生成对抗网络规划设计代码

生成对抗网络（Generative Adversarial Networks, GANs）是一种深度学习模型，通常用于生成新的、逼真的数据样本。其设计包括两个主要部分：生成器（Generator）和判别器（Discriminator）。以下是简单的GAN设计代码概述，以Python和Keras库为例：

# 导入所需库
from keras.layers import Input, Dense, Reshape, Flatten, Embedding
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam

# 定义生成器模型
def build_generator(latent_dim):
    model = Sequential()
    model.add(Dense(256 * 8 * 8, input_dim=latent_dim))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Reshape((8, 8, 256)))
    model.add(Conv2DTranspose(128, (4, 4), strides=(2, 2), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Conv2DTranspose(64, (4, 4), strides=(2, 2), padding='same'))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Conv2DTranspose(1, (4, 4), activation='tanh', padding='same'))
    
    noise = Input(shape=(latent_dim,))
    img = model(noise)
    return Model(noise, img)

# 定义判别器模型
def build_discriminator(img_shape):
    model = Sequential()
    model.add(Conv2D(64, (3, 3), strides=(2, 2), padding='same', input_shape=img_shape))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Conv2D(128, (3, 3), strides=(2, 2), padding='same'))
    model.add(ZeroPadding2D(padding=((0,1),(0,1))))
    model.add(LeakyReLU(alpha=0.2))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    img = Input(shape=img_shape)
    validity = model(img)
    return Model(img, validity)

# 创建并训练GAN
generator = build_generator(latent_dim)  
discriminator = build_discriminator(img_shape)
gan_input = Input(shape=(latent_dim,))
x = generator(gan_input)
gan_output = discriminator(x)
gan_model = Model(gan_input, gan_output)
gan_model.compile(optimizer=Adam(lr=0.0002, beta_1=0.5), loss='binary_crossentropy')

# 更详细的训练过程省略...