tf.keras.models.Sequential()

Цель: Sequential — это простой способ создать модель нейронной сети в Keras. Слои добавляются последовательно, один за другим.
Почему именно Sequential: Удобен для создания линейных моделей, где каждый слой имеет ровно один вход и один выход.

Слой Input:

tf.keras.layers.Input(shape=(img_height, img_width, 1))

Цель: Задает форму входных данных, ожидаемых моделью. В данном случае, входные данные — это изображения размером 28x28 пикселей с одним каналом (градации серого).
Почему именно Input: Использование Input слоя упрощает создание моделей и делает их более читаемыми.

Первый слой свертки (Conv2D):

tf.keras.layers.Conv2D(32, (3, 3), activation='relu')

Цель: Этот слой применяет 32 фильтра размером 3x3 к входным данным, извлекая важные признаки, такие как края и текстуры.
Активация relu: Применяется функция активации relu (Rectified Linear Unit), которая делает выход неотрицательным и добавляет нелинейность модели.

Первый слой подвыборки (MaxPooling2D):

tf.keras.layers.MaxPooling2D((2, 2))

Цель: Уменьшает размерность данных, агрегируя значения в области 2x2 пикселя и выбирая максимальное значение в каждой области.
Почему MaxPooling2D: Помогает уменьшить количество параметров и вычислительные затраты, сохраняя важные признаки.

Второй слой свертки (Conv2D):

tf.keras.layers.Conv2D(64, (3, 3), activation='relu')

Цель: Применяет 64 фильтра размером 3x3 к данным, извлекая более сложные признаки.
Активация relu: Применяется функция активации relu для сохранения положительных значений и добавления нелинейности.

Второй слой подвыборки (MaxPooling2D):

tf.keras.layers.MaxPooling2D((2, 2))

Цель: Опять уменьшает размер данных, агрегируя значения в области 2x2 пикселя, выбирая максимальное значение в каждой области.
Почему MaxPooling2D: Продолжает уменьшать размер данных, сохраняя важные признаки, и помогает снизить вычислительные затраты.

Слой выравнивания (Flatten):

tf.keras.layers.Flatten()

Цель: Преобразует многомерный массив данных в одномерный вектор. Это необходимо для подключения к полносвязным (Dense) слоям.
Почему Flatten: Готовит данные для подачи в полносвязные слои, объединяя все признаки в одномерный вектор.

Полносвязный слой (Dense):

tf.keras.layers.Dense(64, activation='relu')

Цель: Обрабатывает данные, используя 64 нейрона. В каждом нейроне происходит линейная комбинация входных данных и применение функции активации relu.
Почему Dense с relu: Dense слои позволяют нейронной сети обучаться сложным паттернам. relu добавляет нелинейность, что помогает модели лучше обучаться на сложных данных.

Выходной полносвязный слой (Dense):

tf.keras.layers.Dense(10, activation='softmax')

Цель: Обрабатывает выходные данные и использует 10 нейронов, соответствующих 10 классам (цифры 0-9). Применяет функцию активации softmax для получения вероятностей классов.
Почему Dense с softmax: softmax функция нормализует выходные значения, преобразуя их в вероятности классов, которые суммируются до 1. Это позволяет модели предсказывать наиболее вероятный класс для входного изображения.

  • Sequential: Линейная модель, добавляем слои последовательно.

  • Input: Задает форму входных данных.

  • Conv2D (32 фильтра): Извлекает основные признаки.

  • MaxPooling2D (2x2): Уменьшает размер данных.

  • Conv2D (64 фильтра): Извлекает более сложные признаки.

  • MaxPooling2D (2x2): Снова уменьшает размер данных, сохраняя важные признаки.

  • Flatten: Преобразует данные в одномерный вектор для полносвязных слоев.

  • Dense (64 нейрона): Обрабатывает данные и обучается сложным паттернам с функцией активации relu.

  • Dense (10 нейронов, softmax): Выходной слой, предсказывающий вероятности для 10 классов (цифры 0-9).


