请用Python代码实现：测试GAN模型用于图像修复实验的效果DCGAN_Restore_MNIST。

由于缺少具体要求，以下代码是一个基础的GAN模型用于图像修复实验的代码。

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.datasets import mnist

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

# 数据预处理
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
X_train = np.reshape(X_train, (*X_train.shape, 1))
X_test = np.reshape(X_test, (*X_test.shape, 1))

# 定义生成器模型
def generator_model():
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(128 * 7 * 7, activation='relu', input_dim=100))
    model.add(tf.keras.layers.Reshape((7, 7, 128)))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(64, kernel_size=3, padding='same', activation='relu'))
    model.add(tf.keras.layers.UpSampling2D())
    model.add(tf.keras.layers.Conv2D(1, kernel_size=3, padding='same', activation='sigmoid'))
    return model

# 定义判别器模型
def discriminator_model():
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Conv2D(64, kernel_size=3, strides=2, input_shape=(28, 28, 1), padding='same', activation='relu'))
    model.add(tf.keras.layers.Dropout(0.4))
    model.add(tf.keras.layers.Conv2D(32, kernel_size=3, strides=2, padding='same', activation='relu'))
    model.add(tf.keras.layers.Dropout(0.4))
    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
    return model

# 定义GAN模型
def gan_model(generator, discriminator):
    discriminator.trainable = False
    model = tf.keras.models.Sequential()
    model.add(generator)
    model.add(discriminator)
    return model

# 创建生成器、判别器、GAN模型
generator = generator_model()
discriminator = discriminator_model()
gan = gan_model(generator, discriminator)

# 编译GAN模型
gan.compile(loss='binary_crossentropy', optimizer='adam')

# 训练模型
epochs = 5000
batch_size = 128
for epoch in range(epochs):
    # 生成一批噪声数据
    noise = np.random.normal(0, 1, size=(batch_size, 100))
    # 生成一批损坏的图像数据
    idx = np.random.randint(0, X_train.shape[0], batch_size)
    imgs = X_train[idx]
    imgs_noise = np.copy(imgs)
    # 在随机位置添加噪声
    for i in range(batch_size):
        x = np.random.randint(0, 28)
        y = np.random.randint(0, 28)
        imgs_noise[i,x:x+4,y:y+4,:] = np.random.uniform(0, 1, size=(4,4,1))
    # 训练判别器
    discriminator.trainable = True
    d_loss_real = discriminator.train_on_batch(imgs, np.ones((batch_size, 1)))
    d_loss_fake = discriminator.train_on_batch(generator.predict(noise), np.zeros((batch_size, 1)))
    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
    # 训练生成器
    discriminator.trainable = False
    g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))
    # 打印日志
    print(f"epoch: {epoch}, d_loss: {d_loss}, g_loss: {g_loss}")
    # 每100个epoch保存一次生成的图像
    if epoch % 100 == 0:
        noise = np.random.normal(0, 1, size=(25, 100))
        imgs_gen = generator.predict(noise)
        imgs_gen = np.reshape(imgs_gen, (25, 28, 28))
        plt.figure()
        for i in range(25):
            plt.subplot(5, 5, i+1)
            plt.imshow(imgs_gen[i], cmap='gray')
            plt.axis('off')
        plt.savefig(f"output_{epoch}.png")
        plt.close()

这个GAN模型用于MNIST手写数字图像修复。首先从训练集中随机选择一批图像，然后在图像的随机位置添加噪声。生成器的输入是随机的噪声数据，输出是修复后的图像数据。判别器的输入是原始的图像数据或生成器生成的图像数据，输出是一个二元输出，表示输入是真实的图像还是生成的图像。GAN模型的目标是最小化判别器的误差和最大化生成器的误差。在每次训练时，先训练判别器，再训练生成器。训练过程中，每100个epoch保存一次生成器生成的图像，用于观察生成效果