#coding=utf-8
#http://blog.csdn.net/u012223913/article/details/75051516?locationNum=1&fps=1
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from tensorflow.examples.tutorials.mnist import input_data
sess = tf.InteractiveSession()
mb_size = 128
Z_dim = 100
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
def weight_var(shape, name):
return tf.get_variable(name=name, shape=shape, initializer=tf.contrib.layers.xavier_initializer())
def bias_var(shape, name):
return tf.get_variable(name=name, shape=shape, initializer=tf.constant_initializer(0))
# discriminater net
X = tf.placeholder(tf.float32, shape=[None, 784], name='X')
# X [128 784] z1 = W * x + b
# 矩阵乘法 128 * 784 * 784 * 128 = [128 128]
D_W1 = weight_var([784, 128], 'D_W1')
D_b1 = bias_var([128], 'D_b1')
#z2 = W * z1 + b
# 矩阵乘法 128 * 128 * 128 * 1 = [128 1] 输出判决结果，二分类
D_W2 = weight_var([128, 1], 'D_W2')
D_b2 = bias_var([1], 'D_b2')
theta_D = [D_W1, D_W2, D_b1, D_b2]
# generator net
Z = tf.placeholder(tf.float32, shape=[None, 100], name='Z')
# z [128 784] z1 = W * x + b
# 矩阵乘法 128 * 100 * 100 * 128 = [128 128]
G_W1 = weight_var([100, 128], 'G_W1')
G_b1 = bias_var([128], 'G_B1')
#z2 = W * z1 + b
# 矩阵乘法 128 * 128 * 128 * 784 = [128 784] 输出28*28的图像
G_W2 = weight_var([128, 784], 'G_W2')
G_b2 = bias_var([784], 'G_B2')
theta_G = [G_W1, G_W2, G_b1, G_b2]
def generator(z):
G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)
G_log_prob = tf.matmul(G_h1, G_W2) + G_b2
G_prob = tf.nn.sigmoid(G_log_prob)
return G_prob
def discriminator(x):
D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)
D_logit = tf.matmul(D_h1, D_W2) + D_b2
D_prob = tf.nn.sigmoid(D_logit)
return D_prob, D_logit
G_sample = generator(Z)
D_real, D_logit_real = discriminator(X)
D_fake, D_logit_fake = discriminator(G_sample)
#discriminator输出为1表示ground truth
#discriminator输出为0表示非ground truth
#对于生成网络希望两点：
#(2)希望D_real尽可能大，这样保证正确识别真正的样本
#(1)希望D_fake尽可能小，这样可以剔除假的生成样本
D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
#对于判别网络, 希望D_fake尽可能大，这样可以迷惑生成网络，
G_loss = -tf.reduce_mean(tf.log(D_fake))
D_optimizer = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
G_optimizer = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# 启动默认图
sess = tf.Session()
# 初始化
sess.run(init)
def sample_Z(m, n):
'''Uniform prior for G(Z)'''
return np.random.uniform(-1., 1., size=[m, n])
def plot(samples):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples): # [i,samples[i]] imax=16
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_aspect('equal')
plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
return fig
if not os.path.exists('out/'):
os.makedirs('out/')
i = 0
for it in range(1000000):
if it % 1000 == 0:
samples = sess.run(G_sample, feed_dict={
Z: sample_Z(16, Z_dim)}) # 16*784
fig = plot(samples)
plt.savefig('out/{}.png'.format(str(i).zfill(3)), bbox_inches='tight')
i += 1
plt.close(fig)
X_mb, _ = mnist.train.next_batch(mb_size)#ground truth
_, D_loss_curr = sess.run([D_optimizer, D_loss], feed_dict={
X: X_mb, Z: sample_Z(mb_size, Z_dim)})
_, G_loss_curr = sess.run([G_optimizer, G_loss], feed_dict={
Z: sample_Z(mb_size, Z_dim)})
if it % 1000 == 0:
print('Iter: {}'.format(it))
print('D loss: {:.4}'.format(D_loss_curr))
print('G_loss: {:.4}'.format(G_loss_curr))
print()
#http://blog.csdn.net/sparkexpert/article/details/70147409
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
#from skimage.io import imsave
import scipy
import os
import shutil
img_height = 28
img_width = 28
img_size = img_height * img_width
to_train = True
to_restore = False
output_path = "output"
# 总迭代次数500
max_epoch = 500
h1_size = 150
h2_size = 300
z_size = 100
batch_size = 256
# generate (model 1)
def build_generator(z_prior):
w1 = tf.Variable(tf.truncated_normal([z_size, h1_size], stddev=0.1), name="g_w1", dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h1_size]), name="g_b1", dtype=tf.float32)
h1 = tf.nn.relu(tf.matmul(z_prior, w1) + b1)
w2 = tf.Variable(tf.truncated_normal([h1_size, h2_size], stddev=0.1), name="g_w2", dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h2_size]), name="g_b2", dtype=tf.float32)
h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
w3 = tf.Variable(tf.truncated_normal([h2_size, img_size], stddev=0.1), name="g_w3", dtype=tf.float32)
b3 = tf.Variable(tf.zeros([img_size]), name="g_b3", dtype=tf.float32)
h3 = tf.matmul(h2, w3) + b3
x_generate = tf.nn.tanh(h3)
