ckpt = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if ckpt and ckpt.model_checkpoint_path:
print "Restoring previous model..."
try:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model restored"
except:
print "Could not restore model"
pass
########################################### training portion
step = sess.run(global_step)
print 'Loading data...'
train_paths, train_annots, train_ids, test_paths, test_annots, test_ids = data_ops.load_zoo(
DATA_DIR, hot=HOT)
print 'Done'
train_len = len(train_paths)
test_len = len(test_paths)
print 'train num:', train_len
epoch_num = step / (train_len / BATCH_SIZE)
while epoch_num < EPOCHS + 1:
epoch_num = step / (train_len / BATCH_SIZE)
start = time.time()
# train the discriminator n times
sess.run(init)
# restore previous model if there is one
ckpt = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if ckpt and ckpt.model_checkpoint_path:
print "Restoring previous model..."
try:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model restored"
except:
print "Could not restore model"
raise
exit()
print 'Loading data...'
train_images, train_annots, train_ids, test_images, test_annots, test_ids = data_ops.load_zoo(
DATA_DIR, HOT)
test_len = len(test_ids)
print 'Done\n'
'''
Approach:
Going to pick two z vectors and interpolate between them, then set the second vector as the 'start'
and pick a random second vector for the end, and repeat. For each I'll use the same random attribute
'''
idx = np.random.choice(np.arange(test_len), 2, replace=False)
batch_y = test_annots[idx]
# the two z vectors to interpolate between
two_z = np.random.normal(-1.0, 1.0, size=[2, 100]).astype(np.float32)
sess.run(init)
# restore previous model if there is one
ckpt = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if ckpt and ckpt.model_checkpoint_path:
print "Restoring previous model..."
try:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model restored"
except:
print "Could not restore model"
raise
exit()
print 'Loading data...'
train_images, train_annots, train_ids, test_images, test_annots, test_ids = data_ops.load_zoo(
DATA_DIR, SIZE, crop=CROP)
test_len = len(test_annots)
num_gen = 0
gen_z = [] # z vectors used to generate images
gen_y = [] # y vectors used to generate images
print 'generating data...'
while num_gen < MAX_GEN:
batch_z = np.random.normal(-1.0, 1.0, size=[BATCH_SIZE,
100]).astype(np.float32)
idx = np.random.choice(np.arange(test_len), BATCH_SIZE, replace=False)
batch_y = test_annots[idx]
gen_imgs = sess.run([gen_images],
feed_dict={