请给我一段用CGAN生成数组的代码

以下是一个使用CGAN生成数组的Python代码示例：

import numpy as np
import matplotlib.pyplot as plt

from keras.models import Model
from keras.layers import Input, Dense, Reshape, Flatten, Concatenate
from keras.layers import BatchNormalization, Activation, Embedding
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam

# 定义生成器模型
def build_generator(latent_dim, n_classes):
    noise = Input(shape=(latent_dim,))
    label = Input(shape=(1,), dtype='int32')
    label_embedding = Flatten()(Embedding(n_classes, latent_dim)(label))

    model_input = Concatenate()([noise, label_embedding])
    model = Dense(128 * 7 * 7, activation='relu')(model_input)
    model = Reshape((7, 7, 128))(model)
    model = BatchNormalization()(model)
    model = UpSampling2D()(model)
    model = Conv2D(64, kernel_size=5, padding='same', activation='relu')(model)
    model = BatchNormalization()(model)
    model = UpSampling2D()(model)
    model = Conv2D(1, kernel_size=5, padding='same', activation='tanh')(model)

    model = Model([noise, label], model)
    return model

# 定义判别器模型
def build_discriminator(img_shape, n_classes):
    img = Input(shape=img_shape)
    label = Input(shape=(1,), dtype='int32')

    label_embedding = Flatten()(Embedding(n_classes, np.prod(img_shape))(label))
    label_embedding = Reshape(img_shape)(label_embedding)

    model_input = Concatenate()([img, label_embedding])

    model = Conv2D(64, kernel_size=5, padding='same')(model_input)
    model = LeakyReLU(alpha=0.2)(model)
    model = Dropout(0.3)(model)

    model = Conv2D(128, kernel_size=5, strides=2, padding='same')(model)
    model = LeakyReLU(alpha=0.2)(model)
    model = Dropout(0.3)(model)

    model = Flatten()(model)
    model = Dense(1, activation='sigmoid')(model)

    model = Model([img, label], model)
    return model

# 定义CGAN模型
def build_cgan(generator, discriminator):
    discriminator.trainable = False

    noise = Input(shape=(latent_dim,))
    label = Input(shape=(1,), dtype='int32')
    img = generator([noise, label])
    validity = discriminator([img, label])

    model = Model([noise, label], validity)
    model.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))
    return model

# 训练CGAN模型
def train(generator, discriminator, cgan, epochs, batch_size, latent_dim, n_classes, X_train):
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        imgs, labels = X_train[idx], y_train[idx]

        noise = np.random.normal(0, 1, (batch_size, latent_dim))
        gen_imgs = generator.predict([noise, labels])

        d_loss_real = discriminator.train_on_batch([imgs, labels], valid)
        d_loss_fake = discriminator.train_on_batch([gen_imgs, labels], fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        sampled_labels = np.random.randint(0, n_classes, batch_size).reshape(-1, 1)
        g_loss = cgan.train_on_batch([noise, sampled_labels], valid)

        print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

# 加载数据集
(X_train, y_train), (_, _) = mnist.load_data()
X_train = X_train / 127.5 - 1.
X_train = np.expand_dims(X_train, axis=3)

# 定义模型参数
img_shape = X_train.shape[1:]
latent_dim = 100
n_classes = 10
batch_size = 32
epochs = 10000

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

# 构建CGAN模型
cgan = build_cgan(generator, discriminator)

# 训练CGAN模型
train(generator, discriminator, cgan, epochs, batch_size, latent_dim, n_classes, X_train)

这个代码示例使用了Keras框架来实现CGAN模型，使用了MNIST手写数字数据集来训练模型。生成器模型接受一个噪声向量和一个标签，输出一个生成的图像。判别器模型接受一个图像和一个标签，输出这个图像是否为真实数据的概率。CGAN模型将生成器和判别器结合起来，生成器的输出被传入判别器中判断其真伪，并通过反向传播来更新生成器的参数以生成更真实的图像。