def __init__(self, path_root):
"""init."""
super(ParsePublications, self).__init__([])
self.log = Logger.get_logger(auxi.get_fullname(self))
self.path_root = path_root
self.init_path("publications.json")
self.want_we_want = ["acronym", "year", "key", "title", "authors"]
def __init__(self, venue):
"""initialization."""
self.venue_name = venue['venue']
self.venue_url = venue['url']
self.acronym = venue['acronym']
self.xml = None
super(Venues, self).__init__(['publications'])
self.log = Logger.get_logger(auxi.get_fullname(self))
def keep_tracking(self, sess):
"""keep track the status."""
# Keep track of gradient values and sparsity (optional)
grad_summaries = []
for g, v in self.grads_and_vars:
if g is not None:
grad_hist_summary = tf.histogram_summary(
"{}/grad/hist".format(v.name), g)
sparsity_summary = tf.scalar_summary(
"{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
grad_summaries.append(grad_hist_summary)
grad_summaries.append(sparsity_summary)
grad_summaries_merged = tf.merge_summary(grad_summaries)
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.join(para.TRAINING_DIRECTORY, "runs",
auxi.get_fullname(self))
if self.force:
shutil.rmtree(out_dir, ignore_errors=True)
out_dir = os.path.join(out_dir, timestamp)
self.out_dir = out_dir
self.log.info("writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.scalar_summary("loss", self.loss)
# Train Summaries
self.train_summary_op = tf.merge_summary(
[loss_summary, grad_summaries_merged])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
self.train_summary_writer = tf.train.SummaryWriter(
train_summary_dir, sess.graph_def)
# dev summaries
self.dev_summary_op = tf.merge_summary([loss_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
self.dev_summary_writer = tf.train.SummaryWriter(
dev_summary_dir, sess.graph_def)
# Checkpoint directory. Tensorflow assumes this directory
# already exists so we need to create it
checkpoint_dir = os.path.join(out_dir, "checkpoints")
self.checkpoint_prefix = os.path.join(checkpoint_dir, "model")
self.checkpoint_comparison = os.path.join(checkpoint_dir, "comparison")
self.best_model = os.path.join(checkpoint_dir, "best_model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
os.makedirs(self.checkpoint_comparison)
self.saver = tf.train.Saver(tf.all_variables())
def __init__(self):
"""init."""
np.random.seed(para.SEED)
self.log = Logger.get_logger(auxi.get_fullname(self))
self.force = para.FORCE_RM_RECORD
self.build_batch = 1
def __init__(self):
"""init."""
super(RandomSegmentation, self).__init__()
self.log = Logger.get_logger(auxi.get_fullname(self))
np.random.seed(para.SEED)
def __init__(self, path_data):
"""init."""
super(LinearModelSegmentation, self).__init__()
self.log = Logger.get_logger(auxi.get_fullname(self))
self.path_data = path_data
def __init__(self):
"""init."""
super(CrawlerAPI, self).__init__()
self.log = Logger.get_logger(auxi.get_fullname(self))
def __init__(self, path_root):
"""init."""
self.log = Logger.get_logger(auxi.get_fullname(self))
self.path_root = path_root
def __init__(self):
"""init."""
super(GetAuthors, self).__init__()
self.log = Logger.get_logger(auxi.get_fullname(self))
self.api = CrawlerAPI()