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siamese_cnn.py
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siamese_cnn.py
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# -*- encoding:utf8 -*-
import tensorflow as tf
import numpy as np
import os
import sys
from copy import deepcopy
stdout = sys.stdout
reload(sys)
sys.stdout = stdout
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Get ENV
ENVIRON = os.environ.copy()
import cPickle as pkl
from utils import *
from models import SiameseCNN
class CNNConfig(object):
def __init__(self, vocab_size, embeddings=None):
# 输入问题(句子)长度
self.max_q_length = 200
# 输入答案长度
self.max_a_length = 200
# 循环数
self.num_epochs = 100
# batch大小
self.batch_size = 128
# 词表大小
self.vocab_size = vocab_size
# 词向量大小
self.embeddings = embeddings
self.embedding_size = 100
if self.embeddings is not None:
self.embedding_size = embeddings.shape[1]
# 不同类型的filter，对应不同的尺寸
self.filter_sizes = [1, 2, 3, 5, 7, 9]
# 隐层大小
self.hidden_size = 128
self.output_size = 128
# 每种filter的数量
self.num_filters = 128
self.l2_reg_lambda = 0.
self.keep_prob = 0.6
# 学习率
self.lr = 0.00003
# contrasive loss 中的 positive loss部分的权重
self.pos_weight = 5
self.cf = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
self.cf.gpu_options.per_process_gpu_memory_fraction = 0.2
def train(train_corpus, config, val_corpus, eval_train_corpus=None):
iterator = Iterator(train_corpus)
if not os.path.exists(model_path):
os.mkdir(model_path)
with tf.Session(config=config.cf) as sess:
model = SiameseCNN(config)
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
for epoch in xrange(config.num_epochs):
count = 0
for batch_x in iterator.next(config.batch_size, shuffle=True):
batch_qids, batch_q, batch_aids, batch_ap, labels = zip(*batch_x)
batch_q = np.asarray(batch_q)
batch_ap = np.asarray(batch_ap)
_, loss = sess.run([model.train_op, model.total_loss],
feed_dict={model.q:batch_q,
model.a:batch_ap,
model.y:labels,
model.keep_prob:config.keep_prob})
count += 1
if count % 10 == 0:
print('[epoch {}, batch {}]Loss:{}'.format(epoch, count, loss))
saver.save(sess,'{}/my_model'.format(model_path), global_step=epoch)
if eval_train_corpus is not None:
train_res = evaluate(sess, model, eval_train_corpus, config)
print('[train] ' + train_res)
if val_corpus is not None:
val_res = evaluate(sess, model, val_corpus, config)
print('[eval] ' + val_res)
def evaluate(sess, model, corpus, config):
iterator = Iterator(corpus)
count = 0
total_qids = []
total_aids = []
total_pred = []
total_labels = []
total_loss = 0.
for batch_x in iterator.next(config.batch_size, shuffle=False):
batch_qids, batch_q, batch_aids, batch_ap, labels = zip(*batch_x)
batch_q = np.asarray(batch_q)
batch_ap = np.asarray(batch_ap)
q_ap_cosine, loss = sess.run([model.q_a_cosine, model.total_loss],
feed_dict={model.q:batch_q,
model.a:batch_ap,
model.y:labels,
model.keep_prob:1.})
total_loss += loss
count += 1
total_qids.append(batch_qids)
total_aids.append(batch_aids)
total_pred.append(q_ap_cosine)
total_labels.append(labels)
# print(batch_qids[0], [id2word[_] for _ in batch_q[0]],
# batch_aids[0], [id2word[_] for _ in batch_ap[0]])
total_qids = np.concatenate(total_qids, axis=0)
total_aids = np.concatenate(total_aids, axis=0)
total_pred = np.concatenate(total_pred, axis=0)
total_labels = np.concatenate(total_labels, axis=0)
MAP, MRR = eval_map_mrr(total_qids, total_aids, total_pred, total_labels)
# print('Eval loss:{}'.format(total_loss / count))
return 'MAP:{}, MRR:{}'.format(MAP, MRR)
def test(corpus, config):
with tf.Session(config=config.cf) as sess:
model = SiameseCNN(config)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(model_path))
print('[test] ' + evaluate(sess, model, corpus, config))
def main(args):
max_q_length = 25
max_a_length = 90
with open(os.path.join(processed_data_path, 'pointwise_corpus.pkl'), 'r') as fr:
train_corpus, val_corpus, test_corpus = pkl.load(fr)
embeddings = build_embedding(embedding_path, word2id)
train_qids, train_q, train_aids, train_ap, train_labels = zip(*train_corpus)
train_q = padding(train_q, max_q_length)
train_ap = padding(train_ap, max_a_length)
train_corpus = zip(train_qids, train_q, train_aids, train_ap, train_labels)
val_qids, val_q, val_aids, val_ap, labels = zip(*val_corpus)
val_q = padding(val_q, max_q_length)
val_ap = padding(val_ap, max_a_length)
val_corpus = zip(val_qids, val_q, val_aids, val_ap, labels)
test_qids, test_q, test_aids, test_ap, labels = zip(*test_corpus)
test_q = padding(test_q, max_q_length)
test_ap = padding(test_ap, max_a_length)
test_corpus = zip(test_qids, test_q, test_aids, test_ap, labels)
config = CNNConfig(max(word2id.values()) + 1, embeddings=embeddings)
config.max_q_length = max_q_length
config.max_a_length = max_a_length
if args.train:
train(deepcopy(train_corpus), config, val_corpus, deepcopy(train_corpus))
elif args.test:
test(test_corpus, config)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--train", help="whether to train", action='store_true')
parser.add_argument("--test", help="whether to test", action='store_true')
args = parser.parse_args()
raw_data_path = '../data/WikiQA/raw'
processed_data_path = '../data/WikiQA/processed'
processed_data_pkl = os.path.join(processed_data_path, 'vocab.pkl')
embedding_path = '../data/embedding/glove.6B.300d.txt'
model_path = 'models'
if 'GLOVE_EMBEDDING_6B' in ENVIRON:
embedding_path = ENVIRON['GLOVE_EMBEDDING_6B']
print("embedding file: %s" % embedding_path)
if not os.path.exists(processed_data_pkl):
raise BaseException("data [%] not exist, run ch4/preprocess_wiki.py first." % processed_data_pkl)
with open(processed_data_pkl, 'r') as fr:
word2id, id2word = pkl.load(fr)
main(args)