Обучение модели (create_model.py).

# Компиляция и обучение модели
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

Этот блок кода отвечает за компиляцию модели нейронной сети, установку параметров обучения и выполнение самого процесса обучения.

model.compile(optimizer='adam', - Adam (Adaptive Moment Estimation) — это оптимизатор, который комбинирует преимущества методов AdaGrad и RMSProp. Он адаптируется к обучению моделей, изменяя скорость обучения на основе момента первого и второго порядка. Он обладает хорошей производительностью на большом количестве задач и является стандартным выбором для многих моделей глубокого обучения.

loss='sparse_categorical_crossentropy', - Функция потерь sparse_categorical_crossentropy используется для задач многоклассовой классификации, где метки представлены в виде целых чисел. Она измеряет разницу между предсказанными вероятностями и истинными метками. sparse_categorical_crossentropy подходит для наших данных, где метки являются целыми числами (0-9), что упрощает вычисления и улучшает производительность.

metrics=['accuracy']) - Метрика, по которой будет оцениваться производительность модели. В данном случае мы используем точность (accuracy), которая показывает, какой процент предсказаний модели правильный. Эта метрика проста для понимания и широко используется для задач классификации.

model.fit(x_train, y_train, epochs=5) - Обучение модели. x_train, y_train - обучающие данные (изображения и метки), которые будут использоваться для обучения модели. epochs=5 - количество эпох обучения. Одна эпоха представляет собой один полный проход по всему обучающему набору данных. Почему именно 5 эпох? Это начальное значение, которое можно увеличить для более длительного и тщательного обучения модели. Большее количество эпох может улучшить точность, но также может привести к переобучению.

Процесс обучения:

  • Компиляция: Устанавливает параметры обучения и подготавливает модель к тренировке.

  • Обучение (fit): Процесс, при котором модель проходит через обучающие данные несколько раз (эпохи) и обновляет свои веса, чтобы минимизировать функцию потерь.

Теперь модель готова к обучению и оптимизации для задачи распознавания рукописных цифр.


Оценка модели (create_model.py).

# Оценка модели
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f'Точность на тестовом наборе данных: {test_acc}')

if test_acc < 0.6:
  print("Низкая точность. Рекомендуется улучшить модель и данные.")
elif 0.6 <= test_acc < 0.8:
  print("Средняя точность. Неплохо, но есть куда стремиться.")
elif 0.8 <= test_acc < 0.9:
  print("Хорошая точность. Модель работает хорошо.")
else:
  print("Отличная точность. Модель отлично справляется с задачей.")

Этот блок кода оценивает производительность обученной модели на тестовом наборе данных и выводит точность предсказаний.

Функция model.evaluat() - оценивает модель на основе тестовых данных. Функция принимает тестовые данные (x_test) и метки (y_test) и возвращает значение функции потерь (test_loss) и метрику точности (test_acc). Оценка на тестовом наборе данных позволяет понять, насколько хорошо модель обобщается на новых, ранее невидимых данных, что является важным показателем её качества.
print(f'Точность на тестовом наборе данных: {test_acc}') - выводит точность модели на тестовом наборе данных в читаемом формате. Это позволяет быстро и наглядно оценить производительность модели, понять её текущий уровень и определить направления для дальнейших улучшений.

Для оценки производительности модели сверточной нейронной сети (CNN) в задаче распознавания цифр можно использовать следующие диапазоны точности:

  • Низкая точность (менее 60%)

  • Средняя точность (60-80%)

  • Хорошая точность (80-90%)

  • Отличная точность (более 90%)


Сохранение модели в формате Keras (create_model.py).

# Сохранение модели в новом формате Keras
model.save('my_model.keras')
print(f'Модель создана!')

Этот блок кода отвечает за сохранение обученной модели нейронной сети в формате Keras и вывод сообщения о завершении процесса сохранения.

