def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
#input
z_h, images_h, labels_h, domain_labels_h = Holder()
#inference
D_logits_real, D_logits_fake, D_logits_fake_for_G, reg_g, reg_d = \
gan.inference(images_h, domain_labels_h, z_h)
sampler = gan.sampler(z_h, domain_labels_h)
#loss
G_loss, D_loss = gan.loss_l2(D_logits_real, D_logits_fake, D_logits_fake_for_G,\
reg_g, reg_d, FLAGS.weight_G, FLAGS.weight_D)
#train_op
G_vars, D_vars, C_vars = GetVars()
G_train_op, D_train_op = gan.train(G_loss, D_loss, G_vars, D_vars, global_step)
data_set = data.Data().load(FLAGS.data_path1, FLAGS.half_batch_size)
data_set2 = data.Data().load(FLAGS.data_path2, FLAGS.half_batch_size)
sess = sess_init()
threads = tf.train.start_queue_runners(sess=sess)
saver = tf.train.Saver()
for step in xrange(FLAGS.max_steps):
_, images_v, domain_labels_v = \
GenValsForHolder_TF(data_set, data_set2, sess)
z_v_half = np.random.uniform(-1, 1,
[FLAGS.half_batch_size, gan.Z_DIM]).astype(np.float32)
z_for_G_v = np.concatenate((z_v_half, z_v_half),0)
_, errD, reg_d_v = sess.run([D_train_op, D_loss, reg_d],
feed_dict={z_h:z_for_G_v, images_h:images_v, domain_labels_h: domain_labels_v})
_, errG, reg_g_v = sess.run([G_train_op, G_loss, reg_g],
feed_dict={z_h:z_for_G_v, domain_labels_h: domain_labels_v})
if step % 100 == 0:
print "step = %d, errD = %f, errG = %f reg_g = %f reg_d = %f" % (step, errD, errG, reg_g_v, reg_d_v)
if step % 1000 == 0:
z_v_half = np.random.uniform(-1, 1,
[FLAGS.half_batch_size, gan.Z_DIM]).astype(np.float32)
z_v = np.concatenate((z_v_half, z_v_half),0)
samples = sess.run(sampler,
feed_dict={z_h:z_v, domain_labels_h: domain_labels_v})
save_images(samples, [2, FLAGS.half_batch_size],
'./samples/train_{:d}.png'.format(step))
save_images(images_v, [2, FLAGS.half_batch_size],
'./samples_real/train_{:d}.png'.format(step))
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
#input
z_h, images_h, labels_h, domain_labels_h = Holder()
reg_D_weight_h = tf.placeholder(tf.float32)
reg_G_weight_h = tf.placeholder(tf.float32)
#inference
D_logits_real, D_logits_fake, D_logits_fake_for_G, reg_g, reg_d = \
gan.inference(images_h, domain_labels_h, z_h)
sampler = gan.sampler(z_h, domain_labels_h)
#loss
G_loss, D_loss = gan.loss(D_logits_real, D_logits_fake, D_logits_fake_for_G, \
reg_g, reg_d, reg_G_weight_h, reg_D_weight_h)
#train_op
G_vars, D_vars, C_vars = GetVars()
G_train_op, D_train_op = gan.train(G_loss, D_loss, G_vars, D_vars, global_step)
data_set = data1.DATA(FLAGS.batch_size)
data_set.load()
data_set2 = data2.DATA(FLAGS.batch_size)
data_set2.load()
sess = sess_init()
saver = tf.train.Saver()
for step in xrange(FLAGS.max_steps):
z_v, z_for_G_v, images_v, domain_labels_v = \
GenValsForHolder(data_set, data_set2)
_, errD, reg_d_v = sess.run([D_train_op, D_loss, reg_d],
feed_dict={z_h:z_for_G_v, images_h:images_v, domain_labels_h:domain_labels_v, reg_D_weight_h:FLAGS.weight_D})
_, errG, reg_g_v = sess.run([G_train_op, G_loss, reg_g],
feed_dict={z_h:z_for_G_v, domain_labels_h:domain_labels_v, reg_G_weight_h:FLAGS.weight_G})
if step % 100 == 0:
print "step = %d, errD = %f, errG = %f reg_g = %f reg_d = %f" % (step, errD, errG, reg_g_v, reg_d_v)
if step % 1000 == 0:
z_v_half = np.random.uniform(-1, 1,
[FLAGS.half_batch_size, gan.Z_DIM]).astype(np.float32)
z_v = np.concatenate((z_v_half, z_v_half),0)
domain_labels_v = np.zeros((FLAGS.batch_size, 2))
domain_labels_v[0:FLAGS.half_batch_size, 0] = 1
