app.py.py

# -*- coding: utf-8 -*-
"""Untitled3.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1gWAA1NHcuSs1JrZSG9sQIrcozcWOaaZL
"""



import tensorflow as tf
from tensorflow.keras.datasets import mnist #Загрузка датасета mnist:
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train / 255
x_test = x_test / 255
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10) # Преобразование меток в бинарные векторы
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
model = Sequential()
# Добавление слоев
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary()
# Размеры тренировочного, валидационного и тестового датасетов
train_size = x_train.shape[0]
val_size = int(train_size * 0.1)
test_size = x_test.shape[0]
print("Размер тренировочного датасета:", train_size)
print("Размер валидационного датасета:", val_size)
print("Размер тестового датасета:", test_size)
tf.keras.utils.plot_model(model, show_shapes= True, show_layer_names= True, show_layer_activations= True)

model.save('my_model')

from google.colab import drive
drive.mount('/content/drive')

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test)) # Обучение модели

loss, accuracy = model.evaluate(x_test, y_test)
print(f'Loss: {loss}, Accuracy: {accuracy}') # Оценка модели на тестовых данных

import numpy as np
index = np.random.randint(len(x_test)) # возьмем случайное изображение
image = x_test[index]
image = np.expand_dims(image, axis=0)
prediction = model.predict(image) # найдем метки
predicted_digit = np.argmax(prediction)
remainder = predicted_digit % 2 # Вычисление остатка на 2
print(f'Predicted Digit: {predicted_digit}, Remainder: {remainder}')