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train_lsgan.py
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train_lsgan.py
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import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'
from model_lsgan import *
lambd = 0.25
learning_rate = 0.0004
img_width = 28
img_height = 28
depth = 1
z_dim = 100
batch_size = 32
max_epoch = 25
weiht_decay = 0.00002
total_samples = 42000
gpu = 1
train_flag = True
train_data_path = '/home/wh/working/train.csv'
log_path = '/storage/wanghua/kaggle/log/gan_mnist_ll/'
restore_checkpoint = '/storage/wanghua/kaggle/filelist/'
output_dir = log_path + 'gengrate_images/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
def mse_loss(pred, data):
loss_val = tf.sqrt(2 * tf.nn.l2_loss(pred - data)) / batch_size
return loss_val
def tr():
global_step = tf.Variable(0, name="global_step", trainable=False)
# input
image_dims = [img_width, img_height, depth]
input = tf.placeholder(tf.float32, [batch_size] + [img_width * img_height * depth], 'real_data')
inputss = tf.reshape(input, [batch_size] + image_dims, 'rgb')
inputs = tf.cast(inputss, tf.float32) * (1. / 255)
z_prior = tf.placeholder(tf.float32, [batch_size, z_dim], name="z_prior")
d_real, d_real_logits, _ = discriminator(inputs, is_training=True, reuse=False)
faka_data = generator(z_prior, is_training=True, reuse=False)
d_fake, d_fake_logits, _ = discriminator(faka_data, is_training=True, reuse=True)
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_params = [var for var in t_vars if 'dis' in var.name]
g_params = [var for var in t_vars if 'gen' in var.name]
d_loss_real = tf.reduce_mean(mse_loss(d_real_logits, tf.ones_like(d_real_logits)))
d_loss_fake = tf.reduce_mean(mse_loss(d_fake_logits, tf.zeros_like(d_fake_logits)))
d_l2_loss = tf.add_n([weiht_decay * tf.nn.l2_loss(var) for var in tf.trainable_variables()])
dis_loss = d_loss_real + d_loss_fake + d_l2_loss
g_l2_loss = tf.add_n([weiht_decay * tf.nn.l2_loss(var) for var in g_params])
gen_loss = tf.reduce_mean(mse_loss(d_fake_logits, tf.ones_like(d_fake_logits))) + g_l2_loss
# weight clipping
clip_D = [c.assign(tf.clip_by_value(c, -0.01, 0.01)) for c in d_params]
# optimizer
optimizer_g = tf.train.AdamOptimizer(learning_rate,beta1=0.5)
optimizer_d = tf.train.AdamOptimizer(learning_rate,beta1=0.5)
# trainer
d_trainer = optimizer_d.minimize(dis_loss, var_list=d_params)
g_trainer = optimizer_g.minimize(gen_loss, var_list=g_params)
d_loss_real_sum = tf.summary.scalar("d_loss_real", d_loss_real)
d_loss_fake_sum = tf.summary.scalar("d_loss_fake", d_loss_fake)
d_loss_sum = tf.summary.scalar("d_loss", dis_loss)
g_loss_sum = tf.summary.scalar("g_loss", gen_loss)
g_sum = tf.summary.merge([d_loss_fake_sum, g_loss_sum])
d_sum = tf.summary.merge([d_loss_real_sum, d_loss_sum])
os.environ["CUDA_VISIBLE_DEVICES"] = '%d' % gpu
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
count_trainable_params()
train_writer = tf.summary.FileWriter(log_path, sess.graph)
saver = tf.train.Saver()
load_model(sess=sess, saver=saver, restore_checkpoint=restore_checkpoint)
with tf.device('/gpu:%d' % gpu):
show_images_path = []
for epoch in range(max_epoch):
data_load = data_iter('train.csv', batch_size)
setps = int(total_samples / batch_size)
for step in range(setps):
x, y = data_load.next_batch()
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_dim)).astype(np.float32)
dl,summary_str_d, _, _ = sess.run([dis_loss, d_sum, d_trainer,clip_D], feed_dict={input:x, z_prior:z_sample_val})
train_writer.add_summary(summary_str_d, epoch*setps+step)
print('[Epoch: %s] Step: %s Dis_loss: %s' % (epoch, step, dl))
if step % 10 == 0:
for j in range(2):
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_dim)).astype(np.float32)
gl,summary_str_g, _ = sess.run([gen_loss,g_sum, g_trainer],
feed_dict={input:x, z_prior:z_sample_val})
train_writer.add_summary(summary_str_g, epoch * setps + step)
print('[Epoch: %s] Step: %s -------------Gen_loss: %s-------------' % (epoch, step, gl))
z_sample_val = np.random.normal(0, 1, size=(batch_size, z_dim)).astype(np.float32)
[im] = sess.run([faka_data], feed_dict={input:x, z_prior:z_sample_val})
tmp = view_samples(epoch,np.squeeze(im),(4,8), output_dir)
show_images_path.append(tmp)
save_model(saver, sess, log_path, epoch, gl, dl)
gen_gif(show_images_path, output_dir)
def te():
p = 100#多少张图
z_prior = tf.placeholder(tf.float32, [p, z_dim], name="z_prior")
faka_data = generator(z_prior, is_training=False, reuse=False)
os.environ["CUDA_VISIBLE_DEVICES"] = '%d' % gpu
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess_config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=sess_config) as sess:
count_trainable_params()
tf.summary.FileWriter(log_path, sess.graph)
variables_to_restore = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,'gen')
saver = tf.train.Saver(variables_to_restore)
load_model(sess=sess, saver=saver, restore_checkpoint=log_path)
with tf.device('/gpu:%d' % gpu):
z_sample_val = np.random.normal(0, 1, size=(p, z_dim)).astype(np.float32)
[im] = sess.run([faka_data], feed_dict={z_prior:z_sample_val})
_ = view_samples(-1, np.squeeze(im),(10,10), output_dir)
if train_flag:
tr()
else:
te()