if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
initializer = tf.global_variables_initializer()
sess = tf.Session()
sess.run(initializer)
saver.restore(sess, best_path) # restore the model
system = CacheTransition(cache_size, oracle.utils.OracleType.CL)
if use_dep:
shiftpop, pushidx, arcbinary, arclabel = NP2P_data_stream.load_actions(in_path)
system.shiftpop_action_set, system.push_action_set = shiftpop, pushidx
system.arcbinary_action_set, system.arclabel_action_set = arcbinary, arclabel
income_arc_choices, outgo_arc_choices, default_arc_choices = NP2P_data_stream.load_arc_choices(in_path)
system.income_arcChoices, system.outgo_arcChoices = income_arc_choices, outgo_arc_choices
system.default_arcChoices = default_arc_choices
category_res = {feat_vocab.getIndex(x):[0.0,0.0,] for x in ('PHASE=PUSHIDX', 'PHASE=SHTPOP', 'PHASE=ARCBINARY', 'PHASE=ARCLABEL',)}
devDataStream.reset()
for i in range(devDataStream.get_num_batch()):
cur_batch = devDataStream.get_batch(i)
print('Instance {}'.format(i))
run_beam_search(sess, system, valid_graph, feat_vocab, action_vocab, cur_batch, cache_size, FLAGS, category_res)
for k,v in category_res.iteritems():
k = feat_vocab.getWord(k)
print('%s : %.4f %d/%d' %(k, v[1]/v[0], v[1], v[0]))
saver.restore(sess, best_path) # restore the model
system = CacheTransition(cache_size, oracle.utils.OracleType.CL)
if use_dep:
shiftpop, pushidx, arcbinary, arclabel = NP2P_data_stream.load_actions(
in_path)
system.shiftpop_action_set, system.push_action_set = shiftpop, pushidx
system.arcbinary_action_set, system.arclabel_action_set = arcbinary, arclabel
income_arc_choices, outgo_arc_choices, default_arc_choices = NP2P_data_stream.load_arc_choices(
in_path)
system.income_arcChoices, system.outgo_arcChoices = income_arc_choices, outgo_arc_choices
system.default_arcChoices = default_arc_choices
category_res = {
feat_vocab.getIndex(x): [
0.0,
0.0,
]
for x in (
'PHASE=PUSHIDX',
'PHASE=SHTPOP',
'PHASE=ARCBINARY',
'PHASE=ARCLABEL',
)
}
devDataStream.reset()
for i in range(devDataStream.get_num_batch()):
cur_batch = devDataStream.get_batch(i)
print('Instance {}'.format(i))
run_beam_search(sess, system, valid_graph, feat_vocab,
