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ps_SC_MSA_R.py
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ps_SC_MSA_R.py
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import sys
sys.path.append('..')
import tensorflow as tf
import pickle
import random
from numpy import linalg
import numpy as np
import math
import os
import utils
if __name__ == '__main__':
# ################ Prepare Data ###################
# basic config
dim = 200 # dimension of embedding
lr_decay_rate = 0.99 # learning rate decay rate
# Note that there is a mistake about lr_decay_rate, we correct it here.
# To achieve the reported results, actually we do not need to decay the learning rate.
batch_size = 1 # batch size, set to 1 because we use SGD
learning_rate = 0.01 # initial learning rate
# learning_rate = float(sys.argv[1]) # initial learning rate
labda = 0.0001 # regularization term labmda
# labda = float(sys.argv[2]) # regularization term labmda
total_epoch = 50 # total training epoches
# total_epoch = int(sys.argv[3]) # total training epoches
trunc_num = 5 # dimention for decomposed sparse matrix
# trunc_num = int(sys.argv[4])
hownet_filename = 'dataset/hownet.txt'
comp_filename = 'dataset/all.bin'
train_filename = 'dataset/train.bin'
test_filename = 'dataset/test.bin'
dev_filename = 'dataset/dev.bin'
embedding_filename = 'dataset/word_embedding.txt'
sem_embed_filename = 'dataset/sememe_vector.txt'
logdir_name = 'phrase_sim/SCMSApos_'+'trunc'+str(trunc_num)
# load hownet，并把hownet.comp分成test_set和train_set
hownet = utils.Hownet(hownet_file=hownet_filename, comp_file=comp_filename)
hownet.build_hownet()
hownet.token2id()
hownet.load_split_dataset(train_filename=train_filename, test_filename=test_filename, dev_filename=dev_filename)
word_embedding_np, hownet = utils.load_word_embedding(embedding_filename, hownet, scale=False) # load word embedding
sememe_embedding_np = utils.load_sememe_embedding(sem_embed_filename, hownet, scale=True) # load sememe embedding
hownet, wordsim_words = utils.fliter_wordsim_all(hownet) # remove MWEs in testset
train_num = len(hownet.comp_train)
pos_dict, word_remove = utils.load_hownet_pos()
hownet, cls_dict = utils.divide_data_with_pos(pos_dict, hownet)
print("number of dataset in training set:{}".format(len(hownet.comp_train)))
print("number of dataset in test set:{}".format(len(hownet.comp_test)))
print("number of dataset in dev set:{}".format(len(hownet.comp_dev)))
if not os.path.exists(logdir_name):
os.makedirs(logdir_name)
os.makedirs(os.path.join(logdir_name, 'print_files'))
os.makedirs(os.path.join(logdir_name, 'model_file'))
os.makedirs(os.path.join(logdir_name, 'tensorboard_logs'))
os.makedirs(os.path.join(logdir_name, 'example_files'))
# ################ Prepare Data ###################
# ################ Model and Run ###################
input_word_l = tf.placeholder(tf.int32, shape=[1], name='word_left')
input_word_r = tf.placeholder(tf.int32, shape=[1], name='word_right')
input_word_t = tf.placeholder(tf.int32, shape=[1], name='word_truth')
input_pos = tf.placeholder(tf.int32, shape=[1], name='pos_tag')
input_sememes_l = tf.placeholder(tf.int32, shape=[None], name='sememes_left')
input_sememes_r = tf.placeholder(tf.int32, shape=[None], name='sememes_right')
word_embedding = tf.Variable(tf.constant(0.0, shape=[word_embedding_np.shape[0], word_embedding_np.shape[1]]),trainable=False,name='word_embed')
embedding_placeholder = tf.placeholder(tf.float32, [word_embedding_np.shape[0], word_embedding_np.shape[1]])
embedding_init = word_embedding.assign(embedding_placeholder)
