GANs (Generative Adversarial Networks)

#!/usr/bin/python2.7
#coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
# Hyper Parameters
BATCH_SIZE = 64
LR_G = 0.0001 # learning rate for generator
LR_D = 0.0001 # learning rate for discriminator
N_IDEAS = 5 # think of this as number of ideas for generating an art work(Generator)
ART_COMPONENTS = 15
# it could be total point G can draw in the canvas 5个灵感生成的15个线段
PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)]) #纵轴连接(64,15)
# show our beautiful painting range
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')
# savefig('./GAN_range.jpg')
plt.show()
def artist_works():
# painting from the famous artist (real target)
    a = np.random.uniform(1, 2, size=BATCH_SIZE)[:, np.newaxis]

# 随机生成一个一元二次函数的参数
    paintings = a * np.power(PAINT_POINTS, 2) + (a-1)
    return paintings
with tf.variable_scope('Generator'):
    G_in = tf.placeholder(tf.float32, [None, N_IDEAS])
# random ideas (could from normal distribution)
    G_l1 = tf.layers.dense(G_in, 128, tf.nn.relu)
    G_out = tf.layers.dense(G_l1, ART_COMPONENTS)
# making a painting fromthese random ideas
with tf.variable_scope('Discriminator'):
    real_art = tf.placeholder(tf.float32, [None, ART_COMPONENTS], name='real_in')
    #receive art work from the famous artist
    D_l0 = tf.layers.dense(real_art, 128, tf.nn.relu, name='l')
    prob_artist0 = tf.layers.dense(D_l0, 1, tf.nn.sigmoid, name='out')
#probability that the art work is made by artist
# reuse layers for generator
    D_l1 = tf.layers.dense(G_out, 128, tf.nn.relu, name='l', reuse=True)
#receive art work from a newbie like G
    prob_artist1 = tf.layers.dense(D_l1, 1, tf.nn.sigmoid, name='out', reuse=True)
#probability that the art work is made by artist
D_loss = -tf.reduce_mean(tf.log(prob_artist0) + tf.log(1-prob_artist1)) #minimize -
G_loss = tf.reduce_mean(tf.log(1-prob_artist1))
train_D = tf.train.AdamOptimizer(LR_D).minimize(
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()
sess.run(tf.global_variables_initializer())
plt.ion()
# something about continuous plotting
for step in range(5000):
    artist_paintings = artist_works()
# real painting from artist
    G_ideas = np.random.randn(BATCH_SIZE, N_IDEAS)
# 通过灵感来画画
    G_paintings, pa0, Dl = sess.run([G_out, prob_artist0, D_loss, train_D, train_G],
# train and get results
    {G_in: G_ideas, real_art: artist_paintings})[:3]
    if step % 50 == 0:
# plotting
        plt.cla()
        plt.plot(PAINT_POINTS[0], G_paintings[0], c='#4AD631', lw=3, label='Generatedpainting',)
        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)' % -Dl, fontdict={'size': 15})
        plt.ylim((0, 3)); plt.legend(loc='upper right', fontsize=12); plt.draw();
        plt.pause(0.01)
plt.ioff()
# savefig('./GAN.jpg')
plt.show()