如何使用 Python 堆实现生成对抗网络算法？

import numpy as np
import tensorflow as tf
import heapq
from tensorflow.examples.tutorials.mnist import input_data

# 随机生成器的输入形状
random_dim = 10
# 真实数据的输入形状
image_dim = 784
# 生成器隐藏层神经元数量
gen_hidden_dim = 256
# 判别器隐藏层神经元数量
dis_hidden_dim = 256
# 训练过程使用的迭代次数
iterations = 100000
# 每次迭代时使用的样本数量
batch_size = 100

# 定义随机生成器的函数
def generator(x, reuse=False):
    with tf.variable_scope('generator', reuse=reuse):
        # 将输入的低维随机噪声x映射到高维空间
        hidden_layer = tf.layers.dense(inputs=x, units=gen_hidden_dim, activation=tf.nn.relu)
        # 将映射后的数据映射到目标数据空间
        output = tf.layers.dense(inputs=hidden_layer, units=image_dim, activation=tf.nn.sigmoid)
        return output

# 定义判别器的函数
def discriminator(x, reuse=False):
    with tf.variable_scope('discriminator', reuse=reuse):
        # 将输入的数据映射到高维空间
        hidden_layer = tf.layers.dense(inputs=x, units=dis_hidden_dim, activation=tf.nn.relu)
        # 判断输入数据的真假性
        output = tf.layers.dense(inputs=hidden_layer, units=1, activation=tf.nn.sigmoid)
        return output

# 定义模型输入占位符
random_input = tf.placeholder(tf.float32, shape=[None, random_dim], name='random_input')
real_data = tf.placeholder(tf.float32, shape=[None, image_dim], name='real_data')

# 定义生成器的损失函数
fake_data = generator(random_input, reuse=False)
dis_fake_data = discriminator(fake_data, reuse=False)
gen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_fake_data, labels=tf.ones_like(dis_fake_data)))

# 定义判别器的损失函数
dis_real_data = discriminator(real_data, reuse=False)
dis_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_real_data, labels=tf.ones_like(dis_real_data)))
dis_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_fake_data, labels=tf.zeros_like(dis_fake_data)))
dis_loss = dis_loss_real + dis_loss_fake

# 定义优化器
gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
dis_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
gen_optimizer = tf.train.AdamOptimizer(0.0001).minimize(gen_loss, var_list=gen_vars)
dis_optimizer = tf.train.AdamOptimizer(0.0001).minimize(dis_loss, var_list=dis_vars)

# 读取 MNIST 数据集
mnist = input_data.read_data_sets('MNIST_data')

# 创建会话
with tf.Session() as sess:
    # 初始化变量
    init = tf.global_variables_initializer()
    sess.run(init)

    # 迭代训练
    for i in range(iterations):
        # 获取一批随机噪声
        random_batch = np.random.uniform(-1, 1, [batch_size, random_dim])
        # 获取一批真实数据
        real_batch, _ = mnist.train.next_batch(batch_size)

        # 训练生成器
        _, gen_loss_val = sess.run([gen_optimizer, gen_loss], feed_dict={random_input: random_batch})

        # 训练判别器
        _, dis_loss_val = sess.run([dis_optimizer, dis_loss], feed_dict={real_data: real_batch, random_input: random_batch})

        # 打印损失结果
        if i % 1000 == 0:
            print('Iteration: %d, Generator Loss: %f, Discriminator Loss: %f' % (i, gen_loss_val, dis_loss_val))