对抗自编码降维代码示例

以下是一个简单的对抗自编码器的Python示例代码，用于实现降维：

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

# 定义对抗自编码器的生成器和判别器
def make_generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(256, activation='relu', input_shape=(100,)))
    model.add(layers.Dense(784, activation='sigmoid'))
    model.add(layers.Reshape((28, 28, 1)))
    return model

def make_discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Flatten())
    model.add(layers.Dense(256, activation='relu'))
    model.add(layers.Dense(1, activation='sigmoid'))
    return model

# 定义损失函数和优化器
cross_entropy = tf.keras.losses.BinaryCrossentropy()

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)

# 定义对抗自编码器模型
class AdversarialAutoencoder(tf.keras.Model):
    def __init__(self, latent_dim):
        super(AdversarialAutoencoder, self).__init__()
        self.latent_dim = latent_dim
        self.generator = make_generator_model()
        self.discriminator = make_discriminator_model()

    def call(self, inputs):
        x = self.generator(inputs)
        return x

    def train_step(self, data):
        real_images = data

        # 训练判别器
        with tf.GradientTape() as disc_tape:
            # 从噪声中生成假图像
            noise = tf.random.normal([real_images.shape[0], self.latent_dim])
            fake_images = self.generator(noise)

            # 计算判别器损失
            real_output = self.discriminator(real_images)
            fake_output = self.discriminator(fake_images)
            disc_loss = discriminator_loss(real_output, fake_output)

        # 计算判别器梯度并更新
        gradients_of_discriminator = disc_tape.gradient(disc_loss, self.discriminator.trainable_variables)
        discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, self.discriminator.trainable_variables))

        # 训练生成器
        with tf.GradientTape() as gen_tape:
            # 从噪声中生成假图像
            noise = tf.random.normal([real_images.shape[0], self.latent_dim])
            fake_images = self.generator(noise)

            # 计算生成器损失
            fake_output = self.discriminator(fake_images)
            gen_loss = generator_loss(fake_output)

        # 计算生成器梯度并更新
        gradients_of_generator = gen_tape.gradient(gen_loss, self.generator.trainable_variables)
        generator_optimizer.apply_gradients(zip(gradients_of_generator, self.generator.trainable_variables))

        return {'discriminator_loss': disc_loss, 'generator_loss': gen_loss}

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 标准化像素值
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

# 将图像展平
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

# 定义对抗自编码器模型和训练参数
aae = AdversarialAutoencoder(latent_dim=100)
aae.compile(optimizer=tf.keras.optimizers.Adam(1e-4))

# 训练对抗自编码器
aae.fit(x_train, x_train, epochs=20, batch_size=128, validation_data=(x_test, x_test))

这个代码示例使用了MNIST数据集，将图像展平后进行降维。对抗自编码器使用了一个生成器和一个判别器，通过对抗训练来实现降维。每个训练步骤中，先训练判别器，然后训练生成器，以此来实现对抗训练。最终的目标是让生成器可以生成与输入数据相似的噪声图像，这些噪声图像可以被判别器正确地分类为真实数据。