def load_model (model_prefix, word_vocab, batch_size):
FLAGS = load_namespace(model_prefix + ".config.json")
label_vocab = Vocab(model_prefix + ".label_vocab", fileformat='txt2')
num_classes = label_vocab.size()
best_path = model_prefix + ".best.model"
with tf.Graph().as_default():
initializer = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("Model", reuse=False, initializer=initializer):
valid_graph = HanModelGraph(num_classes=num_classes, word_vocab=word_vocab,
dropout_rate=FLAGS.dropout_rate, learning_rate=FLAGS.learning_rate,
lambda_l2=FLAGS.lambda_l2,
context_lstm_dim=FLAGS.context_lstm_dim,
is_training=False, batch_size=batch_size)
vars_ = {}
print ("ValidGraph Build")
for var in tf.global_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
config = tf.ConfigProto(intra_op_parallelism_threads=0,
inter_op_parallelism_threads=0,
allow_soft_placement=True)
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver.restore(sess, best_path)
return valid_graph, sess, label_vocab, FLAGS
if not options.__dict__.has_key("with_target_lattice"):
options.__dict__["with_target_lattice"] = False
if not options.__dict__.has_key("add_first_word_prob_for_phrase"):
options.__dict__["add_first_word_prob_for_phrase"] = False
if not options.__dict__.has_key("pretrain_with_max_matching"):
options.__dict__["pretrain_with_max_matching"] = False
if not options.__dict__.has_key("reward_type"):
options.__dict__["reward_type"] = "bleu"
return options
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config_path', type=str, help='Configuration file.')
print("CUDA_VISIBLE_DEVICES " + os.environ['CUDA_VISIBLE_DEVICES'])
FLAGS, unparsed = parser.parse_known_args()
if FLAGS.config_path is not None:
print('Loading the configuration from ' + FLAGS.config_path)
FLAGS = namespace_utils.load_namespace(FLAGS.config_path)
FLAGS = enrich_options(FLAGS)
sys.stdout.flush()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
from __future__ import print_function
import argparse
import re
import os
import sys
import json
import time
import numpy as np
import codecs
import MHQA_data_stream
import namespace_utils
if __name__ == '__main__':
# load the configuration file
FLAGS = namespace_utils.load_namespace("/u/nalln478/ws/exp.multihop_qa/sub.MHQA/config.json")
in_path = "/u/nalln478/ws/exp.multihop_qa/sub.MHQA/data/dev.json"
print('Loading test set from {}.'.format(in_path))
testset, _ = MHQA_data_stream.read_data_file(in_path, FLAGS)
print('Number of samples: {}'.format(len(testset)))
right = 0.0
total = 0.0
cands_total = 0.0
for i, (question, passage, entity_start, entity_end, edges, candidates, ref,
ids, candidates_str) in enumerate(testset):
if np.argmax(len(x) for x in candidates) == ref:
right += 1.0
total += 1.0
cands_total += len(candidates)
default="prediction",
help='prediction or probs')
args, unparsed = parser.parse_known_args()
model_prefix = args.model_prefix
in_path = args.in_path
out_path = args.out_path
word_vec_path = args.word_vec_path
mode = args.mode
out_json_path = None
dump_prob_path = None
# load the configuration file
print('Loading configurations.')
FLAGS = namespace_utils.load_namespace(model_prefix + ".config.json")
print(FLAGS)
with_POS = False
if hasattr(FLAGS, 'with_POS'): with_POS = FLAGS.with_POS
with_NER = False
if hasattr(FLAGS, 'with_NER'): with_NER = FLAGS.with_NER
wo_char = False
if hasattr(FLAGS, 'wo_char'): wo_char = FLAGS.wo_char
wo_left_match = False
if hasattr(FLAGS, 'wo_left_match'): wo_left_match = FLAGS.wo_left_match
wo_right_match = False
if hasattr(FLAGS, 'wo_right_match'): wo_right_match = FLAGS.wo_right_match
from SentenceMatchModelGraph import SentenceMatchModelGraph
from SentenceMatchDataStream import SentenceMatchDataStream
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_prefix', type=str, required=True, help='Prefix to the models.')
parser.add_argument('--in_path', type=str, default='../data_quora/dev.tsv', help='the path to the test file.')