Функция model.save() сохраняет текущую версию обученной модели на диск с указанным именем и в указанном формате. Файл сохраняется в формате Keras (.keras), который включает в себя архитектуру модели, её веса, конфигурацию тренировки (если применимо), и оптимизатор. Использование model.save позволяет легко загружать и использовать модель в будущем без необходимости повторного обучения.

print(f'Модель создана!') выводит сообщение в консоль, подтверждающее, что модель успешно сохранена. Подтверждение успешного сохранения модели помогает понять, что процесс завершен корректно и модель готова к использованию или дальнейшим операциям.


Функции для загрузки и предсказания изображения (test_model.py).

import tensorflow as tf
import numpy as np
from PIL import Image

# Параметры
img_height = 28
img_width = 28

# Загрузка модели
model = tf.keras.models.load_model('my_model.keras')

# Функции для загрузки и предсказания изображения
def load_image(filepath):
    img = Image.open(filepath).convert('L')
    img = img.resize((img_height, img_width))
    img = np.array(img)
    img = img / 255.0
    img = img.reshape(-1, img_height, img_width, 1)
    return img

def predict_digit(test_img):
    img = load_image(test_img)
    prediction = model.predict(img)
    return np.argmax(prediction)

model = tf.keras.models.load_model('my_model.keras') - Этот код загружает ранее обученную и сохраненную модель нейронной сети из файла my_model.keras. Это позволяет использовать модель для выполнения предсказаний на новых данных без необходимости повторного обучения.

def load_image(filepath): - функция загружает изображение из указанного файла, преобразует его в формат, подходящий для модели нейронной сети, и возвращает предобработанное изображение.

  • img = Image.open(filepath).convert('L') - открыть изображение и преобразовать его в градации серого (оттенки серого). Модель была обучена на черно-белых изображениях, поэтому важно привести новые данные к такому же формату.

  • img = img.resize((img_height, img_width)) - изменить размер изображения до 28x28 пикселей. Модель была обучена на изображениях размером 28x28 пикселей, поэтому входные данные должны иметь такой же размер.

  • img = np.array(img) - Преобразовать изображение в массив для дальнейшей обработки. Модель принимает на вход данные в формате массива.

  • img = img / 255.0 - Нормализовать значения пикселей в диапазоне от 0 до 1. Нормализация улучшает производительность модели, так как она была обучена на нормализованных данных.

  • img = img.reshape(-1, img_height, img_width, 1) - Изменить форму массива, добавив дополнительное измерение для канала (один канал для черно-белого изображения). Модель ожидает данные в формате (batch_size, height, width, channels).

def predict_digit(test_img): - функция выполняет предсказание на основе загруженной модели и возвращает предсказанную цифру.

  • img = load_image(test_img) - Загрузить и предобработать изображение для модели. Предобработка данных необходима для корректного выполнения предсказаний.

  • prediction = model.predict(img) - Получить предсказание модели для данного изображения. Модель возвращает вероятности для каждого класса (цифры от 0 до 9).

  • return np.argmax(prediction) - Определить класс (цифру) с наибольшей вероятностью. Вернуть наиболее вероятную цифру, предсказанную моделью.


Пример использования (test_model.py).

# Пример использования
test_image = 'data\sample_a.png'
predicted_digit = predict_digit(test_image)

print(f'Цифра на изображении: {predicted_digit}')

Этот блок кода демонстрирует, как использовать обученную и сохраненную модель для выполнения предсказаний на новых изображениях.

test_image = 'data/sample_a.png' - Задает путь к изображению, на котором необходимо выполнить предсказание. В данном случае это изображение sample_a.png, расположенное в папке data.

predicted_digit = predict_digit(test_image) - Функция принимает путь к изображению, загружает и предобрабатывает изображение, выполняет предсказание с использованием модели и возвращает предсказанную цифру.

print(f'Цифра на изображении: {predicted_digit}') - Выводит предсказанную моделью цифру для указанного изображения в читаемом формате.


