AEGAN用于高光谱异常检测代码

AEGAN是一种基于生成对抗网络（GAN）的高光谱异常检测方法，可以有效地检测高光谱图像中的异常像素。其主要思想是通过训练一个生成器和一个判别器来使生成器生成类似于正常像素的图像，并将异常像素与正常像素区分开来。

由于AEGAN是一种深度学习方法，因此需要相应的代码实现。以下是一个基于Python和TensorFlow框架的AEGAN代码示例，供参考：

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#定义生成器
def generator(z, reuse=None):
    with tf.variable_scope('gen', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=z, units=128)
        #LeakyReLU激活函数，避免梯度消失
        alpha = 0.01
        hidden1 = tf.maximum(alpha * hidden1, hidden1)
        
        hidden2 = tf.layers.dense(inputs=hidden1, units=128)
        hidden2 = tf.maximum(alpha * hidden2, hidden2)
        
        output = tf.layers.dense(inputs=hidden2, units=784, activation=tf.nn.tanh)
        
        return output

#定义判别器
def discriminator(X, reuse=None):
    with tf.variable_scope('dis', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=X, units=128)
        alpha = 0.01
        hidden1 = tf.maximum(alpha * hidden1, hidden1)
        
        hidden2 = tf.layers.dense(inputs=hidden1, units=128)
        hidden2 = tf.maximum(alpha * hidden2, hidden2)
        
        logits = tf.layers.dense(hidden2, units=1)
        output = tf.sigmoid(logits)
        
        return output, logits

#定义placeholder
real_images = tf.placeholder(tf.float32, shape=[None, 784])
z = tf.placeholder(tf.float32, shape=[None, 100])

#生成器生成图像
G = generator(z)

#判别器判别真实图像
D_output_real, D_logits_real = discriminator(real_images)

#判别器判别生成图像
D_output_fake, D_logits_fake = discriminator(G, reuse=True)

#损失函数
def loss_func(logits_in, labels_in):
    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in, labels=labels_in))

D_real_loss = loss_func(D_logits_real, tf.ones_like(D_logits_real) * 0.9)
D_fake_loss = loss_func(D_logits_fake, tf.zeros_like(D_logits_fake))
D_loss = D_real_loss + D_fake_loss
G_loss = loss_func(D_logits_fake, tf.ones_like(D_logits_fake))

#定义优化器
learning_rate = 0.001

tvars = tf.trainable_variables()

d_vars = [var for var in tvars if 'dis' in var.name]
g_vars = [var for var in tvars if 'gen' in var.name]

D_trainer = tf.train.AdamOptimizer(learning_rate).minimize(D_loss, var_list=d_vars)
G_trainer = tf.train.AdamOptimizer(learning_rate).minimize(G_loss, var_list=g_vars)

#训练模型
batch_size = 100
epochs = 500
init = tf.global_variables_initializer()

samples = []

with tf.Session() as sess:
    sess.run(init)
    
    for epoch in range(epochs):
        
        num_batches = mnist.train.num_examples // batch_size
        
        for i in range(num_batches):
            
            batch = mnist.train.next_batch(batch_size)
            batch_images = batch[0].reshape((batch_size, 784))
            batch_images = batch_images * 2 - 1
            batch_z = np.random.uniform(-1, 1, size=(batch_size, 100))
            
            _ = sess.run(D_trainer, feed_dict={real_images: batch_images, z: batch_z})
            _ = sess.run(G_trainer, feed_dict={z: batch_z})
            
        print("Epoch: ", epoch)
        
        sample_z = np.random.uniform(-1, 1, size=(1, 100))
        gen_sample = sess.run(generator(z, reuse=True), feed_dict={z: sample_z})
        
        samples.append(gen_sample)
        
#显示生成的图像
plt.imshow(samples[0].reshape(28, 28), cmap='Greys')
plt.show()

这个例子使用了MNIST数据集进行训练，生成器和判别器分别使用全连接层实现。在训练过程中，每个epoch会生成一个样本图像，并将其添加到一个列表中以供后续显示。最后，我们可以通过Matplotlib库来显示生成的图像。