parser.add_argument('--out_path', type=str, required=True, help='The path to the output file.')
parser.add_argument('--word_vec_path', type=str, default='../data_quora/wordvec.txt', help='word embedding file for the input file.')
args, unparsed = parser.parse_known_args()
# load the configuration file
print('Loading configurations.')
options = namespace_utils.load_namespace(args.model_prefix + ".config.json")
if args.word_vec_path is None: args.word_vec_path = options.word_vec_path
# load vocabs
print('Loading vocabs.')
word_vocab = Vocab(args.word_vec_path, fileformat='txt3')
label_vocab = Vocab(args.model_prefix + ".label_vocab", fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
print('label_vocab: {}'.format(label_vocab.word_vecs.shape))
num_classes = label_vocab.size()
char_vocab = None
if options.with_char:
char_vocab = Vocab(args.model_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
import tensorflow as tf
import os
from vocab_utils import Vocab as EntVocab
import namespace_utils
from SentenceMatchModelGraph import SentenceMatchModelGraph
if __name__ == "__main__":
config_path = "./configs/snli.sample.config"
config_FLAGS = namespace_utils.load_namespace(config_path)
config_FLAGS.__dict__["in_format"] = 'tsv'
word_vec_path = config_FLAGS.word_vec_path
log_dir = config_FLAGS.model_dir
path_prefix = os.path.join(log_dir,
"SentenceMatch.{}".format(config_FLAGS.suffix))
ent_word_vocab = EntVocab(word_vec_path, fileformat='txt3')
print("word_vocab shape is {}".format(ent_word_vocab.word_vecs.shape))
best_path = path_prefix + ".best.model"
label_path = path_prefix + ".label_vocab"
print("best_path: {}".format(best_path))
if os.path.exists(best_path + ".index"):
print("Loading label vocab")
label_vocab = EntVocab(label_path, fileformat='txt2')
else:
raise Exception("no pretrained model")
num_classes = label_vocab.size()
print("Number of labels: {}".format(num_classes))
'The type of LSTM out, mean pooled of all steps or select the last step.'
)
parser.add_argument('--pos_weight',
type=float,
default=0.0,
help='Pos weight of weighted cross entropy losses')
parser.add_argument('--seed',
type=int,
default=2018,
help='Initial seed for algorithm.')
parser.add_argument('--config_path', type=str, help='Configuration file.')
# print("CUDA_VISIBLE_DEVICES " + os.environ['CUDA_VISIBLE_DEVICES'])
args, unparsed = parser.parse_known_args()
if args.config_path is not None:
print('Loading the configuration from ' + args.config_path)
FLAGS = namespace_utils.load_namespace(args.config_path)
else:
FLAGS = args
sys.stdout.flush()
# enrich arguments to backwards compatibility
FLAGS = enrich_options(FLAGS)
if not FLAGS.with_cv:
main(FLAGS)
else:
main_cv(FLAGS)
# python SentenceMatchTrainer.py --config_path ../configs/snli.test.config
op = 'wik'
if args.is_trec == True or args.is_trec == 'True':
op = 'tre'
log_dir = '../models' + op
path_prefix = log_dir + "/SentenceMatch.normal"
#model_prefix = args.model_prefix
in_path = args.in_path
word_vec_path = args.word_vec_path
out_json_path = None
dump_prob_path = None
# load the configuration file
print('Loading configurations.')
FLAGS = namespace_utils.load_namespace(path_prefix + args.index +
".config.json")
print(FLAGS)
with_POS = False
if hasattr(FLAGS, 'with_POS'): with_POS = FLAGS.with_POS
with_NER = False
if hasattr(FLAGS, 'with_NER'): with_NER = FLAGS.with_NER
wo_char = False
if hasattr(FLAGS, 'wo_char'): wo_char = FLAGS.wo_char
wo_left_match = False
if hasattr(FLAGS, 'wo_left_match'): wo_left_match = FLAGS.wo_left_match
wo_right_match = False
if hasattr(FLAGS, 'wo_right_match'): wo_right_match = FLAGS.wo_right_match
def get_test_result(in_p,root_path):
print('Loading configurations.')