W_a = tf.Variable(tf.truncated_normal([dim, dim], stddev=0.5), tf.float32, name='W_a')
b_a = tf.Variable(tf.zeros([1, dim]), tf.float32, name='b_a')
U = tf.Variable(tf.truncated_normal([4, 2 * dim, trunc_num], stddev=0.5), tf.float32, name='U')
V = tf.Variable(tf.truncated_normal([4, dim, trunc_num], stddev=0.5), tf.float32, name='V')
U_i = tf.reshape(tf.nn.embedding_lookup(U, input_pos), [2 * dim, trunc_num])
V_i = tf.reshape(tf.nn.embedding_lookup(V, input_pos), [dim, trunc_num])
W_c_i = tf.matmul(U_i, V_i, transpose_b=True)
W_c_base = tf.Variable(tf.truncated_normal([2 * dim, dim], stddev=1.0), tf.float32, name='W_c_base')
b_c = tf.Variable(tf.zeros([1, dim]), tf.float32, name='b_c')
global_step = tf.Variable(0, trainable=False)
# regularizer
tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(labda)(W_a))
tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(labda)(U))
tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(labda)(V))
tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(labda)(W_c_base))
with tf.name_scope('word_embedding'):
embed_word_r = tf.nn.embedding_lookup(word_embedding, input_word_r)
embed_word_l = tf.nn.embedding_lookup(word_embedding, input_word_l)
embed_truth = tf.nn.embedding_lookup(word_embedding, input_word_t)
with tf.name_scope('sememe_embedding'):
sememe_embedding = tf.Variable(tf.constant(0.0, shape=[hownet.sem_num, dim]),trainable=True,name='Sememe_embeddings')
sememe_placeholder = tf.placeholder(tf.float32, [hownet.sem_num, dim])
sememe_init = sememe_embedding.assign(sememe_placeholder)
embed_sememe_l = tf.nn.embedding_lookup(sememe_embedding, input_sememes_l)
embed_sememe_r = tf.nn.embedding_lookup(sememe_embedding, input_sememes_r)
embed_sememe_r = utils.norm(embed_sememe_r)
embed_sememe_l = utils.norm(embed_sememe_l)
# 在att_no_wordvec基础上，把att_no_wordvec的聚合得到的词向量变成预训练的词向量，同时与word embedding一起使用
with tf.name_scope('attention_left'):
embed_word_align_r = tf.nn.tanh(tf.matmul(embed_word_r, W_a)+b_a, name='embed_align_r') # 1 * dim
att_l = tf.nn.softmax(tf.matmul(embed_sememe_l, tf.transpose(embed_word_align_r)), axis=0)
embed_sememe_l = att_l * embed_sememe_l
embed_aggre_word_l_pure = tf.reduce_sum(embed_sememe_l, axis=0, keepdims=True, name='embed_aggre_word_l_pure')
with tf.name_scope('attention_right'):
embed_word_align_l = tf.nn.tanh(tf.matmul(embed_word_l, W_a)+b_a, name='embed_align_l')
att_r = tf.nn.softmax(tf.matmul(embed_sememe_r, tf.transpose(embed_word_align_l)), axis=0)
embed_sememe_r = att_r * embed_sememe_r
embed_aggre_word_r_pure = tf.reduce_sum(embed_sememe_r, axis=0, keepdims=True, name='embed_aggre_word_r_pure')
with tf.name_scope('phrase_embedding'):
embed_word_whole = embed_word_r + embed_word_l
embed_sememe_whole = embed_aggre_word_r_pure + embed_aggre_word_l_pure
phrase_vec = tf.nn.tanh(tf.matmul(tf.concat([embed_word_whole, embed_sememe_whole], 1), W_c_i+W_c_base)+b_c, name="phrase_vec")
with tf.name_scope('loss'):
loss_pure = tf.reduce_mean((phrase_vec - embed_truth) ** 2)
tf.summary.scalar("loss pure", loss_pure)
tf.add_to_collection("losses", loss_pure)
cross_entropy_mean = tf.add_n(tf.get_collection("losses")) # 只是变量名叫cross_entropy_mean, 实际上是loss_all
tf.summary.scalar('loss_all', cross_entropy_mean)
lr = tf.train.exponential_decay(learning_rate, global_step=global_step, decay_steps=train_num/batch_size,decay_rate=lr_decay_rate, staircase=True)
tf.summary.scalar("learning_rate", lr)
opt = tf.train.GradientDescentOptimizer(learning_rate=lr)
grads_vars = opt.compute_gradients(cross_entropy_mean)
capped_grads_vars = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in grads_vars]