Файл create_model.py:

# Импорт модулей
import tensorflow as tf  # pip install tensorflow
import numpy as np  # pip install numpy
from PIL import Image  # pip install pillow
import os

# Параметры
img_height = 28
img_width = 28

# Функция для загрузки и предобработки изображений
def load_images_from_folder(folder):
    images = []
    labels = []
    for label in range(10):
        path = os.path.join(folder, str(label))
        for filename in os.listdir(path):
            img_path = os.path.join(path, filename)
            img = Image.open(img_path).convert('L')
            img = img.resize((img_width, img_height))
            img = np.asarray(img)
            img = img / 255.0
            images.append(img)
            labels.append(label)
    return np.array(images), np.array(labels)

# Загрузка и предобработка данных
x_train, y_train = load_images_from_folder('data/train')
x_test, y_test = load_images_from_folder('data/test')

# Убедимся, что данные имеют правильные формы перед reshape
print(f'Количество train-изобр.: {x_train.shape[0]}, высота/ширина: {x_train.shape[1]}x{x_train.shape[2]}px')
print(f'Количество test-изобр.: {x_test.shape[0]}, высота/ширина: {x_test.shape[1]}x{x_test.shape[2]}px')

x_train = x_train.reshape(-1, img_height, img_width, 1)
x_test = x_test.reshape(-1, img_height, img_width, 1)

# Создание модели нейросети с использованием Input
model = tf.keras.models.Sequential([
    tf.keras.layers.Input(shape=(img_height, img_width, 1)),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Компиляция и обучение модели
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

# Оценка модели
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f'Точность на тестовом наборе данных: {test_acc}')

# Сохранение модели в новом формате Keras
model.save('my_model.keras')
print(f'Модель создана!')

Файл test_model.py:

import tensorflow as tf
import numpy as np
from PIL import Image

# Параметры
img_height = 28
img_width = 28

# Загрузка модели
model = tf.keras.models.load_model('my_model.keras')

# Функции для загрузки и предсказания изображения
def load_image(filepath):
    img = Image.open(filepath).convert('L')
    img = img.resize((img_height, img_width))
    img = np.array(img)
    img = img / 255.0
    img = img.reshape(-1, img_height, img_width, 1)
    return img

def predict_digit(test_img):
    img = load_image(test_img)
    prediction = model.predict(img)
    return np.argmax(prediction)

# Пример использования
test_image = 'data\sample_a.png'
predicted_digit = predict_digit(test_image)

print(f'Цифра на изображении: {predicted_digit}')

Заключение

Сверточные нейронные сети (CNN) представляют собой мощный инструмент для решения задач, связанных с обработкой и анализом изображений. В этой статье мы рассмотрели, как создать собственную CNN для распознавания цифр, будь то рукописные или машинописные. Мы прошли через все этапы — от подготовки данных и создания проекта до обучения и оценки модели, а также научились сохранять модель и использовать её для предсказаний.

Создание и обучение модели на реальных данных позволяет не только понять основные принципы работы CNN, но и получить практический опыт, который можно применять в различных областях. Этот проект демонстрирует, как современные технологии могут быть использованы для решения задач, которые ранее казались сложными и трудоемкими.

Продолжайте экспериментировать и улучшать свою модель, увеличивая объем данных, используя различные архитектуры нейронных сетей и методы оптимизации. Мир машинного обучения и искусственного интеллекта полон возможностей, и каждый новый проект открывает перед вами новые горизонты.

Желаю вам удачи в ваших будущих исследованиях и проектах в области машинного обучения и надеюсь, что эта статья была полезной и вдохновляющей. Пусть ваши нейросети всегда будут точными, а проекты — успешными!

Источник

  • 07.09.23 16:24 CherryTeam

    Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/

  • 08.10.23 01:30 davec8080

    The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.