model_prefix =root_path+"/stsapp/src/logs/SentenceMatch.snli"
word_vec_path = root_path+"/stsapp/src/data/snli/wordvec.txt"
in_path = in_p
out_path =root_path+"/stsapp/src/result.txt"
print("access decoder")
options = namespace_utils.load_namespace(model_prefix + ".config.json")
if word_vec_path is None: word_vec_path = options.word_vec_path
# load vocabs
print('Loading vocabs.')
word_vocab = Vocab(word_vec_path, fileformat='txt3')
label_vocab = Vocab(model_prefix + ".label_vocab", fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
print('label_vocab: {}'.format(label_vocab.word_vecs.shape))
num_classes = label_vocab.size()
if options.with_char:
char_vocab = Vocab(model_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
print('Build SentenceMatchDataStream ... ')
testDataStream = SentenceMatchDataStream(in_path, word_vocab=word_vocab, char_vocab=char_vocab,
label_vocab=label_vocab,
isShuffle=False, isLoop=True, isSort=True, options=options)
print('Number of instances in devDataStream: {}'.format(testDataStream.get_num_instance()))
print('Number of batches in devDataStream: {}'.format(testDataStream.get_num_batch()))
sys.stdout.flush()
best_path = model_prefix + ".best.model"
init_scale = 0.01
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-init_scale, init_scale)
global_step = tf.train.get_or_create_global_step()
with tf.variable_scope("Model", reuse=False, initializer=initializer):
valid_graph = SentenceMatchModelGraph(num_classes, word_vocab=word_vocab, char_vocab=char_vocab,
is_training=False, options=options)
initializer = tf.global_variables_initializer()
vars_ = {}
for var in tf.global_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
sess = tf.Session()
sess.run(initializer)
print("Restoring model from " + best_path)
saver.restore(sess, best_path)
print("DONE!")
acc,result = train.evaluation(sess, valid_graph, testDataStream, outpath=out_path,
label_vocab=label_vocab)
print("Accuracy for test set is : ",colored(acc, 'green'),"\n")
# print(result['probs'])
return acc,result
assert self.out_neigh_mask.shape == self.out_neigh_indices.shape
assert self.out_neigh_mask.shape == self.out_neigh_edges.shape
# [batch_size, sent_len_max]
self.src = padding_utils.pad_2d_vals(ori_batch.src, len(ori_batch.src),
self.options.max_src_len)
self.sent_inp = padding_utils.pad_2d_vals(ori_batch.sent_inp,
len(ori_batch.sent_inp),
self.options.max_answer_len)
self.sent_out = padding_utils.pad_2d_vals(ori_batch.sent_out,
len(ori_batch.sent_out),
self.options.max_answer_len)
if __name__ == "__main__":
FLAGS = namespace_utils.load_namespace('../config.json')
print('Collecting vocab')
allEdgelabels = set([line.strip().split()[0] \
for line in open('../data/edgelabel_vocab.en', 'rU')])
edgelabel_vocab = Vocab(voc=allEdgelabels,
dim=FLAGS.edgelabel_dim,
fileformat='build')
word_vocab_enc = Vocab('../data/vectors.en.st', fileformat='txt2')
word_vocab_dec = Vocab('../data/vectors.de.st', fileformat='txt2')
print('Loading trainset')
trainset, _, _, _, _ = read_amr_file('../data/newstest2013.tok.json',
FLAGS, word_vocab_enc, word_vocab_dec,
None, edgelabel_vocab)
print('Build DataStream ... ')
trainDataStream = G2SDataStream(trainset,
word_vocab_enc,
def question_gen_run(argv):
#parser = argparse.ArgumentParser()
#parser.add_argument('--model_prefix', type=str, required=True, help='Prefix to the models.')
#parser.add_argument('--in_path', type=str, required=True, help='The path to the test file.')
#parser.add_argument('--out_path', type=str, required=True, help='The path to the output file.')
