基于MNIST，使用Conditional-GAN框架生成手写数字，给出完整代码

基于MNIST数据集生成手写数字的Conditional GAN (条件生成对抗网络) 实现通常需要Python语言和相关的深度学习库，如TensorFlow或PyTorch。这里我将提供一个简化的使用TensorFlow实现的例子：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.datasets import mnist
from tensorflow.keras.optimizers import Adam

# 加载MNIST数据集
(x_train, _), (_, _) = mnist.load_data()
x_train = x_train / 255.

# 数据预处理，添加通道维度
x_train = x_train.reshape(-1, 28, 28, 1)

# 定义生成器模型
def build_generator(latent_dim):
    model = tf.keras.Sequential([
        Input(shape=(latent_dim,)),
        Dense(7*7*64, activation='relu'),
        Reshape((7, 7, 64)),
        Conv2DTranspose(64, (4,4), strides=2, padding='same', activation='relu'),
        Conv2DTranspose(32, (4,4), strides=2, padding='same', activation='relu'),
        Conv2D(1, (3,3), activation='sigmoid')
    ])
    return model

# 定义判别器模型
def build_discriminator(img_shape):
    model = tf.keras.Sequential([
        Input(img_shape),
        Conv2D(64, (3,3), strides=2, padding='same', input_shape=img_shape, activation='relu'),
        Conv2D(128, (3,3), strides=2, padding='same', activation='relu'),
        Flatten(),
        Dense(1, activation='sigmoid')
    ])
    return model

latent_dim = 100
generator = build_generator(latent_dim)
discriminator = build_discriminator((28, 28, 1))

# 编译GAN
optimizer_g = Adam(0.0002, 0.5)
optimizer_d = Adam(0.0002, 0.5)
loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)

@tf.function
def train_step(real_images, noise):
    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(real_images, training=True)
        fake_output = discriminator(generated_images, training=True)

        # 计算损失
        gen_loss = loss_fn(tf.ones_like(fake_output), fake_output)
        disc_loss_real = loss_fn(tf.ones_like(real_output), real_output)
        disc_loss_fake = loss_fn(tf.zeros_like(fake_output), fake_output)

        disc_loss = disc_loss_real + disc_loss_fake

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    optimizer_g.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    optimizer_d.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

# 开始训练
num_epochs = 50
for epoch in range(num_epochs):
    for i in range(int(60000 / batch_size)):
        real_images_batch = x_train[i * batch_size:(i + 1) * batch_size]
        noise = tf.random.normal([batch_size, latent_dim])

        train_step(real_images_batch, noise)

# 生成样本展示
generated_images = generator.predict(tf.random.normal([16, latent_dim]))
# 显示生成的手写数字图像
plt.imshow(generated_images[0].reshape(28, 28), cmap='gray')
plt.show()