LITMIR.BIZ

      # Импорт библиотек

      import tensorflow as tf

      import numpy as np

      from sklearn.model_selection import train_test_split

      # Подготовка данных

      X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

      y = np.array([[0], [1], [1], [0]])

      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

      # Определение архитектуры нейросети

      model = tf.keras.Sequential([

      tf.keras.layers.Dense(4, input_shape=(2,), activation='relu'),

      tf.keras.layers.Dense(1, activation='sigmoid')

      ])

      # Инициализация весов

      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

      # Обучение нейросети

      history = model.fit(X_train, y_train, epochs=1000, validation_data=(X_test, y_test))

      # Оценка качества модели

      loss, accuracy = model.evaluate(X_test, y_test)

      print('Loss:', loss)

      print('Accuracy:', accuracy)

      # Финальное тестирование

      X_new = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

      y_pred = model.predict(X_new)

      print('Predictions:', y_pred)