#parser.add_argument('--mode', type=str, required=True, help='Can be `greedy` or `beam`')
#args, unparsed = parser.parse_known_args()
#model_prefix = args.model_prefix
#in_path = args.in_path
#out_path = args.out_path
#mode = args.mode
print(sys.argv)
model_prefix = argv[0]
in_path = argv[1]
out_path = argv[2]
mode = argv[3]
print("CUDA_VISIBLE_DEVICES " + os.environ['CUDA_VISIBLE_DEVICES'])
# load the configuration file
print('Loading configurations from ' + model_prefix + ".config.json")
FLAGS = namespace_utils.load_namespace(model_prefix + ".config.json")
FLAGS = NP2P_trainer.enrich_options(FLAGS)
# load vocabs
print('Loading vocabs.')
word_vocab = char_vocab = POS_vocab = NER_vocab = None
if FLAGS.with_word:
word_vocab = Vocab(FLAGS.word_vec_path, fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
if FLAGS.with_char:
char_vocab = Vocab(model_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
if FLAGS.with_POS:
POS_vocab = Vocab(model_prefix + ".POS_vocab", fileformat='txt2')
print('POS_vocab: {}'.format(POS_vocab.word_vecs.shape))
if FLAGS.with_NER:
NER_vocab = Vocab(model_prefix + ".NER_vocab", fileformat='txt2')
print('NER_vocab: {}'.format(NER_vocab.word_vecs.shape))
print('Loading test set.')
if FLAGS.infile_format == 'fof':
testset, _ = NP2P_data_stream.read_generation_datasets_from_fof(
in_path, isLower=FLAGS.isLower)
elif FLAGS.infile_format == 'plain':
testset, _ = NP2P_data_stream.read_all_GenerationDatasets(
in_path, isLower=FLAGS.isLower)
else:
testset, _ = NP2P_data_stream.read_all_GQA_questions(
in_path, isLower=FLAGS.isLower, switch=FLAGS.switch_qa)
print('Number of samples: {}'.format(len(testset)))
print('Build DataStream ... ')
batch_size = -1
if mode.find('beam') >= 0: batch_size = 1
devDataStream = NP2P_data_stream.QADataStream(testset,
word_vocab,
char_vocab,
POS_vocab,
NER_vocab,
options=FLAGS,
isShuffle=False,
isLoop=False,
isSort=True,
batch_size=batch_size)
print('Number of instances in testDataStream: {}'.format(
devDataStream.get_num_instance()))
print('Number of batches in testDataStream: {}'.format(
devDataStream.get_num_batch()))
best_path = model_prefix + ".best.model"
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-0.01, 0.01)
with tf.name_scope("Valid"):
with tf.variable_scope("Model",
reuse=False,
initializer=initializer):
valid_graph = ModelGraph(word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
options=FLAGS,
mode="decode")
## remove word _embedding
vars_ = {}
for var in tf.all_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
initializer = tf.global_variables_initializer()
#gpu_fraction = 0.1
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess = tf.Session()
sess.run(initializer)
saver.restore(sess, best_path) # restore the model
total = 0
correct = 0
if mode.endswith('evaluate'):
ref_outfile = open(out_path + ".ref", 'wt')
pred_outfile = open(out_path + ".pred", 'wt')
else:
outfile = open(out_path, 'wt')
total_num = devDataStream.get_num_batch()
devDataStream.reset()
for i in range(total_num):
cur_batch = devDataStream.get_batch(i)
if mode == 'pointwise':
(sentences, prediction_lengths, generator_input_idx,
generator_output_idx) = search(sess,
valid_graph,
word_vocab,
cur_batch,
FLAGS,
decode_mode=mode)
for j in xrange(cur_batch.batch_size):
cur_total = cur_batch.answer_lengths[j]
cur_correct = 0
for k in xrange(cur_total):
if generator_output_idx[
j, k] == cur_batch.in_answer_words[j, k]:
cur_correct += 1.0
total += cur_total
correct += cur_correct
outfile.write(
cur_batch.instances[j][1].tokText.encode('utf-8') +
"\n")
outfile.write(sentences[j].encode('utf-8') + "\n")
outfile.write("========\n")
outfile.flush()
print('Current dev accuracy is %d/%d=%.2f' %