train_one_example = opt.apply_gradients(capped_grads_vars)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(embedding_init, feed_dict={embedding_placeholder: word_embedding_np}) # initialize word embedding
sess.run(sememe_init, feed_dict={sememe_placeholder: sememe_embedding_np}) # initialize sememe embedding
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(logdir_name + '/tensorboard_logs', sess.graph) # writer of tensorboard
print_writer_filename = logdir_name + '/print_files/print.txt' # saver for printing
saver = tf.train.Saver(max_to_keep=1) # saver for saving model
# training
random.shuffle(hownet.comp_train)
for epoch in range(total_epoch):
example_writer_filename = logdir_name + '/example_files/epoch' + str(epoch + 1) + '.txt' # file for writing examples
loss_train = 0
loss_this_epoch = 0
print("\nEpoch:" + str(epoch + 1))
for current_num, train_tup in enumerate(hownet.comp_train):
total_num = epoch * train_num + current_num
batch_dict = utils.generate_one_example(hownet, train_tup)
_, summary_i, step_num, loss_i = sess.run(
[train_one_example, merged, global_step, cross_entropy_mean],
feed_dict={input_word_l: [batch_dict['wl']],
input_word_r: [batch_dict['wr']],
input_word_t: [batch_dict['lb']],
input_pos:[batch_dict['pos']],
input_sememes_l: batch_dict['sl'],
input_sememes_r: batch_dict['sr'], })
loss_this_epoch += loss_i
if current_num % 100 == 0:
sys.stdout.flush()
sys.stdout.write('\rTraining num: ' + str(current_num) + ' of ' + str(train_num) + ' loss:' + str(loss_this_epoch / (0.1 + current_num)))
train_writer.add_summary(summary=summary_i, global_step=step_num)
with open(print_writer_filename, 'a', encoding='utf-8') as fprint:
fprint.write('epoch: '+str(epoch+1)+' loss:'+str(loss_this_epoch/(0.1+len(hownet.comp_train)))+'\n')
saver.save(sess, logdir_name + '/model_file/model_ckpt', global_step=epoch + 1)
# dev
loss_dev = 0
for current_num, dev_tup in enumerate(hownet.comp_dev):
batch_dict = utils.generate_one_example(hownet, dev_tup)
loss_i = sess.run(cross_entropy_mean,
feed_dict={input_word_l: [batch_dict['wl']],
input_word_r: [batch_dict['wr']],
input_word_t: [batch_dict['lb']],
input_pos: [batch_dict['pos']],
input_sememes_l: batch_dict['sl'],
input_sememes_r: batch_dict['sr'], })
loss_dev += loss_i
sys.stdout.flush()
sys.stdout.write('\nDev set loss:' + str(loss_dev / (0.1 + len(hownet.comp_dev))) + '\n')
with open(print_writer_filename, 'a', encoding='utf-8') as fprint:
fprint.write('Dev set loss:' + str(loss_dev / (0.1 + len(hownet.comp_dev))) + '\n')
# test MWE similarity: write embedding to phrase_vec_file
number = 0
phrase_vec_file = os.path.join(logdir_name, 'example_files', 'phrase_vector_epoch.txt')
for current_num, test_tup in enumerate(hownet.comp_test):
if test_tup[4] in wordsim_words:
batch_test = utils.generate_one_example(hownet, test_tup)
phrase_vector = sess.run(phrase_vec, feed_dict={input_word_l: [batch_test['wl']],
input_word_r: [batch_test['wr']],
input_word_t: [batch_test['lb']],
input_pos: [batch_test['pos']],
input_sememes_l: batch_test['sl'],
input_sememes_r: batch_test['sr'], })
with open(phrase_vec_file, 'a', encoding='utf-8') as f_phrase_embed:
f_phrase_embed.write(test_tup[4] + ' ')
phrase_vector = phrase_vector.tolist()[0]
phrase_vector = [str(vec) for vec in phrase_vector]
f_phrase_embed.write(' '.join(phrase_vector))
f_phrase_embed.write('\n')
number += 1
print('Have written {} words to phrase_vector.txt'.format(number))
with open(print_writer_filename, 'a', encoding='utf-8') as fprint:
fprint.write('Have written {} words to phrase_vector.txt'.format(number))
train_writer.close()