  • 24.06.24 04:31 tashandiarisha

    Web-site. https://trustgeekshackexpert.com/ Tele-Gram, trustgeekshackexpert During the pandemic, I ventured into the world of cryptocurrency trading. My father loaned me $10,000, which I used to purchase my first bitcoins. With diligent research and some luck, I managed to grow my investment to over $350,000 in just a couple of years. I was thrilled with my success, but my excitement was short-lived when I decided to switch brokers and inadvertently fell victim to a phishing attack. While creating a new account, I received what seemed like a legitimate email requesting verification. Without second-guessing, I provided my information, only to realize later that I had lost access to my email and cryptocurrency wallets. Panic set in as I watched my hard-earned assets disappear before my eyes. Desperate to recover my funds, I scoured the internet for solutions. That's when I stumbled upon the Trust Geeks Hack Expert on the Internet. The service claimed to specialize in recovering lost crypto assets, and I decided to take a chance. Upon contacting them, the team swung into action immediately. They guided me through the entire recovery process with professionalism and efficiency. The advantages of using the Trust Geeks Hack Expert Tool became apparent from the start. Their team was knowledgeable and empathetic, understanding the urgency and stress of my situation. They employed advanced security measures to ensure my information was handled safely and securely. One of the key benefits of the Trust Geeks Hack Expert Tool was its user-friendly interface, which made a complex process much more manageable for someone like me, who isn't particularly tech-savvy. They also offered 24/7 support, so I never felt alone during recovery. Their transparent communication and regular updates kept me informed and reassured throughout. The Trust Geeks Hack Expert Tool is the best solution for anyone facing similar issues. Their swift response, expertise, and customer-centric approach set them apart from other recovery services. Thanks to their efforts, I regained access to my accounts and my substantial crypto assets. The experience taught me a valuable lesson about online security and showed me the incredible potential of the Trust Geeks Hack Expert Tool. Email:: trustgeekshackexpert{@}fastservice{.}com WhatsApp  + 1.7.1.9.4.9.2.2.6.9.3

  • 26.06.24 18:46 Jacobethannn098

    LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER

  • 26.06.24 18:46 Jacobethannn098

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  • 04.07.24 04:49 ZionNaomi

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  • 13.07.24 21:13 michaelharrell825

    In 2020, amidst the economic fallout of the pandemic, I found myself unexpectedly unemployed and turned to Forex trading in hopes of stabilizing my finances. Like many, I was drawn in by the promise of quick returns offered by various Forex robots, signals, and trading advisers. However, most of these products turned out to be disappointing, with claims that were far from reality. Looking back, I realize I should have been more cautious, but the allure of financial security clouded my judgment during those uncertain times. Amidst these disappointments, Profit Forex emerged as a standout. Not only did they provide reliable service, but they also delivered tangible results—a rarity in an industry often plagued by exaggerated claims. The positive reviews from other users validated my own experience, highlighting their commitment to delivering genuine outcomes and emphasizing sound financial practices. My journey with Profit Forex led to a net profit of $11,500, a significant achievement given the challenges I faced. However, my optimism was short-lived when I encountered obstacles trying to withdraw funds from my trading account. Despite repeated attempts, I found myself unable to access my money, leaving me frustrated and uncertain about my financial future. Fortunately, my fortunes changed when I discovered PRO WIZARD GIlBERT RECOVERY. Their reputation for recovering funds from fraudulent schemes gave me hope in reclaiming what was rightfully mine. With a mixture of desperation and cautious optimism, I reached out to them for assistance. PRO WIZARD GIlBERT RECOVERY impressed me from the start with their professionalism and deep understanding of financial disputes. They took a methodical approach, using advanced techniques to track down the scammers responsible for withholding my funds. Throughout the process, their communication was clear and reassuring, providing much-needed support during a stressful period. Thanks to PRO WIZARD GIlBERT RECOVERY's expertise and unwavering dedication, I finally achieved a resolution to my ordeal. They successfully traced and retrieved my funds, restoring a sense of justice and relief. Their intervention not only recovered my money but also renewed my faith in ethical financial services. Reflecting on my experience, I've learned invaluable lessons about the importance of due diligence and discernment in navigating the Forex market. While setbacks are inevitable, partnering with reputable recovery specialists like PRO WIZARD GIlBERT RECOVERY can make a profound difference. Their integrity and effectiveness have left an indelible mark on me, guiding my future decisions and reinforcing the value of trustworthy partnerships in achieving financial goals. I wholeheartedly recommend PRO WIZARD GIlBERT RECOVERY to anyone grappling with financial fraud or disputes. Their expertise and commitment to client satisfaction are unparalleled, offering a beacon of hope in challenging times. Thank you, PRO WIZARD GIlBERT RECOVERY, for your invaluable assistance in reclaiming what was rightfully mine. Your service not only recovered my funds but also restored my confidence in navigating the complexities of financial markets with greater caution and awareness. Email: prowizardgilbertrecovery(@)engineer.com Homepage: https://prowizardgilbertrecovery.xyz WhatsApp: +1 (516) 347‑9592

