Nikuln/

T

    # Labels for real and generated images
    labels_real = np.ones((batch_size, 1))
    labels_fake = np.zeros((batch_size, 1))

    # Train the discriminator
    d_loss_real = discriminator.train_on_batch(real_images, labels_real)
    d_loss_fake = discriminator.train_on_batch(generated_images, labels_fake)
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

    # Train the generator
    noise = np.random.normal(0, 1, (batch_size, noise_dim))
    labels_gan = np.ones((batch_size, 1))
    g_loss = gan.train_on_batch(noise, labels_gan)

    # Print progress
    print(f"Epoch {epoch}, D Loss: {d_loss[0]}, G Loss: {g_loss}")

    # Save generated images at specific intervals
    if epoch % save_interval == 0:
        save_generated_images(epoch, generator)
        def save_generated_images(epoch, generator, examples=10, dim=(1, 10), figsize=(10, 1)):
noise = np.random.normal(0, 1, (examples, 100))
generated_images = generator.predict(noise)
plt.figure(figsize=figsize)
for i in range(examples):
    plt.subplot(dim[0], dim[1], i+1)
    plt.imshow(generated_images[i], interpolation='nearest', cmap='gray')
    plt.axis('off')
plt.tight_layout()
plt.savefig(f'gan_generated_image_epoch_{epoch}.png')
if __name__ == "__main__":
# Build and compile the models
generator = build_generator()
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)

# Train the GAN
train_gan(generator, discriminator, gan)