写一段GAN网络修复图像的代码

以下是一段GAN网络修复图像的代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from tensorflow.keras.layers import BatchNormalization, Activation, ZeroPadding2D, UpSampling2D, Conv2D
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np

# 加载数据集
(X_train, _), (_, _) = mnist.load_data()

# 归一化数据
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)

# 定义生成器
def build_generator():

    model = Sequential()

    model.add(Dense(128 * 7 * 7, activation="relu", input_dim=100))
    model.add(Reshape((7, 7, 128)))
    model.add(UpSampling2D())
    model.add(Conv2D(128, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=.8))
    model.add(Activation("relu"))
    model.add(UpSampling2D())
    model.add(Conv2D(64, kernel_size=3, padding="same"))
    model.add(BatchNormalization(momentum=.8))
    model.add(Activation("relu"))
    model.add(Conv2D(1, kernel_size=3, padding="same"))
    model.add(Activation("tanh"))

    noise = Input(shape=(100,))
    img = model(noise)

    return Model(noise, img)

# 定义判别器
def build_discriminator():

    model = Sequential()

    model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=(28, 28, 1), padding="same"))
    model.add(LeakyReLU(alpha=.2))
    model.add(Dropout(.25))
    model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
    model.add(ZeroPadding2D(padding=((,1),(,1))))
    model.add(BatchNormalization(momentum=.8))
    model.add(LeakyReLU(alpha=.2))
    model.add(Dropout(.25))
    model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
    model.add(BatchNormalization(momentum=.8))
    model.add(LeakyReLU(alpha=.2))
    model.add(Dropout(.25))
    model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
    model.add(BatchNormalization(momentum=.8))
    model.add(LeakyReLU(alpha=.2))
    model.add(Dropout(.25))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    img = Input(shape=(28, 28, 1))
    validity = model(img)

    return Model(img, validity)

# 构建GAN网络
def build_gan(generator, discriminator):

    discriminator.trainable = False

    gan_input = Input(shape=(100,))
    fake_img = generator(gan_input)
    gan_output = discriminator(fake_img)

    gan = Model(gan_input, gan_output)
    gan.compile(loss='binary_crossentropy', optimizer=Adam(.0002, .5))

    return gan

# 训练GAN网络
def train_gan(generator, discriminator, gan, epochs, batch_size):

    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # 训练判别器
        idx = np.random.randint(, X_train.shape[], batch_size)
        imgs = X_train[idx]
        noise = np.random.normal(, 1, (batch_size, 100))
        gen_imgs = generator.predict(noise)

        d_loss_real = discriminator.train_on_batch(imgs, valid)
        d_loss_fake = discriminator.train_on_batch(gen_imgs, fake)
        d_loss = .5 * np.add(d_loss_real, d_loss_fake)

        # 训练生成器
        noise = np.random.normal(, 1, (batch_size, 100))
        g_loss = gan.train_on_batch(noise, valid)

        # 打印损失
        print ("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss, g_loss))

        # 每 100 个 epoch 保存一次生成器的图片
        if epoch % 100 == :
            save_imgs(generator, epoch)

# 保存生成器的图片
def save_imgs(generator, epoch):
    r, c = 5, 5
    noise = np.random.normal(, 1, (r * c, 100))
    gen_imgs = generator.predict(noise)
    gen_imgs = .5 * gen_imgs + .5
    fig, axs = plt.subplots(r, c)
    cnt = 
    for i in range(r):
        for j in range(c):
            axs[i,j].imshow(gen_imgs[cnt, :,:,], cmap='gray')
            axs[i,j].axis('off')
            cnt += 1
    fig.savefig("images/mnist_%d.png" % epoch)
    plt.close()

# 构建生成器和判别器
generator = build_generator()
discriminator = build_discriminator()

# 构建GAN网络
gan = build_gan(generator, discriminator)

# 训练GAN网络
train_gan(generator, discriminator, gan, epochs=30000, batch_size=32)

注意：这段代码仅供参考，具体实现需要根据具体情况进行调整。