帮我用python写一个有2个限制条件的GAN网络

你好，我是 CSDN 开发的 C 知道。关于您的问题，我可以回答。以下是一个简单的 Python 代码，用于实现具有两个限制条件的 GAN 网络：

# 导入必要的库
import tensorflow as tf
from tensorflow.keras import layers

# 定义生成器模型
def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(256, input_shape=(100,), use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(512, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(28*28*1, use_bias=False, activation='tanh'))
    model.add(layers.Reshape((28, 28, 1)))

    return model

# 定义判别器模型
def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Flatten(input_shape=(28, 28, 1)))
    model.add(layers.Dense(512, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(256, use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(1))

    return model

# 定义损失函数
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

# 定义生成器损失函数
def generator_loss(fake_output, condition):
    return cross_entropy(tf.ones_like(fake_output), fake_output) + condition

# 定义判别器损失函数
def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

# 定义优化器
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

# 定义训练函数
@tf.function
def train_step(images, condition):
    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, condition)
        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))

# 定义训练数据
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5  # 将像素值归一化到[-1, 1]范围内
BUFFER_SIZE = 60000
BATCH_SIZE = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

# 创建生成器和判别器实例
generator = make_generator_model()
discriminator = make_discriminator_model()

# 训练模型
EPOCHS = 100
condition = .5
for epoch in range(EPOCHS):
    for image_batch in train_dataset:
        train_step(image_batch, condition)

    # 每 10 个 epoch 保存一次生成的图片
    if epoch % 10 == :
        noise = tf.random.normal([1, 100])
        generated_image = generator(noise, training=False)
        generated_image = (generated_image * 127.5 + 127.5).numpy().astype('uint8')
        plt.imshow(generated_image[, :, :, ], cmap='gray')
        plt.axis('off')
        plt.show()

这个 GAN 网络有两个限制条件，一个是生成器的损失函数中添加了一个条件，另一个是训练过程中的条件。具体实现细节可以参考代码注释。希望能对您有所帮助。