给我一段GAN模型的代码

这是一个GAN模型的示例代码：import numpy as np
import tensorflow as tf# 定义输入样本
X_inputs = tf.placeholder(shape=[None, 2], dtype=tf.float32)# 定义生成器
def generator(inputs):
w1 = tf.Variable(tf.random_normal([2, 4]))
b1 = tf.Variable(tf.random_normal([4]))
h1 = tf.nn.sigmoid(tf.matmul(inputs, w1) + b1)
w2 = tf.Variable(tf.random_normal([4, 2]))
b2 = tf.Variable(tf.random_normal([2]))
outputs = tf.nn.sigmoid(tf.matmul(h1, w2) + b2)
return outputs# 定义判别器
def discriminator(inputs):
w1 = tf.Variable(tf.random_normal([2, 4]))
b1 = tf.Variable(tf.random_normal([4]))
h1 = tf.nn.sigmoid(tf.matmul(inputs, w1) + b1)
w2 = tf.Variable(tf.random_normal([4, 1]))
b2 = tf.Variable(tf.random_normal([1]))
outputs = tf.nn.sigmoid(tf.matmul(h1, w2) + b2)
return outputs# 定义生成器和判别器
generator_model = generator(X_inputs)
discriminator_model_real = discriminator(X_inputs)
discriminator_model_fake = discriminator(generator_model)# 定义损失函数
# 生成器损失
g_loss = -tf.reduce_mean(tf.log(discriminator_model_fake))
# 判别器损失
d_loss_real = -tf.reduce_mean(tf.log(discriminator_model_real))
d_loss_fake = -tf.reduce_mean(tf.log(1. - discriminator_model_fake))
d_loss = d_loss_real + d_loss_fake# 定义优化器
learning_rate = 0.001
trainer_g = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator'))
trainer_d = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator'))# 训练
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)for i in range(10000):
# 构造训练数据
x_data = np.random.normal(0, 1, size=[16, 2])

# 训练判别器
sess.run(trainer_d, feed_dict={X_inputs: x_data})

# 训练生成器
sess.run(trainer_g, feed_dict={X_inputs: x_data})

if i % 1000 == 0:
print('step:', i)
generator_data = sess.run(generator_model, feed_dict={X_inputs: x_data})
label_x = np.array([[1, 0] for _ in range(16)])
label_y = np.array([[0, 1] for _ in range(16)])
discriminator_data_x = sess.run(discriminator_model_real, feed_dict={X_inputs: label_x})
discriminator_data_y = sess.run(discriminator_model_real, feed_dict={X_inputs: label_y})
discriminator_data_fake = sess.run(discriminator_model_fake, feed_dict={X_inputs: generator_data})
print('accuracy of real data:', np.mean(discriminator_data_x))
print('accuracy of fake data:', np.mean(discriminator_data_fake))
print('accuracy of generated data:', np.mean(discriminator_data_y))我为您提供了一段GAN模型的代码，希望对您有所帮助。