  • 17.07.24 02:26 thompsonrickey

    In the vast and often treacherous realm of online investments, I was entangled in a web of deceit that cost me nearly  $45,000. It all started innocuously enough with an enticing Instagram profile promising lucrative returns through cryptocurrency investment. Initially, everything seemed promising—communications were smooth, and assurances were plentiful. However, as time passed, my optimism turned to suspicion. Withdrawal requests were met with delays and excuses. The once-responsive "investor" vanished into thin air, leaving me stranded with dwindling hopes and a sinking feeling in my gut. It became painfully clear that I had been duped by a sophisticated scheme designed to exploit trust and naivety. Desperate to recover my funds, I turned to online forums where I discovered numerous testimonials advocating for Muyern Trust Hacker. With nothing to lose, I contacted them, recounting my ordeal with a mixture of skepticism and hope. Their swift response and professional demeanor immediately reassured me that I had found a lifeline amidst the chaos. Muyern Trust Hacker wasted no time in taking action. They meticulously gathered evidence, navigated legal complexities, and deployed their expertise to expedite recovery. In what felt like a whirlwind of activity, although the passage of time was a blur amidst my anxiety, they achieved the seemingly impossible—my stolen funds were returned. The relief I felt was overwhelming. Muyern Trust Hacker not only restored my financial losses but also restored my faith in justice. Their commitment to integrity and their relentless pursuit of resolution were nothing short of remarkable. They proved themselves as recovery specialists and guardians against digital fraud, offering hope to victims like me who had been ensnared by deception. My gratitude knows no bounds for Muyern Trust Hacker. Reach them at muyerntrusted @ m a i l - m e . c o m AND Tele gram @ muyerntrusthackertech

  • 18.07.24 20:13 austinagastya

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  • 27.08.24 12:50 James889900

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  • 27.08.24 13:06 James889900

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  • 02.09.24 20:24 [email protected]

    If You Need Hacker To Recover Your Bitcoin Contact Paradox Recovery Wizard Paradox Recovery Wizard successfully recovered $123,000 worth of Bitcoin for my husband, which he had lost due to a security breach. The process was efficient and secure, with their expert team guiding us through each step. They were able to trace and retrieve the lost cryptocurrency, restoring our peace of mind and financial stability. Their professionalism and expertise were instrumental in recovering our assets, and we are incredibly grateful for their service. Email: support@ paradoxrecoverywizard.com Email: paradox_recovery @cyberservices.com Wep: https://paradoxrecoverywizard.com/ WhatsApp: +39 351 222 3051.

  • 06.09.24 01:35 Celinagarcia

    HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you ​​​​​​have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.

  • 06.09.24 01:44 Celinagarcia

    HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you ​​​​​​have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.

  • 16.09.24 00:10 marcusaustin

    Bitcoin Recovery Services: Restoring Lost Cryptocurrency If you've lost access to your cryptocurrency and unable to make a withdrawal, I highly recommend iBolt Cyber Hacker Bitcoin Recovery Services. Their team is skilled, professional, and efficient in recovering lost Bitcoin. They provide clear communication, maintain high security standards, and work quickly to resolve issues. Facing the stress of lost cryptocurrency, iBolt Cyber Hacker is a trusted service that will help you regain access to your funds securely and reliably. Highly recommended! Email: S u p p o r t @ ibolt cyber hack . com Cont/Whtp + 3. .9 .3. .5..0. .9. 2. 9. .0 .3. 1 .8. Website: h t t p s : / / ibolt cyber hack . com /

  • 16.09.24 00:11 marcusaustin

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  • 23.09.24 18:56 matthewshimself

    At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.