g_params = [w1, b1, w2, b2, w3, b3]
return x_generate, g_params
# discriminator (model 2)
def build_discriminator(x_data, x_generated, keep_prob):
# tf.concat
x_in = tf.concat([x_data, x_generated], 0)
w1 = tf.Variable(tf.truncated_normal([img_size, h2_size], stddev=0.1), name="d_w1", dtype=tf.float32)
b1 = tf.Variable(tf.zeros([h2_size]), name="d_b1", dtype=tf.float32)
h1 = tf.nn.dropout(tf.nn.relu(tf.matmul(x_in, w1) + b1), keep_prob)
w2 = tf.Variable(tf.truncated_normal([h2_size, h1_size], stddev=0.1), name="d_w2", dtype=tf.float32)
b2 = tf.Variable(tf.zeros([h1_size]), name="d_b2", dtype=tf.float32)
h2 = tf.nn.dropout(tf.nn.relu(tf.matmul(h1, w2) + b2), keep_prob)
w3 = tf.Variable(tf.truncated_normal([h1_size, 1], stddev=0.1), name="d_w3", dtype=tf.float32)
b3 = tf.Variable(tf.zeros([1]), name="d_b3", dtype=tf.float32)
h3 = tf.matmul(h2, w3) + b3
y_data = tf.nn.sigmoid(tf.slice(h3, [0, 0], [batch_size, -1], name=None))
y_generated = tf.nn.sigmoid(tf.slice(h3, [batch_size, 0], [-1, -1], name=None))
d_params = [w1, b1, w2, b2, w3, b3]
return y_data, y_generated, d_params
#
def show_result(batch_res, fname, grid_size=(8, 8), grid_pad=5):
batch_res = 0.5 * batch_res.reshape((batch_res.shape[0], img_height, img_width)) + 0.5
img_h, img_w = batch_res.shape[1], batch_res.shape[2]
grid_h = img_h * grid_size[0] + grid_pad * (grid_size[0] - 1)
grid_w = img_w * grid_size[1] + grid_pad * (grid_size[1] - 1)
img_grid = np.zeros((grid_h, grid_w), dtype=np.uint8)
for i, res in enumerate(batch_res):
if i >= grid_size[0] * grid_size[1]:
break
img = (res) * 255
img = img.astype(np.uint8)
row = (i // grid_size[0]) * (img_h + grid_pad)
col = (i % grid_size[1]) * (img_w + grid_pad)
img_grid[row:row + img_h, col:col + img_w] = img
#imsave(fname, img_grid)
#img.save('output/num.jpg')
scipy.misc.imsave(fname, img_grid)
def train():
# load data（mnist手写数据集）
mnist = input_data.read_data_sets('mnist_data', one_hot=True)
x_data = tf.placeholder(tf.float32, [batch_size, img_size], name="x_data")
z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior")
keep_prob = tf.placeholder(tf.float32, name="keep_prob")
global_step = tf.Variable(0, name="global_step", trainable=False)
# 创建生成模型
x_generated, g_params = build_generator(z_prior)
# 创建判别模型
y_data, y_generated, d_params = build_discriminator(x_data, x_generated, keep_prob)
# 损失函数的设置
d_loss = - (tf.log(y_data) + tf.log(1 - y_generated))
g_loss = - tf.log(y_generated)
optimizer = tf.train.AdamOptimizer(0.0001)
# 两个模型的优化函数
d_trainer = optimizer.minimize(d_loss, var_list=d_params)
g_trainer = optimizer.minimize(g_loss, var_list=g_params)
init = tf.initialize_all_variables()
saver = tf.train.Saver()
# 启动默认图
sess = tf.Session()
# 初始化
sess.run(init)
if to_restore:
chkpt_fname = tf.train.latest_checkpoint(output_path)
saver.restore(sess, chkpt_fname)
else:
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.mkdir(output_path)
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
steps = 60000 / batch_size
for i in range(sess.run(global_step), max_epoch):
for j in np.arange(steps):
# for j in range(steps):
print("epoch:%s, iter:%s" % (i, j))
# 每一步迭代，我们都会加载256个训练样本，然后执行一次train_step
x_value, _ = mnist.train.next_batch(batch_size)
x_value = 2 * x_value.astype(np.float32) - 1
z_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
# 执行生成
sess.run(d_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
# 执行判别
if j % 1 == 0:
sess.run(g_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_sample_val})
show_result(x_gen_val, "output/sample{0}.jpg".format(i))
z_random_sample_val = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_random_sample_val})
show_result(x_gen_val, "output/random_sample{0}.jpg".format(i))
sess.run(tf.assign(global_step, i + 1))
saver.save(sess, os.path.join(output_path, "model"), global_step=global_step)
def test():
z_prior = tf.placeholder(tf.float32, [batch_size, z_size], name="z_prior")
x_generated, _ = build_generator(z_prior)
chkpt_fname = tf.train.latest_checkpoint(output_path)
init = tf.initialize_all_variables()
sess = tf.Session()
saver = tf.train.Saver()
sess.run(init)
saver.restore(sess, chkpt_fname)
z_test_value = np.random.normal(0, 1, size=(batch_size, z_size)).astype(np.float32)
x_gen_val = sess.run(x_generated, feed_dict={z_prior: z_test_value})
show_result(x_gen_val, "output/test_result.jpg")
if __name__ == '__main__':
if to_train:
train()
else:
test()