(correct, total, correct / float(total) * 100))
elif mode in ['greedy', 'multinomial']:
print('Batch {}'.format(i))
(sentences, prediction_lengths, generator_input_idx,
generator_output_idx) = search(sess,
valid_graph,
word_vocab,
cur_batch,
FLAGS,
decode_mode=mode)
for j in xrange(cur_batch.batch_size):
outfile.write(
cur_batch.instances[j][1].ID_num.encode('utf-8') +
"\n")
outfile.write(
cur_batch.instances[j][1].tokText.encode('utf-8') +
"\n")
outfile.write(sentences[j].encode('utf-8') + "\n")
outfile.write("========\n")
outfile.flush()
elif mode == 'greedy_evaluate':
print('Batch {}'.format(i))
(sentences, prediction_lengths, generator_input_idx,
generator_output_idx) = search(sess,
valid_graph,
word_vocab,
cur_batch,
FLAGS,
decode_mode="greedy")
for j in xrange(cur_batch.batch_size):
ref_outfile.write(
cur_batch.instances[j][1].tokText.encode('utf-8') +
"\n")
pred_outfile.write(sentences[j].encode('utf-8') + "\n")
ref_outfile.flush()
pred_outfile.flush()
elif mode == 'beam_evaluate':
print('Instance {}'.format(i))
ref_outfile.write(
cur_batch.instances[0][1].tokText.encode('utf-8') + "\n")
ref_outfile.flush()
hyps = run_beam_search(sess, valid_graph, word_vocab,
cur_batch, FLAGS)
cur_passage = cur_batch.instances[0][0]
cur_id2phrase = None
if FLAGS.with_phrase_projection:
(cur_phrase2id, cur_id2phrase) = cur_batch.phrase_vocabs[0]
cur_sent = hyps[0].idx_seq_to_string(cur_passage,
cur_id2phrase, word_vocab,
FLAGS)
pred_outfile.write(cur_sent.encode('utf-8') + "\n")
pred_outfile.flush()
else: # beam search
print('Instance {}'.format(i))
hyps = run_beam_search(sess, valid_graph, word_vocab,
cur_batch, FLAGS)
outfile.write(
"Input: " +
cur_batch.instances[0][0].tokText.encode('utf-8') + "\n")
outfile.write(
"Truth: " +
cur_batch.instances[0][1].tokText.encode('utf-8') + "\n")
for j in xrange(len(hyps)):
hyp = hyps[j]
cur_passage = cur_batch.instances[0][0]
cur_id2phrase = None
if FLAGS.with_phrase_projection:
(cur_phrase2id,
cur_id2phrase) = cur_batch.phrase_vocabs[0]
cur_sent = hyp.idx_seq_to_string(cur_passage,
cur_id2phrase, word_vocab,
FLAGS)
outfile.write("Hyp-{}: ".format(j) +
cur_sent.encode('utf-8') +
" {}".format(hyp.avg_log_prob()) + "\n")
#outfile.write("========\n")
outfile.flush()
if mode.endswith('evaluate'):
ref_outfile.close()
pred_outfile.close()
else:
outfile.close()
def decode(self,
model_prefix,
in_path,
out_path,
word_vec_path,
mode,
out_json_path=None,
dump_prob_path=None):
# model_prefix = args.model_prefix
# in_path = args.in_path
# out_path = args.out_path
# word_vec_path = args.word_vec_path
# mode = args.mode
# out_json_path = None
# dump_prob_path = None
# load the configuration file
print('Loading configurations.')
FLAGS = namespace_utils.load_namespace(model_prefix + ".config.json")
print(FLAGS)
with_POS = False
if hasattr(FLAGS, 'with_POS'): with_POS = FLAGS.with_POS
with_NER = False
if hasattr(FLAGS, 'with_NER'): with_NER = FLAGS.with_NER
wo_char = False
if hasattr(FLAGS, 'wo_char'): wo_char = FLAGS.wo_char
wo_left_match = False
if hasattr(FLAGS, 'wo_left_match'): wo_left_match = FLAGS.wo_left_match
wo_right_match = False
if hasattr(FLAGS, 'wo_right_match'):
wo_right_match = FLAGS.wo_right_match
wo_full_match = False
if hasattr(FLAGS, 'wo_full_match'): wo_full_match = FLAGS.wo_full_match
wo_maxpool_match = False
if hasattr(FLAGS, 'wo_maxpool_match'):
wo_maxpool_match = FLAGS.wo_maxpool_match
wo_attentive_match = False
if hasattr(FLAGS, 'wo_attentive_match'):
wo_attentive_match = FLAGS.wo_attentive_match
wo_max_attentive_match = False
if hasattr(FLAGS, 'wo_max_attentive_match'):
wo_max_attentive_match = FLAGS.wo_max_attentive_match
# load vocabs
print('Loading vocabs.')
