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transe_model.py
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# coding:utf-8
import tensorflow as tf
import sys
import numpy as np
import ctypes
class TransE(object):
def __init__(self, entity_size, relation_size, hidden_size, margin=1.0, learning_rate=0.001, l1_flag=True,
model_name='transe_model', ckpt_path='./ckpt/transe/'):
self.entity_size = entity_size
self.relation_size = relation_size
self.hidden_size = hidden_size
self.margin = margin
self.learning_rate = learning_rate
self.l1_flag = l1_flag
self.model_name = model_name
self.ckpt_path = ckpt_path
# build graph
sys.stdout.write('\nBuilding Graph...')
tf.reset_default_graph()
# set inputs
self.pos_h = tf.placeholder(tf.int32, [None])
self.pos_t = tf.placeholder(tf.int32, [None])
self.pos_r = tf.placeholder(tf.int32, [None])
self.neg_h = tf.placeholder(tf.int32, [None])
self.neg_t = tf.placeholder(tf.int32, [None])
self.neg_r = tf.placeholder(tf.int32, [None])
with tf.name_scope("embedding"):
self.ent_embeddings = tf.get_variable(name="ent_embedding", shape=[self.entity_size, self.hidden_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
self.rel_embeddings = tf.get_variable(name="rel_embedding", shape=[self.relation_size, self.hidden_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
pos_h_e = tf.nn.embedding_lookup(self.ent_embeddings, self.pos_h)
pos_t_e = tf.nn.embedding_lookup(self.ent_embeddings, self.pos_t)
pos_r_e = tf.nn.embedding_lookup(self.rel_embeddings, self.pos_r)
neg_h_e = tf.nn.embedding_lookup(self.ent_embeddings, self.neg_h)
neg_t_e = tf.nn.embedding_lookup(self.ent_embeddings, self.neg_t)
neg_r_e = tf.nn.embedding_lookup(self.rel_embeddings, self.neg_r)
if self.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)
self.predict = pos
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)
self.predict = pos
with tf.name_scope("output"):
self.loss = tf.reduce_sum(tf.maximum(pos - neg + margin, 0))
self.global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
grads_and_vars = optimizer.compute_gradients(self.loss)
self.train_op = optimizer.apply_gradients(grads_and_vars, global_step=self.global_step)
sys.stdout.write('Done...\n')
def train(self, dataset, num_steps=500, batch_size=50, sess=None):
saver = tf.train.Saver() # use to save the model
if sess is None:
sess = tf.Session()
sess.run(tf.global_variables_initializer())
ph = np.zeros(batch_size, dtype=np.int32)
pt = np.zeros(batch_size, dtype=np.int32)
pr = np.zeros(batch_size, dtype=np.int32)
nh = np.zeros(batch_size, dtype=np.int32)
nt = np.zeros(batch_size, dtype=np.int32)
nr = np.zeros(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]
dataset.getBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p,
ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int]
nbatches = dataset.getTripleTotal() // batch_size
step = 0
sys.stdout.write('Training started...\n')
try:
for step in range(1, num_steps + 1):
res = 0
for batch in range(nbatches):
dataset.getBatch(ph_addr, pt_addr, pr_addr, nh_addr, nt_addr, nr_addr, batch_size)
feed_dict = {self.pos_h: ph, self.pos_t: pt, self.pos_r: pr, self.neg_h: nh, self.neg_t: nt,
self.neg_r: nr}
_, _, loss = sess.run([self.train_op, self.global_step, self.loss], feed_dict=feed_dict)
res += loss
print(' step %4d, loss: %8.4f' % (step, res / nbatches))
if step % 100 == 0: # save the model periodically
sys.stdout.write('Saving model at step %d... ' % step)
saver.save(sess, self.ckpt_path + self.model_name, global_step=step)
sys.stdout.write('Done...\n')
except KeyboardInterrupt:
sys.stdout.write('Interrupted by user at training step %d, saving model at this step.. ' % step)
saver.save(sess, self.ckpt_path + self.model_name, global_step=step)
sys.stdout.write('Done...\n')
return sess
def restore_last_session(self):
saver = tf.train.Saver()
sess = tf.Session() # create a session
ckpt = tf.train.get_checkpoint_state(self.ckpt_path) # get checkpoint state
if ckpt and ckpt.model_checkpoint_path: # restore session
saver.restore(sess, ckpt.model_checkpoint_path)
return sess
def test(self, testset, sess=None):
if sess is None:
print('restore model from last check point...')
sess = self.restore_last_session()
batch_size = testset.getEntityTotal()
ph = np.zeros(batch_size, dtype=np.int32)
pt = np.zeros(batch_size, dtype=np.int32)
pr = np.zeros(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]
testset.getHeadBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
testset.getTailBatch.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p]
testset.testHead.argtypes = [ctypes.c_void_p]
testset.testTail.argtypes = [ctypes.c_void_p]
num_steps = testset.getTestTotal()
for step in range(num_steps):
# test head
testset.getHeadBatch(ph_addr, pt_addr, pr_addr)
feed_dict = {self.pos_h: ph, self.pos_t: pt, self.pos_r: pr}
_, predict_head = sess.run([self.global_step, self.predict], feed_dict)
testset.testHead(predict_head.__array_interface__['data'][0])
# test tail
testset.getTailBatch(ph_addr, pt_addr, pr_addr)
feed_dict = {self.pos_h: ph, self.pos_t: pt, self.pos_r: pr}
_, predict_tail = sess.run([self.global_step, self.predict], feed_dict)
testset.testTail(predict_tail.__array_interface__['data'][0])
print(step)
if step % 50 == 0:
testset.test()
testset.test()