def __init__(self, options, corpus, session):
Model.__init__(self, options, corpus, session)
# Key node
self._pop_per_item = list()
self.train()
self.build_eval_graph()
def __init__(self, options, corpus, session):
Model.__init__(self, options, corpus, session)
self.last_save_epoch = -1
# Model parameter
self._all_user_emb = None
self._user_emb = None
self._item_emb = None
self._item_bias = None
# Best parameter
self._all_user_emb_best = None
self._user_emb_best = None
self._item_emb_best = None
self._item_bias_best = None
# Nodes in the graph which are used to run/feed/fetch.
self._cur_user = None
self._prev_item = None
self._pos_item = None
self._neg_item = None
# Key Nodes
# Train
self._loss, self._loss1, self._loss2 = None, None, None
self._train = None
self.global_step = None
self._debug = None
# Op
self._norm_all_user_emb_op = None
self._norm_item_emb_op = None
self._save_best_param_op = None
self._restore_best_param_op = None
# Summary
self._loss_summary = None
self._train_writer = None
self.build_graph()
self.build_eval_graph()
# Properly initialize all variables.
tf.global_variables_initializer().run()
self.saver = tf.train.Saver()
if len(self._options.checkpoint_name) > 0:
ckpt_path = self._options.checkpoints_path + self._options.checkpoint_name
self.load_model(ckpt_path)
if self._options.write_summary:
self._train_writer = tf.summary.FileWriter(
"{}{}_{}/train".format(self._options.save_path,
self._options.timestamp,
self.to_string()), self._session.graph)
self._test_writer = tf.summary.FileWriter("{}{}_{}/test".format(
self._options.save_path, self._options.timestamp,
self.to_string()))