先放结果
这是通过GAN迭代训练30W次,耗时3小时生成的手写字图片效果,大部分的还是能看出来是数字的。
实现原理
简单说下原理,生成对抗网络需要训练两个任务,一个叫生成器,一个叫判别器,如字面意思,一个负责生成图片,一个负责判别图片,生成器不断生成新的图片,然后判别器去判断哪儿哪儿不行,生成器再不断去改进,不断的像真实的图片靠近。
这就如同一个造假团伙一样,A负责生产,B负责就鉴定,刚开始的时候,两个人都是菜鸟,A随便画了一幅画拿给B看,B说你这不行,然后A再改进,当然需要改进的不止A,随着A的改进,B也得不断提升,B需要发现更细微的差异,直至他们觉得已经没什么差异了(实际肯定还存在差异),他们便决定停止"训练",开始卖吧。
实现代码
# -*- coding: utf-8 -*-
# @author: Awesome_Tang
# @date: 2019-02-22
# @version: python2.7
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from datetime import datetime
import numpy as np
import os
import matplotlib.pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
class Config:
alpha = 1e-2
drop_rate = 0.5 # 保留比例
steps = 300000 # 迭代次数
batch_size = 128 # 每批次训练样本数
epochs = 100 # 训练轮次
num_units = 128
size = 784
noise_size = 100
smooth = 0.01
learning_rate = 1e-4
print_per_step = 1000
class Gan:
def __init__(self):
print('Loading data......')
# 读取MNIST数据集
self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 定义占位符,真实图片和生成的图片
self.real_images = tf.placeholder(tf.float32, [None, Config.size], name='real_images')
self.noise = tf.placeholder(tf.float32, [None, Config.noise_size], name='noise')
self.drop_rate = tf.placeholder('float')
self.train_step()
def generator_graph(self, noise, n_units, out_dim, alpha, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
# Hidden layer
h1 = tf.layers.dense(noise, n_units, activation=None)
# Leaky ReLU
h1 = tf.maximum(alpha * h1, h1)
h1 = tf.layers.dropout(h1, rate=self.drop_rate)
# Logits and tanh output
logits = tf.layers.dense(h1, out_dim, activation=None)
out = tf.tanh(logits)
return out
@staticmethod
def discriminator_graph(image, n_units, alpha, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
# Hidden layer
h1 = tf.layers.dense(image, n_units, activation=None)
# Leaky ReLU
h1 = tf.maximum(alpha * h1, h1)
logits = tf.layers.dense(h1, 1, activation=None)
# out = tf.sigmoid(logits)
return logits
def net(self):
# generator
fake_image = self.generator_graph(self.noise, Config.num_units, Config.size, Config.alpha)
# discriminator
real_logits = self.discriminator_graph(self.real_images, Config.num_units, Config.alpha)
fake_logits = self.discriminator_graph(fake_image, Config.num_units, Config.alpha, reuse=True)
# discriminator的loss
# 识别真实图片
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=real_logits, labels=tf.ones_like(real_logits)) * (
1 - Config.smooth))
# 识别生成的图片
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.zeros_like(fake_logits)))
# 总体loss
d_loss = tf.add(d_loss_real, d_loss_fake)
# generator的loss
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=fake_logits, labels=tf.ones_like(fake_logits)) * (
1 - Config.smooth))
net_vars = tf.trainable_variables()
# generator中的tensor
g_vars = [var for var in net_vars if var.name.startswith("generator")]
# discriminator中的tensor
d_vars = [var for var in net_vars if var.name.startswith("discriminator")]
# optimizer
dis_optimizer = tf.train.AdamOptimizer(Config.learning_rate).minimize(d_loss, var_list=d_vars)
gen_optimizer = tf.train.AdamOptimizer(Config.learning_rate).minimize(g_loss, var_list=g_vars)
return dis_optimizer, gen_optimizer, d_loss, g_loss
def train_step(self):
dis_optimizer, gen_optimizer, d_loss, g_loss = self.net()
print('Training & Evaluating......')
start_time = datetime.now()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for step in range(Config.steps):
real_image, _ = self.mnist.train.next_batch(Config.batch_size)
real_image = real_image * 2 - 1
# generator的输入噪声
batch_noise = np.random.uniform(-1, 1, size=(Config.batch_size, Config.noise_size))
sess.run(gen_optimizer, feed_dict={self.noise: batch_noise, self.drop_rate: Config.drop_rate})
sess.run(dis_optimizer, feed_dict={self.noise: batch_noise, self.real_images: real_image})
if step % Config.print_per_step == 0:
dis_loss = sess.run(d_loss, feed_dict={self.noise: batch_noise, self.real_images: real_image})
gen_loss = sess.run(g_loss, feed_dict={self.noise: batch_noise, self.drop_rate: 1.})
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
msg = 'Step {:3}k Dis_Loss:{:6.2f}, Gen_Loss:{:6.2f}, Time_Usage:{:6.2f} mins.'
print(msg.format(int(step / 1000), dis_loss, gen_loss, time_diff / 60.))
self.gen_image(sess)
def gen_image(self, sess):
sample_noise = np.random.uniform(-1, 1, size=(25, Config.noise_size))
samples = sess.run(
self.generator_graph(self.noise, Config.num_units, Config.size, Config.alpha, reuse=True),
feed_dict={self.noise: sample_noise})
plt.figure(figsize=(8, 8), dpi=80)
for i in range(25):
img = samples[i]
plt.subplot(5, 5, i + 1)
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
plt.axis('off')
plt.show()
if __name__ == "__main__":
Gan()
Peace~~
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