GAN算法异常检测代码

以下是使用GAN算法进行异常检测的Python代码示例：

首先，我们需要定义生成器和判别器模型：

from keras.layers import Input, Dense, Dropout
from keras.models import Model

def build_generator(latent_dim, output_dim):
    input_layer = Input(shape=(latent_dim,))
    x = Dense(128, activation='relu')(input_layer)
    x = Dense(256, activation='relu')(x)
    x = Dense(512, activation='relu')(x)
    x = Dense(output_dim, activation='sigmoid')(x)
    generator = Model(inputs=input_layer, outputs=x)
    return generator

def build_discriminator(input_dim):
    input_layer = Input(shape=(input_dim,))
    x = Dense(512, activation='relu')(input_layer)
    x = Dropout(0.2)(x)
    x = Dense(256, activation='relu')(x)
    x = Dropout(0.2)(x)
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.2)(x)
    x = Dense(1, activation='sigmoid')(x)
    discriminator = Model(inputs=input_layer, outputs=x)
    return discriminator

然后，我们可以使用生成器和判别器模型来构建GAN模型：

from keras.optimizers import Adam

def build_gan(generator, discriminator):
    discriminator.trainable = False
    gan_input_layer = Input(shape=(latent_dim,))
    x = generator(gan_input_layer)
    gan_output = discriminator(x)
    gan = Model(inputs=gan_input_layer, outputs=gan_output)
    gan_optimizer = Adam(lr=0.0002, beta_1=0.5)
    gan.compile(loss='binary_crossentropy', optimizer=gan_optimizer)
    return gan

接下来，我们可以使用GAN模型来训练我们的异常检测器：

import numpy as np

def train_gan(X_train, latent_dim, epochs):
    generator = build_generator(latent_dim, X_train.shape[1])
    discriminator = build_discriminator(X_train.shape[1])
    gan = build_gan(generator, discriminator)
    for epoch in range(epochs):
        noise = np.random.normal(0, 1, size=(X_train.shape[0], latent_dim))
        generated_data = generator.predict(noise)
        real_data = X_train[np.random.randint(0, X_train.shape[0], size=X_train.shape[0])]
        X = np.concatenate([real_data, generated_data])
        y_discriminator = np.zeros(2*X_train.shape[0])
        y_discriminator[:X_train.shape[0]] = 0.9
        discriminator.trainable = True
        discriminator_loss = discriminator.train_on_batch(X, y_discriminator)
        noise = np.random.normal(0, 1, size=(X_train.shape[0], latent_dim))
        y_generator = np.ones(X_train.shape[0])
        discriminator.trainable = False
        gan_loss = gan.train_on_batch(noise, y_generator)
        if epoch % 100 == 0:
            print("Epoch:", epoch, "Discriminator Loss:", discriminator_loss, "GAN Loss:", gan_loss)
    return generator

最后，我们可以使用生成器模型来生成新的数据，并将其与原始数据进行比较来检测异常：

def detect_anomalies(X_train, X_test, generator):
    generated_data = generator.predict(np.random.normal(0, 1, size=(X_test.shape[0], latent_dim)))
    errors = np.mean(np.power(X_test - generated_data, 2), axis=1)
    threshold = np.percentile(errors, 95)
    anomalies = X_test[errors > threshold]
    return anomalies

这是一个简单的使用GAN算法进行异常检测的示例。请注意，这只是一个简单的示例，实际上可能需要更多的调整和优化才能实现更好的性能。