GAN

gistfile1.txt

import tensorflow as tf
from tensorflow.keras import layers

# Генератор
def build_generator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, input_dim=100, activation='relu'))
    model.add(layers.Dense(784, activation='sigmoid'))
    return model

# Дискриминатор
def build_discriminator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(128, input_dim=784, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))
    return model

# Построение моделей
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy', optimizer='adam')

# GAN (генератор + дискриминатор)
discriminator.trainable = False
gan_input = layers.Input(shape=(100,))
gan_output = discriminator(generator(gan_input))
gan = tf.keras.Model(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer='adam')

#  Тренировка GAN
import numpy as np

def train_gan(gan, generator, discriminator, epochs, batch_size=128):
    for epoch in range(epochs):
        # Генерация рандомного шума
        noise = np.random.normal(0, 1, (batch_size, 100))
        generated_data = generator.predict(noise)

        # Даем настоящие данные
        real_data = np.random.rand(batch_size, 784) 
        
        # Тренировка дискриминатора
        combined_data = np.concatenate([real_data, generated_data])
        labels = np.concatenate([np.ones(batch_size), np.zeros(batch_size)])
        discriminator.train_on_batch(combined_data, labels)

        #  Тренировка генератора
        noise = np.random.normal(0, 1, (batch_size, 100))
        misleading_labels = np.ones(batch_size)
        gan.train_on_batch(noise, misleading_labels)