GAN对抗生成神经网络训练生成二次元妹子代码

这里提供一个简单的GAN训练生成二次元妹子的代码，需要使用Python和Tensorflow库：

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

mnist = input_data.read_data_sets("MNIST_data/")

# 定义生成器网络
def generator(z, reuse=None):
    with tf.variable_scope('gen', reuse=reuse):
        hidden1 = tf.layers.dense(inputs=z, 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)
        output = tf.layers.dense(inputs=hidden2, units=784, activation=tf.nn.tanh)
        return output

# 定义判别器网络
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

# 定义输入占位符
real_images = tf.placeholder(tf.float32, shape=[None, 784])
z = tf.placeholder(tf.float32, shape=[None, 100])

# 定义损失函数
G = generator(z)
D_output_real, D_logits_real = discriminator(real_images)
D_output_fake, D_logits_fake = discriminator(G, reuse=True)

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) # 平滑标签
D_fake_loss = loss_func(D_logits_fake, tf.zeros_like(D_logits_real))
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
lr = 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(lr).minimize(D_loss, var_list=d_vars)
G_trainer = tf.train.AdamOptimizer(lr).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  # 将像素值调整到[-1, 1]区间
            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: {}/{}".format(epoch+1, epochs))
        # 每10个epoch保存一次生成的样本
        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)

# 显示生成的图片
plt.imshow(samples[0].reshape(28,28), cmap='Greys')
plt.show()

该代码使用了MNIST数据集作为训练数据，生成器和判别器的结构均为两层全连接层，使用了Adam优化器。生成器的输入是随机噪声，输出是28x28像素的二次元妹子图片。