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transR.py
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transR.py
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#coding:utf-8
import numpy as np
import tensorflow as tf
import os
import time
import datetime
import ctypes
ll = ctypes.cdll.LoadLibrary
lib = ll("./init.so")
class Config(object):
def __init__(self):
self.L1
self.hidden_sizeE = 100
self.hidden_sizeR = 100
self.nbatches = 100
self.entity = 0
self.relation = 0
self.trainTimes = 3000
self.margin = 1.0
class TransEModel(object):
def __init__(self, config):
entity_total = config.entity
relation_total = config.relation
batch_size = config.batch_size
sizeE = config.hidden_sizeE
sizeR = config.hidden_sizeR
margin = config.margin
with tf.name_scope("read_inputs"):
self.pos_h = tf.placeholder(tf.int32, [batch_size])
self.pos_t = tf.placeholder(tf.int32, [batch_size])
self.pos_r = tf.placeholder(tf.int32, [batch_size])
self.neg_h = tf.placeholder(tf.int32, [batch_size])
self.neg_t = tf.placeholder(tf.int32, [batch_size])
self.neg_r = tf.placeholder(tf.int32, [batch_size])
with tf.name_scope("embedding"):
self.ent_embeddings = tf.get_variable(name = "ent_embedding", shape = [entity_total, sizeE], initializer = tf.contrib.layers.xavier_initializer(uniform = False))
self.rel_embeddings = tf.get_variable(name = "rel_embedding", shape = [relation_total, sizeR], initializer = tf.contrib.layers.xavier_initializer(uniform = False))
self.rel_matrix = tf.get_variable(name = "rel_matrix", shape = [relation_total, sizeE * sizeR], initializer = tf.contrib.layers.xavier_initializer(uniform = False))
with tf.name_scope('lookup_embeddings'):
pos_h_e = tf.reshape(tf.nn.embedding_lookup(self.ent_embeddings, self.pos_h), [-1, sizeE, 1])
pos_t_e = tf.reshape(tf.nn.embedding_lookup(self.ent_embeddings, self.pos_t), [-1, sizeE, 1])
pos_r_e = tf.reshape(tf.nn.embedding_lookup(self.rel_embeddings, self.pos_r), [-1, sizeE, 1])
neg_h_e = tf.reshape(tf.nn.embedding_lookup(self.ent_embeddings, self.neg_h), [-1, sizeE, 1])
neg_t_e = tf.reshape(tf.nn.embedding_lookup(self.ent_embeddings, self.neg_t), [-1, sizeE, 1])
neg_r_e = tf.reshape(tf.nn.embedding_lookup(self.rel_embeddings, self.neg_r), [-1, sizeE, 1])
matrix = tf.reshape(tf.nn.embedding_lookup(self.rel_matrix, self.neg_r), [-1, sizeR, sizeE])
pos_h_e = tf.reshape(tf.batch_matmul(matrix, pos_h_e), [-1, sizeR])
pos_t_e = tf.reshape(tf.batch_matmul(matrix, pos_t_e), [-1, sizeR])
pos_r_e = tf.reshape(tf.batch_matmul(matrix, pos_r_e), [-1, sizeR])
neg_h_e = tf.reshape(tf.batch_matmul(matrix, neg_h_e), [-1, sizeR])
neg_t_e = tf.reshape(tf.batch_matmul(matrix, neg_t_e), [-1, sizeR])
neg_r_e = tf.reshape(tf.batch_matmul(matrix, neg_r_e), [-1, sizeR])
if config.L1_flag:
pos = tf.reduce_sum(abs(pos_h_e + pos_r_e - pos_t_e), 1, keep_dims = True)
neg = tf.reduce_sum(abs(neg_h_e + neg_r_e - neg_t_e), 1, keep_dims = True)
else:
pos = tf.reduce_sum((pos_h_e + pos_r_e - pos_t_e) ** 2, 1, keep_dims = True)
neg = tf.reduce_sum((neg_h_e + neg_r_e - neg_t_e) ** 2, 1, keep_dims = True)
with tf.name_scope("output"):
self.loss = tf.reduce_sum(tf.maximum(pos - neg + margin, 0))
def main(_):
lib.init()
config = Config()
config.relation = lib.getRelationTotal()
config.entity = lib.getEntityTotal()
config.batch_size = lib.getTripleTotal() / config.nbatches
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
initializer = tf.contrib.layers.xavier_initializer(uniform = False)
with tf.variable_scope("model", reuse=None, initializer = initializer):
trainModel = TransEModel(config = config)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(0.001)
grads_and_vars = optimizer.compute_gradients(trainModel.loss)
train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
sess.run(tf.initialize_all_variables())
def train_step(pos_h_batch, pos_t_batch, pos_r_batch, neg_h_batch, neg_t_batch, neg_r_batch):
feed_dict = {
trainModel.pos_h: pos_h_batch,
trainModel.pos_t: pos_t_batch,
trainModel.pos_r: pos_r_batch,
trainModel.neg_h: neg_h_batch,
trainModel.neg_t: neg_t_batch,
trainModel.neg_r: neg_r_batch
}
_, step, loss = sess.run(
[train_op, global_step, trainModel.loss], feed_dict)
return loss
ph = np.zeros(config.batch_size, dtype = np.int32)
pt = np.zeros(config.batch_size, dtype = np.int32)
pr = np.zeros(config.batch_size, dtype = np.int32)
nh = np.zeros(config.batch_size, dtype = np.int32)
nt = np.zeros(config.batch_size, dtype = np.int32)
nr = np.zeros(config.batch_size, dtype = np.int32)
ph_addr = ph.__array_interface__['data'][0]
pt_addr = pt.__array_interface__['data'][0]
pr_addr = pr.__array_interface__['data'][0]
nh_addr = nh.__array_interface__['data'][0]
nt_addr = nt.__array_interface__['data'][0]
nr_addr = nr.__array_interface__['data'][0]
for times in range(config.trainTimes):
res = 0.0
for batch in range(config.nbatches):
lib.getBatch(ph_addr, pt_addr, pr_addr, nh_addr, nt_addr, nr_addr, config.batch_size)
res += train_step(ph, pt, pr, nh, nt, nr)
current_step = tf.train.global_step(sess, global_step)
print times
print res
if (times % 100 == 0):
f = open("entity2vec.bern", "w")
enb = trainModel.ent_embeddings.eval()
for i in enb:
for j in i:
f.write("%f\t"%(j))
f.write("\n")
f.close()
f = open("relation2vec.bern", "w")
enb = trainModel.rel_embeddings.eval()
for i in enb:
for j in i:
f.write("%f\t"%(j))
f.write("\n")
f.close()
f = open("matrix.bern", "w")
mat = np.resize(trainModel.rel_matrix.eval(),(config.relation, config.hidden_sizeR, config.hidden_sizeE))
for i in range(config.relation):
for j in range(config.hidden_sizeE):
for k in range(config.hidden_sizeR):
f.write("%f\t"%mat[i][k][j])
f.close()
if __name__ == "__main__":
tf.app.run()