1、结构图
2、知识点
生成器(G):将噪音数据生成一个想要的数据
判别器(D):将生成器的结果进行判别,
3、代码及案例
# coding: utf-8
# ## 对抗生成网络案例 ##
#
#
# <img src="jpg/3.png" alt="FAO" width="590" >
# - 判别器 : 火眼金睛,分辨出生成和真实的 <br />
# <br />
# - 生成器 : 瞒天过海,骗过判别器 <br />
# <br />
# - 损失函数定义 : 一方面要让判别器分辨能力更强,另一方面要让生成器更真 <br />
# <br />
#
# <img src="jpg/1.jpg" alt="FAO" width="590" >
# In[1]:
import tensorflow as tf
import numpy as np
import pickle
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')
# # 导入数据
# In[2]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/data')
# ## 网络架构
#
# ### 输入层 :待生成图像(噪音)和真实数据
#
# ### 生成网络:将噪音图像进行生成
#
# ### 判别网络:
# - (1)判断真实图像输出结果
# - (2)判断生成图像输出结果
#
# ### 目标函数:
# - (1)对于生成网络要使得生成结果通过判别网络为真
# - (2)对于判别网络要使得输入为真实图像时判别为真 输入为生成图像时判别为假
#
# <img src="jpg/2.png" alt="FAO" width="590" >
# ## Inputs
# In[3]:
#真实数据和噪音数据
def get_inputs(real_size, noise_size):
real_img = tf.placeholder(tf.float32, [None, real_size])
noise_img = tf.placeholder(tf.float32, [None, noise_size])
return real_img, noise_img
# ## 生成器
# * noise_img: 产生的噪音输入
# * n_units: 隐层单元个数
# * out_dim: 输出的大小(28 * 28 * 1)
# In[4]:
def get_generator(noise_img, n_units, out_dim, reuse=False, alpha=0.01):
with tf.variable_scope("generator", reuse=reuse):
# hidden layer
hidden1 = tf.layers.dense(noise_img, n_units)
# leaky ReLU
hidden1 = tf.maximum(alpha * hidden1, hidden1)
# dropout
hidden1 = tf.layers.dropout(hidden1, rate=0.2)
# logits & outputs
logits = tf.layers.dense(hidden1, out_dim)
outputs = tf.tanh(logits)
return logits, outputs
# ## 判别器
# * img:输入
# * n_units:隐层单元数量
# * reuse:由于要使用两次
# In[5]:
def get_discriminator(img, n_units, reuse=False, alpha=0.01):
with tf.variable_scope("discriminator", reuse=reuse):
# hidden layer
hidden1 = tf.layers.dense(img, n_units)
hidden1 = tf.maximum(alpha * hidden1, hidden1)
# logits & outputs
logits = tf.layers.dense(hidden1, 1)
outputs = tf.sigmoid(logits)
return logits, outputs
# ## 网络参数定义
# * img_size:输入大小
# * noise_size:噪音图像大小
# * g_units:生成器隐层参数
# * d_units:判别器隐层参数
# * learning_rate:学习率
# In[6]:
img_size = mnist.train.images[0].shape[0]
noise_size = 100
g_units = 128
d_units = 128
learning_rate = 0.001
alpha = 0.01
# ## 构建网络
# In[7]:
tf.reset_default_graph()
real_img, noise_img = get_inputs(img_size, noise_size)
# generator
g_logits, g_outputs = get_generator(noise_img, g_units, img_size)
# discriminator
d_logits_real, d_outputs_real = get_discriminator(real_img, d_units)
d_logits_fake, d_outputs_fake = get_discriminator(g_outputs, d_units, reuse=True)
# ### 目标函数:
# - (1)对于生成网络要使得生成结果通过判别网络为真
# - (2)对于判别网络要使得输入为真实图像时判别为真 输入为生成图像时判别为假
#
# <img src="jpg/2.png" alt="FAO" width="590" >
# In[8]:
# discriminator的loss
# 识别真实图片
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real)))
# 识别生成的图片
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_logits_fake)))
# 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=d_logits_fake,
labels=tf.ones_like(d_logits_fake)))
# ## 优化器
# In[9]:
train_vars = tf.trainable_variables()
# generator
g_vars = [var for var in train_vars if var.name.startswith("generator")]
# discriminator
d_vars = [var for var in train_vars if var.name.startswith("discriminator")]
# optimizer
d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)
# # 训练
# In[10]:
# batch_size
batch_size = 64
# 训练迭代轮数
epochs = 300
# 抽取样本数
n_sample = 25
# 存储测试样例
samples = []
# 存储loss
losses = []
# 保存生成器变量
saver = tf.train.Saver(var_list = g_vars)