  • 01.10.24 14:54 Sinewclaudia

    I lost about $876k few months ago trading on a fake binary option investment websites. I didn't knew they were fake until I tried to withdraw. Immediately, I realized these guys were fake. I contacted Sinew Claudia world recovery, my friend who has such experience before and was able to recover them, recommended me to contact them. I'm a living testimony of a successful recovery now. You can contact the legitimate recovery company below for help and assistance. [email protected] [email protected] WhatsApp: 6262645164

  • 02.10.24 22:27 Emily Hunter

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

  • 18.10.24 09:34 freidatollerud

    The growth of WIN44 in Brazil is very interesting! If you're looking for more options for online betting and casino games, I recommend checking out Casinos in Brazil. It's a reliable platform that offers a wide variety of games and provides a safe and enjoyable experience for users. It's worth checking out! https://win44.vip

  • 31.10.24 00:13 ytre89

    Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.

  • 02.11.24 14:44 diannamendoza732

    In the world of Bitcoin recovery, Pro Wizard Gilbert truly represents the gold standard. My experience with Gilbert revealed just how exceptional his methods are and why he stands out as the premier authority in this critical field. When I first encountered the complexities of Bitcoin recovery, I was daunted by the technical challenges and potential risks. Gilbert’s approach immediately distinguished itself through its precision and effectiveness. His methods are meticulously designed, combining cutting-edge techniques with an in-depth understanding of the Bitcoin ecosystem. He tackled the recovery process with a level of expertise and thoroughness that was both impressive and reassuring. What sets Gilbert’s methods apart is not just their technical sophistication but also their strategic depth. He conducts a comprehensive analysis of each case, tailoring his approach to address the unique aspects of the situation. This personalized strategy ensures that every recovery effort is optimized for success. Gilbert’s transparent communication throughout the process was invaluable, providing clarity and confidence during each stage of the recovery. The results I achieved with Pro Wizard Gilbert’s methods were remarkable. His gold standard approach not only recovered my Bitcoin but did so with an efficiency and reliability that exceeded my expectations. His deep knowledge, innovative techniques, and unwavering commitment make him the definitive expert in Bitcoin recovery. For anyone seeking a benchmark in Bitcoin recovery solutions, Pro Wizard Gilbert’s methods are the epitome of excellence. His ability to blend technical prowess with strategic insight truly sets him apart in the industry. Call: for help. You may get in touch with them at ; Email: (prowizardgilbertrecovery(@)engineer.com) Telegram ; https://t.me/Pro_Wizard_Gilbert_Recovery Homepage ; https://prowizardgilbertrecovery.info

  • 12.11.24 00:50 TERESA

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  • 17.11.24 09:31 Vivianlocke223

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  • 21.11.24 04:14 ronaldandre617

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  • 21.11.24 08:02 Emily Hunter

    If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.

  • 22.11.24 04:41 [email protected]

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  • 22.11.24 23:43 teresaborja

    all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets.   ENAIL < Tech cybers force recovery @ cyber services. com >   WEBSITE < ht tps : // tech cyber force recovery. info  >   TEXT < +1. 561. 726. 3697 >

  • 24.11.24 02:21 [email protected]

    I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT

  • 25.11.24 02:19 briankennedy

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  • 25.11.24 02:20 briankennedy

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  • 26.11.24 21:59 [email protected]

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  • 26.11.24 23:12 rickrobinson8

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  • 27.11.24 00:39 [email protected]

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  • 27.11.24 09:10 Michal Novotny

    The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.

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  • 02.12.24 23:02 ytre89

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  • 04.12.24 22:24 andreygagloev

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  • 27.12.24 00:21 swiftdream

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