人工知能ベースの仮想現実技術におけるリアリズムの問​​題

import tensorflow as tf
import numpy as np

# 定义生成器模型
def generator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(256, input_shape=(100,)))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(512))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(784, activation='tanh'))
    return model

# 定义判别器模型
def discriminator_model():
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(512, input_shape=(784,)))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(256))
    model.add(tf.keras.layers.LeakyReLU())
    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
    return model

# 定义生成器的损失函数
def generator_loss(fake_output):
    return tf.losses.sigmoid_cross_entropy(tf.ones_like(fake_output), fake_output)

# 定义判别器的损失函数
def discriminator_loss(real_output, fake_output):
    real_loss = tf.losses.sigmoid_cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = tf.losses.sigmoid_cross_entropy(tf.zeros_like(fake_output), fake_output)
    return real_loss + fake_loss

# 定义模型的优化器
generator_optimizer = tf.keras.optimizers.Adam(0.0002, 0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(0.0002, 0.5)

# 定义生成器和判别器的实例
generator = generator_model()
discriminator = discriminator_model()

# 定义训练步骤
@tf.function
def train_step(images):
    noise = tf.random.normal([batch_size, 100])
    
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)
        
        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)
        
        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)
        
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
    
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 开始训练
def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)
            
        # 每个 epoch 结束后显示生成的图像
        if epoch % 10 == 0:
            generate_images(generator, epoch + 1)
            
# 生成图像
def generate_images(model, epoch):
    noise = tf.random.normal([16, 100])
    generated_images = model(noise, training=False)
    
    generated_images = 0.5 * generated_images + 0.5

    for i in range(generated_images.shape[0]):
        plt.subplot(4, 4, i + 1)
        plt.imshow(generated_images[i, :, :, 0] * 255, cmap='gray')
        plt.axis('off')
        
    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()

# 加载数据集，训练模型
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 784).astype('float32')
train_images = (train_images - 127.5) / 127.5
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(60000).batch(256)

train(train_dataset, epochs=100)