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]))
# if i != 542:
# continue
cur_batch = devDataStream.get_batch(i)
print('Instance {}'.format(i))
hyps = run_beam_search(sess, system, valid_graph, feat_vocab,
action_vocab, cur_batch, cache_size, FLAGS)
outfile.write("# tok:: " +
cur_batch.instances[0][0].tokText.encode('utf-8') +
"\n")
outfile.write(
"# sentence-{}".format(cur_batch.instances[0][0].idx) + "\n")
for j in xrange(len(hyps)):
hyp = hyps[j]
cur_result = ' '.join(
[action_vocab.getWord(x) for x in hyp.actions])
outfile.write("# Hyp-{}:: ".format(j) + cur_result + "\n")
output_amr = generateAMR(hyp, cur_batch.instances[0][0])
outfile.write(output_amr.to_amr_string().encode('utf-8') +
"\n")
if j == 0:
curr_repr = "result %d, action seq: %s" % (
j, hyp.actionSeqStr(action_vocab))
print(curr_repr)
print(hyp.trans_state.toString().encode('utf-8'))
print(output_amr.to_amr_string().encode('utf-8') + "\n")
break
outfile.write("\n")
outfile.flush()
outfile.close()