word_vocab = Vocab(word_vec_path, fileformat='txt3')
label_vocab = Vocab(model_prefix + ".label_vocab", fileformat='txt2')
print('word_vocab: {}'.format(word_vocab.word_vecs.shape))
print('label_vocab: {}'.format(label_vocab.word_vecs.shape))
num_classes = label_vocab.size()
POS_vocab = None
NER_vocab = None
char_vocab = None
if with_POS:
POS_vocab = Vocab(model_prefix + ".POS_vocab", fileformat='txt2')
if with_NER:
NER_vocab = Vocab(model_prefix + ".NER_vocab", fileformat='txt2')
char_vocab = Vocab(model_prefix + ".char_vocab", fileformat='txt2')
print('char_vocab: {}'.format(char_vocab.word_vecs.shape))
print('Build SentenceMatchDataStream ... ')
testDataStream = SentenceMatchTrainer.SentenceMatchDataStream(
in_path,
word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
label_vocab=label_vocab,
batch_size=FLAGS.batch_size,
isShuffle=False,
isLoop=True,
isSort=True,
max_char_per_word=FLAGS.max_char_per_word,
max_sent_length=FLAGS.max_sent_length)
print('Number of instances in testDataStream: {}'.format(
testDataStream.get_num_instance()))
print('Number of batches in testDataStream: {}'.format(
testDataStream.get_num_batch()))
if wo_char: char_vocab = None
init_scale = 0.01
best_path = model_prefix + ".best.model"
print('Decoding on the test set:')
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(
-init_scale, init_scale)
with tf.variable_scope("Model",
reuse=False,
initializer=initializer):
valid_graph = SentenceMatchModelGraph(
num_classes,
word_vocab=word_vocab,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab,
dropout_rate=FLAGS.dropout_rate,
learning_rate=FLAGS.learning_rate,
optimize_type=FLAGS.optimize_type,
lambda_l2=FLAGS.lambda_l2,
char_lstm_dim=FLAGS.char_lstm_dim,
context_lstm_dim=FLAGS.context_lstm_dim,
aggregation_lstm_dim=FLAGS.aggregation_lstm_dim,
is_training=False,
MP_dim=FLAGS.MP_dim,
context_layer_num=FLAGS.context_layer_num,
aggregation_layer_num=FLAGS.aggregation_layer_num,
fix_word_vec=FLAGS.fix_word_vec,
with_filter_layer=FLAGS.with_filter_layer,
with_highway=FLAGS.with_highway,
word_level_MP_dim=FLAGS.word_level_MP_dim,
with_match_highway=FLAGS.with_match_highway,
with_aggregation_highway=FLAGS.with_aggregation_highway,
highway_layer_num=FLAGS.highway_layer_num,
with_lex_decomposition=FLAGS.with_lex_decomposition,
lex_decompsition_dim=FLAGS.lex_decompsition_dim,
with_char=(not FLAGS.wo_char),
with_left_match=(not FLAGS.wo_left_match),
with_right_match=(not FLAGS.wo_right_match),
with_full_match=(not FLAGS.wo_full_match),
with_maxpool_match=(not FLAGS.wo_maxpool_match),
with_attentive_match=(not FLAGS.wo_attentive_match),
with_max_attentive_match=(
not FLAGS.wo_max_attentive_match))
# remove word _embedding
vars_ = {}
for var in tf.all_variables():
if "word_embedding" in var.name: continue
if not var.name.startswith("Model"): continue
vars_[var.name.split(":")[0]] = var
saver = tf.train.Saver(vars_)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
step = 0
best_path = best_path.replace('//', '/')
saver.restore(sess, best_path)
accuracy = SentenceMatchTrainer.evaluate(testDataStream,
valid_graph,
sess,
outpath=out_path,
label_vocab=label_vocab,
mode=mode,
char_vocab=char_vocab,
POS_vocab=POS_vocab,
NER_vocab=NER_vocab)
