mad-gan异常检测代码

根据提供的引用内容，Mad-Gan是使用LSTM作为生成器和判别器的GAN模型，用于异常检测。由于没有提供具体的代码，以下是一个Mad-Gan的异常检测代码的示例：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Reshape, LSTM, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K

# 定义生成器模型
def build_generator(latent_dim, n_features):
    # 输入层
    input_layer = Input(shape=(latent_dim,))
    # 将输入转换为3D张量
    reshaped_input = Reshape((1, latent_dim))(input_layer)
    # LSTM层
    lstm_layer = LSTM(100, activation='relu')(reshaped_input)
    # Dropout层
    dropout_layer = Dropout(0.2)(lstm_layer)
    # 输出层
    output_layer = Dense(n_features)(dropout_layer)
    # 定义模型
    model = Model(input_layer, output_layer)
    return model

# 定义判别器模型
def build_discriminator(n_features):
    # 输入层
    input_layer = Input(shape=(1, n_features))
    # LSTM层
    lstm_layer = LSTM(100, activation='relu')(input_layer)
    # Dropout层
    dropout_layer = Dropout(0.2)(lstm_layer)
    # 输出层
    output_layer = Dense(1, activation='sigmoid')(dropout_layer)
    # 定义模型
    model = Model(input_layer, output_layer)
    return model

# 定义GAN模型
def build_gan(generator, discriminator):
    # 冻结判别器的权重
    discriminator.trainable = False
    # 定义GAN模型
    gan_input = Input(shape=(latent_dim,))
    x = generator(gan_input)
    gan_output = discriminator(x)
    gan = Model(gan_input, gan_output)
    # 编译GAN模型
    gan.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0002, beta_1=0.5))
    return gan

# 定义损失函数
def custom_loss(y_true, y_pred):
    mse = K.mean(K.square(y_pred - y_true), axis=1)
    return mse

# 加载数据
data = np.load('data.npy')

# 定义超参数
latent_dim = 5
n_features = data.shape[1]

# 构建生成器和判别器模型
generator = build_generator(latent_dim, n_features)
discriminator = build_discriminator(n_features)

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

# 训练GAN模型
for epoch in range(epochs):
    # 生成随机向量
    random_latent_vectors = np.random.normal(size=(len(data), latent_dim))
    # 生成异常数据
    generated_data = generator.predict(random_latent_vectors)
    # 将异常数据和真实数据合并
    X = np.concatenate([data, generated_data])
    # 为真实数据和异常数据创建标签
    y = np.zeros(2 * len(data))
    y[:len(data)] = 1
    # 训练判别器
    discriminator.trainable = True
    discriminator.train_on_batch(X, y)
    # 训练生成器
    noise = np.random.normal(size=(len(data), latent_dim))
    y2 = np.ones(len(data))
    discriminator.trainable = False
    gan.train_on_batch(noise, y2)

# 使用生成器生成异常数据
random_latent_vectors = np.random.normal(size=(len(data), latent_dim))
generated_data = generator.predict(random_latent_vectors)

# 计算异常数据的损失
loss = custom_loss(data, generated_data)

# 输出异常数据的损失
print('Loss:', loss)