# 开始训练
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for batch_i in range(mnist.train.num_examples//batch_size):
batch = mnist.train.next_batch(batch_size)
batch_images = batch[0].reshape((batch_size, 784))
# 对图像像素进行scale,这是因为tanh输出的结果介于(-1,1),real和fake图片共享discriminator的参数
batch_images = batch_images*2 - 1
# generator的输入噪声
batch_noise = np.random.uniform(-1, 1, size=(batch_size, noise_size))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={real_img: batch_images, noise_img: batch_noise})
_ = sess.run(g_train_opt, feed_dict={noise_img: batch_noise})
# 每一轮结束计算loss
train_loss_d = sess.run(d_loss,
feed_dict = {real_img: batch_images,
noise_img: batch_noise})
# real img loss
train_loss_d_real = sess.run(d_loss_real,
feed_dict = {real_img: batch_images,
noise_img: batch_noise})
# fake img loss
train_loss_d_fake = sess.run(d_loss_fake,
feed_dict = {real_img: batch_images,
noise_img: batch_noise})
# generator loss
train_loss_g = sess.run(g_loss,
feed_dict = {noise_img: batch_noise})
print("Epoch {}/{}...".format(e+1, epochs),
"判别器损失: {:.4f}(判别真实的: {:.4f} + 判别生成的: {:.4f})...".format(train_loss_d, train_loss_d_real, train_loss_d_fake),
"生成器损失: {:.4f}".format(train_loss_g))
losses.append((train_loss_d, train_loss_d_real, train_loss_d_fake, train_loss_g))
# 保存样本
sample_noise = np.random.uniform(-1, 1, size=(n_sample, noise_size))
gen_samples = sess.run(get_generator(noise_img, g_units, img_size, reuse=True),
feed_dict={noise_img: sample_noise})
samples.append(gen_samples)
saver.save(sess, './checkpoints/generator.ckpt')
# 保存到本地
with open('train_samples.pkl', 'wb') as f:
pickle.dump(samples, f)
# # loss迭代曲线
# In[11]:
fig, ax = plt.subplots(figsize=(20,7))
losses = np.array(losses)
plt.plot(losses.T[0], label='判别器总损失')
plt.plot(losses.T[1], label='判别真实损失')
plt.plot(losses.T[2], label='判别生成损失')
plt.plot(losses.T[3], label='生成器损失')
plt.title("对抗生成网络")
ax.set_xlabel('epoch')
plt.legend()
# # 生成结果
# In[12]:
# Load samples from generator taken while training
with open('train_samples.pkl', 'rb') as f:
samples = pickle.load(f)
# In[13]:
#samples是保存的结果 epoch是第多少次迭代
def view_samples(epoch, samples):
fig, axes = plt.subplots(figsize=(7,7), nrows=5, ncols=5, sharey=True, sharex=True)
for ax, img in zip(axes.flatten(), samples[epoch][1]): # 这里samples[epoch][1]代表生成的图像结果,而[0]代表对应的logits
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')
return fig, axes
# In[14]:
_ = view_samples(-1, samples) # 显示最终的生成结果
# # 显示整个生成过程图片
# In[15]:
# 指定要查看的轮次
epoch_idx = [10, 30, 60, 90, 120, 150, 180, 210, 240, 290]
show_imgs = []
for i in epoch_idx:
show_imgs.append(samples[i][1])
# In[16]:
# 指定图片形状
rows, cols = 10, 25
fig, axes = plt.subplots(figsize=(30,12), nrows=rows, ncols=cols, sharex=True, sharey=True)
idx = range(0, epochs, int(epochs/rows))
for sample, ax_row in zip(show_imgs, axes):
for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):
ax.imshow(img.reshape((28,28)), cmap='Greys_r')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
# # 生成新的图片
# In[17]:
# 加载我们的生成器变量
saver = tf.train.Saver(var_list=g_vars)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))
sample_noise = np.random.uniform(-1, 1, size=(25, noise_size))
gen_samples = sess.run(get_generator(noise_img, g_units, img_size, reuse=True),
feed_dict={noise_img: sample_noise})
# In[18]:
_ = view_samples(0, [gen_samples])
View Code
4、优化目标