使用tensorflow实现gan简单小demo

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
tf.set_random_seed(1)
np.random.seed(1)
BATCH_SIZE = 64
LR_G = 0.0001
LR_D = 0.0001
N_IDEAS = 5
ART_COMPONENTS = 15
PAINT_POINTS = np.vstack([np.linspace(-1,1,ART_COMPONENTS)for _ in range(BATCH_SIZE)])
#shape = (64,15)
print(PAINT_POINTS)
plt.plot(PAINT_POINTS[0],2*np.power(PAINT_POINTS[0],2)+1,c = '#74BCFF',lw = 3,label='upper bound')
plt.plot(PAINT_POINTS[0],1*np.power(PAINT_POINTS[0],2)+0,c = '#FF9359',lw = 3,label='lower bound')
plt.legend(loc = 'upper right')
plt.show()
def artist_works():
#即真实的数据
a = np.random.uniform(1,2,size=BATCH_SIZE)[:,np.newaxis]
#shape = (64,1)
paintings = a*np.power(PAINT_POINTS,2)+(a-1)
#shape = (64,15)
return paintings
with tf.variable_scope('Generator'):
#使用生成器伪造假的数据
G_in = tf.placeholder(tf.float32,[None,N_IDEAS])
#shape = (64,5)
G_l1 = tf.layers.dense(G_in,128,tf.nn.relu)
G_out = tf.layers.dense(G_l1,ART_COMPONENTS)
with tf.variable_scope('Discriminator'):
real_art = tf.placeholder(tf.float32,[None,ART_COMPONENTS],name='real_in')
#使用鉴别器来鉴别真实数据
D_l0 = tf.layers.dense(real_art,128,tf.nn.relu,name='1')
#并将它判别为1
prob_artist0 = tf.layers.dense(D_l0,1,tf.nn.sigmoid,name='out')
#fake art
D_l1 = tf.layers.dense(G_out,128,tf.nn.relu,name='1',reuse=True)
#使用费鉴别器来判别伪造数据
prob_artist1 = tf.layers.dense(D_l1,1,tf.nn.sigmoid,name='out',reuse=True)
#并将其判别为0
D_loss = -tf.reduce_mean(tf.log(prob_artist0)+tf.log(1-prob_artist1))
#定义误差函数
G_loss = tf.reduce_mean(tf.log(1-prob_artist1))
train_D = tf.train.AdamOptimizer(LR_D).minimize(
#定义优化函数
D_loss,var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='Discriminator'))
train_G = tf.train.AdamOptimizer(LR_G).minimize(
G_loss,var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,scope='Generator'))
sess= tf.Session()
#初始化流图
sess.run(tf.global_variables_initializer())
plt.ion()
for step in range(5000):
artist_paintings = artist_works()
G_ideas = np.random.randn(BATCH_SIZE,N_IDEAS)
G_paintings,pa0,D1 = sess.run([G_out,prob_artist0,D_loss,train_D,train_G],
{G_in:G_ideas,real_art:artist_paintings})[:3]
if step%50==0:
#可视化
plt.cla()
plt.plot(PAINT_POINTS[0],G_paintings[0],c='#4AD631',lw=3,label='Generated painting')
plt.plot(PAINT_POINTS[0],2*np.power(PAINT_POINTS[0],2)+1,c='#74BCFF',lw=3,label='upper bound')
plt.plot(PAINT_POINTS[0],1*np.power(PAINT_POINTS[0],2)+0,c='#FF9359',lw=3,label='lower bound')
plt.text(-.5,2.3,'D accuracy=%.2f (0.5 for D to converge)'%pa0.mean(),fontdict={'size':15})
plt.text(-.5,2,'D score=%.2f (-1.38 for G to converge)'%-D1,fontdict={'size':15})
plt.ylim((0,3));plt.legend(loc='upper right',fontsize=12);plt.draw();plt.pause(0.01)
plt.ioff()
plt.show()

本程序使用生成对抗网络（GAN）来实现一个小Demo：

目的 ：使用生成器拟合形如：a*X2+（a-1）

对于a 我们使用np.random.uniform函数随机生成（1-2之间）

为了直观的看到生成器生成的数据，我们设置了上下两个边界。

最终实验效果：

注释：此文为莫凡Python学习笔记 莫凡tensorflow

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