写一个基于tensorflow的GAN网络，可以生成5列表格数据，保存训练好的判别器和鉴别器，给出代码

好的，以下是一个基于Tensorflow实现的GAN网络，用于生成5列表格数据：

import tensorflow as tf
import numpy as np

# 定义生成器
def generator(z, output_dim, n_hidden=128, n_layer=2):
    with tf.variable_scope("generator"):
        h = tf.layers.dense(z, n_hidden, activation=tf.nn.relu)
        for i in range(n_layer-1):
            h = tf.layers.dense(h, n_hidden, activation=tf.nn.relu)
        out = tf.layers.dense(h, output_dim, activation=None)
    return out

# 定义判别器
def discriminator(x, n_hidden=128, n_layer=2):
    with tf.variable_scope("discriminator"):
        h = tf.layers.dense(x, n_hidden, activation=tf.nn.relu)
        for i in range(n_layer-1):
            h = tf.layers.dense(h, n_hidden, activation=tf.nn.relu)
        out = tf.layers.dense(h, 1, activation=None)
    return out

# 定义输入占位符
input_dim = 5
z_dim = 10
X = tf.placeholder(tf.float32, shape=[None, input_dim])
z = tf.placeholder(tf.float32, shape=[None, z_dim])

# 定义损失函数
fake_X = generator(z, input_dim)
D_real = discriminator(X)
D_fake = discriminator(fake_X)
D_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_real, labels=tf.ones_like(D_real)) + tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.zeros_like(D_fake)))
G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_fake, labels=tf.ones_like(D_fake)))

# 定义优化器
D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
D_train_op = tf.train.AdamOptimizer().minimize(D_loss, var_list=D_vars)
G_train_op = tf.train.AdamOptimizer().minimize(G_loss, var_list=G_vars)

# 定义训练参数
batch_size = 32
epochs = 10000
display_step = 1000

# 开始训练
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(epochs):
    # 生成随机噪声
    batch_z = np.random.uniform(-1, 1, size=[batch_size, z_dim])
    # 生成真实数据
    batch_X = np.random.uniform(0, 1, size=[batch_size, input_dim])
    # 训练判别器
    _, D_loss_curr = sess.run([D_train_op, D_loss], feed_dict={X: batch_X, z: batch_z})
    # 训练生成器
    _, G_loss_curr = sess.run([G_train_op, G_loss], feed_dict={z: batch_z})
    
    # 显示损失
    if epoch % display_step == 0:
        print("Epoch: %d, D_loss: %f, G_loss: %f" % (epoch, D_loss_curr, G_loss_curr))

# 保存模型
saver = tf.train.Saver()
saver.save(sess, "model.ckpt")

该代码中，生成器使用多层全连接神经网络，输出5维向量，判别器也使用多层全连接神经网络，输出1维标量。训练过程中，使用交叉熵损失函数和Adam优化器。训练完成后，可以使用`saver.save`方法保存模型。