生成对抗网络代码python

生成对抗网络（GAN）是一种用于生成新数据的机器学习模型。下面是一个简单的GAN代码示例，使用Python和TensorFlow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))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Dense(28*28, 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))
    model.add(layers.LeakyReLU())
    model.add(layers.Dense(256))
    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):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

# 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

# 定义生成器和判别器
generator = make_generator_model()
discriminator = make_discriminator_model()

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

# 定义训练循环
@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))

# 使用MNIST数据集进行训练
(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')
train_images = (train_images - 127.5) / 127.5
BUFFER_SIZE = 60000
BATCH_SIZE = 256
train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

# 训练模型
EPOCHS = 50
for epoch in range(EPOCHS):
    for image_batch in train_dataset:
        train_step(image_batch)

# 生成新的图片
noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)

以上是一个简单的生成对抗网络（GAN）的Python代码示例，用于生成手写数字图片。请注意，这只是一个基本示例，实际应用中可能需要进行更多的调整和优化。