def gachabit_loop(DEBUG_MODE=False):
global last_check
last_check = datetime.utcnow()
while True:
logger.info("Checking mentions")
last_check = check_mentions(api, DEBUG_MODE)
logger.info("Replied to tweets up to %s" % last_check)
config.save_cfg()
logger.info("Napping...zzz...")
sleep(sleep_timer)
def save_model(self, sess):
print('Saving the model ... DO NOT TERMINATE NOW! ... ', end='')
sys.stdout.flush()
if not self.cfg[
'KEEP_PREV']: # delete all previous model files to save disk space
for f in glob.glob(os.path.join(self.cfg['CPT_PATH'], 'chatbot*')):
os.remove(f)
saver = tf.train.Saver()
saver.save(sess,
os.path.join(self.cfg['CPT_PATH'], 'chatbot'),
global_step=self.global_step)
config.save_cfg(self.cfg)
print('Saved!')
def saveConfig(self):
cfg.IMAGE_NAME = self.cur_image_name
save_cfg()
assignments[y, x, 0] = level
assignments[y, x, 1:] = closest[y, x, :]
used[y, x] = 1
# Debug images.
if run_type == "train":
accuracy_map = np.divide(accuracy_map, float(ensemble_size))
accuracy_map_dir = "render_images/accuracy_maps"
if not os.path.exists(accuracy_map_dir):
os.mkdir(accuracy_map_dir)
misc.imsave("%s/accuracy_map_%d.png"\
% (accuracy_map_dir, level), accuracy_map)
palette = generate_palette(len(centers))
cluster_map = np.array(render_clusters(width, height, labels, palette))
cluster_map = np.transpose(np.reshape(cluster_map, (width, height, 3)),
(1, 0, 2))
misc.imsave("render_images/cluster_map_%d.png" % (level), cluster_map)
# Save pixel assignments to file.
if run_type == "train":
config.save_cfg(cfg_dir, average, indices, assignments, num_images,
level)
del train_data
del train_labels
del closest
del average
level = level + 1
def save_model(self, path):
saver = tf.train.Saver()
save_path = saver.save(self.model_session, path)
print("Model saved in file: %s" % save_path)
config.save_cfg(path)