tensorflow 1.01中GAN(生成對抗網路)手寫字型生成例子(MINST)的測試
為了更好地掌握GAN的例子，從網上找了段程式碼進行跑了下，測試了效果。具體過程如下：
程式碼檔案如下：
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
from skimage.io import imsave
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"
# 總迭代次數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)
def train():
# load data（mnist手寫資料集）
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)
# 建立生成模型
x_generated, g_params = build_generator(z_prior)
# 建立判別模型
y_data, y_generated, d_params = build_discriminator(x_data, x_generated, keep_prob)
# 損失函式的設定
d_loss = - (tf.log(y_data) + tf.log(1 - y_generated))
g_loss = - tf.log(y_generated)
optimizer = tf.train.AdamOptimizer(0.0001)
# 兩個模型的優化函式
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()
# 啟動預設圖
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，我們都會載入256個訓練樣本，然後執行一次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)
# 執行生成
sess.run(d_trainer,
feed_dict={x_data: x_value, z_prior: z_value, keep_prob: np.sum(0.7).astype(np.float32)})
# 執行判別
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()
按照500次迭代，每次迭代產生一張手寫體圖片，然後進行判別反饋，這樣持續下去，可以看到不同迭代次數的效果。
(第1張)
(第2張)
第10張
第24張
第50張
第140張
第256張
第500張