domain_labels_v[FLAGS.half_batch_size:, 1] = 1
samples = sess.run(sampler,
feed_dict={z_h:z_v, domain_labels_h:domain_labels_v})
save_images(samples, [2, FLAGS.half_batch_size],
'./samples/train_{:d}.bmp'.format(step))
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
#input
z_h, images_h, labels_h1, domain_labels_h = Holder()
test_images_h1, test_images_h2, test_labels_h1, test_labels_h2, \
test_domain_labels_h, test_domain_labels_h2 = holder_test()
reg_D_weight_h = tf.placeholder(tf.float32)
reg_G_weight_h = tf.placeholder(tf.float32)
#inference
D_logits_real, D_logits_fake, D_logits_fake_for_G, reg_g, reg_d = \
gan.inference(images_h, domain_labels_h, z_h)
C_logits = gan.classifier(images_h, domain_labels_h, FLAGS.batch_size)
sampler = gan.sampler(z_h, domain_labels_h)
#loss
G_loss, D_loss = gan.loss(D_logits_real, D_logits_fake, D_logits_fake_for_G, \
reg_g, reg_d, reg_G_weight_h, reg_D_weight_h, C_logits, labels_h1)
#train_op
G_vars, D_vars, C_vars = GetVars()
G_train_op, D_train_op = gan.train(G_loss, D_loss, G_vars, D_vars,
global_step)
#for_eval
test_logits = gan.classifier(test_images_h1, test_domain_labels_h,
FLAGS.test_batch_size, True)
test_op = gan.correct_num(test_logits, test_labels_h1)
test_logits2 = gan.classifier(test_images_h2, test_domain_labels_h2,
FLAGS.test_batch_size2, True)
test_op2 = gan.correct_num(test_logits2, test_labels_h2)
data_set, data_set2 = load_data()
sess = sess_init()
saver = tf.train.Saver()
for step in xrange(FLAGS.max_steps):
_, z_for_G_v, images_v, labels_v1, domain_labels_v = \
GenValsForHolder(data_set, data_set2)
errC, errD, errG, reg_g_v, reg_d_v = (0, 0, 0, 0, 0)
if step > FLAGS.start_regD:
_, errD, reg_d_v = sess.run([D_train_op, D_loss, reg_d],
feed_dict={z_h:z_for_G_v, images_h:images_v, labels_h1:labels_v1,\
domain_labels_h:domain_labels_v, reg_D_weight_h:FLAGS.weight_D})
else:
_, errD, reg_d_v = sess.run([D_train_op, D_loss, reg_d],
feed_dict={z_h:z_for_G_v, images_h:images_v, labels_h1:labels_v1,\
domain_labels_h:domain_labels_v, reg_D_weight_h:0.0})
_, errG, reg_g_v = sess.run([G_train_op, G_loss, reg_g],
feed_dict={z_h:z_for_G_v, domain_labels_h:domain_labels_v, \
reg_G_weight_h:FLAGS.weight_G})
if step % 100 == 0:
print "step = %d, errD = %f, errG = %f errC = %f reg_g = %f reg_d = %f" \
% (step, errD, errG, errC, reg_g_v, reg_d_v)
if step % 1000 == 0:
test_domain_labels_v = np.zeros((FLAGS.test_batch_size, 2))
test_domain_labels_v[:, 0] = 1
test_domain_labels_v2 = np.zeros((FLAGS.test_batch_size2, 2))
test_domain_labels_v2[:, 1] = 1
para_list = (test_op, test_images_h1, test_labels_h1, test_domain_labels_h, \
test_domain_labels_v, data_set, FLAGS.test_batch_size, FLAGS.test_sample_size)
Eval(para_list, sess, step, 'Source')
para_list = (test_op2, test_images_h2, test_labels_h2, test_domain_labels_h2, \
test_domain_labels_v2, data_set2, FLAGS.test_batch_size2, FLAGS.test_sample_size2)
Eval(para_list, sess, step, 'Target')
z_v_half = np.random.uniform(
-1, 1,
[FLAGS.half_batch_size, gan.Z_DIM]).astype(np.float32)
z_v = np.concatenate((z_v_half, z_v_half), 0)
domain_labels_v = np.zeros((FLAGS.batch_size, 2))
domain_labels_v[0:FLAGS.half_batch_size, 0] = 1
domain_labels_v[FLAGS.half_batch_size:, 1] = 1
samples = sess.run(sampler,
feed_dict={
z_h: z_v,
domain_labels_h: domain_labels_v
})
save_images(samples, [2, FLAGS.half_batch_size],
'./samples/train_{:d}.bmp'.format(step))