def _build_model_op(self):
with tf.variable_scope('embeddings'):
_word_emb = tf.Variable(self.config.emb, name='_word_emb', dtype=tf.float32,
trainable=self.config.finetune_emb)
self.sl_emb = tf.nn.embedding_lookup(_word_emb, self.sl, name='sent_left_emb')
self.sr_emb = tf.nn.embedding_lookup(_word_emb, self.sr, name='sent_right_emb')
self.desc_emb = tf.nn.embedding_lookup(_word_emb, self.desc, name='desc_emb')
with tf.variable_scope('lexical_encoder'):
lexical_rnn = bidirectional_dynamic_rnn(self.config.num_units, use_peepholes=True, scope='lexical_rnn')
self.de = lexical_rnn(self.desc_emb, self.desc_seq_len, return_last_state=True, keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
with tf.variable_scope('concat_sentence'):
de = tf.expand_dims(self.de, axis=1)
s_emb = tf.concat([self.sl_emb, de, self.sr_emb], axis=1) # (batch_size, seq_len, word_dim)
with tf.variable_scope('context_encoder'):
context_rnn = bidirectional_dynamic_rnn(self.config.num_units, use_peepholes=True, scope='context_rnn')
self.hi = context_rnn(s_emb, self.sent_seq_len, return_last_state=True, keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
with tf.variable_scope('project'):
w = tf.get_variable(name='W', shape=[2 * self.config.num_units, 2 * self.config.num_units],
dtype=tf.float32)
b = tf.get_variable("b", shape=[self.config.batch_size, ], dtype=tf.float32,
initializer=tf.constant_initializer(0.))
tmp = tf.matmul(self.hi, w) # (batch_size, 2 * num_units)
tmp = tf.matmul(tmp, tf.transpose(self.de)) # (batch_size, 2 * num_units)
# tmp = tf.reshape(tmp, [self.config.batch_size, self.config.batch_size])
print("tmp", tmp.get_shape())
self.logits = tf.sigmoid(tf.diag_part(tf.nn.bias_add(tmp, b)))
def _build_model_op(self):
with tf.variable_scope('embeddings'):
_word_emb = tf.Variable(self.config.emb,
name='_word_emb',
dtype=tf.float32,
trainable=self.config.finetune_emb)
self.sl_emb = tf.nn.embedding_lookup(_word_emb,
self.sl,
name='sent_left_emb')
self.sr_emb = tf.nn.embedding_lookup(_word_emb,
self.sr,
name='sent_right_emb')
self.desc_emb = tf.nn.embedding_lookup(_word_emb,
self.desc,
name='desc_emb')
with tf.variable_scope('lexical_encoder'):
s = self.desc_emb.get_shape()
desc_emb = tf.reshape(self.desc_emb,
shape=[s[0] * s[1], s[2], s[-1]])
desc_seq_len = tf.reshape(self.desc_seq_len, shape=[s[0] * s[1]])
lexical_rnn = bidirectional_dynamic_rnn(self.config.desc_units,
use_peepholes=True,
scope='lexical_rnn')
de = lexical_rnn(desc_emb, desc_seq_len, return_last_state=True)
# (batch_size, num_cands, num_units)
self.de = tf.reshape(
de, shape=[s[0], s[1], 2 * self.config.desc_units])
with tf.variable_scope('concat_sentence'):
de = tf.expand_dims(
self.de, axis=2) # (batch_size, num_cands, 1, num_units)
print("de", de.get_shape())
sl_emb = tf.tile(tf.expand_dims(self.sl_emb, axis=1),
[1, self.config.num_cands, 1, 1])
sr_emb = tf.tile(tf.expand_dims(self.sl_emb, axis=1),
[1, self.config.num_cands, 1, 1])
s_emb = tf.concat([sl_emb, de, sr_emb], axis=-2)
print("s_emb", s_emb.get_shape())
with tf.variable_scope('context_encoder'):
s = s_emb.get_shape().as_list()
s_emb = tf.reshape(s_emb, shape=[s[0] * s[1], s[2], s[3]])
sent_seq_len = tf.concat([self.sent_seq_len for _ in range(s[1])],
axis=0)
context_rnn = bidirectional_dynamic_rnn(self.config.num_units,
use_peepholes=True,
scope='context_rnn')
hi = context_rnn(s_emb, sent_seq_len, return_last_state=True)
self.hi = tf.reshape(hi,
shape=[s[0], s[1], 2 * self.config.num_units])
with tf.variable_scope('project'):
batch_size, num_cands, _ = self.de.get_shape()
w = tf.get_variable(name='W',
shape=[
batch_size, 2 * self.config.num_units,
2 * self.config.num_units
],
dtype=tf.float32)
# (2 * num_units, batch_size * cands)
# de = tf.transpose(tf.reshape(self.de, shape=[-1, 2 * self.config.num_units * s[0]]))
# hi = tf.reshape(self.hi, shape=[-1, 2 * self.config.num_units * 3]) # (batch_size * cands, 2 * num_units)
# p = tf.diag_part(tf.matmul(tf.matmul(hi, w), de))
de = tf.transpose(self.de, perm=[0, 2, 1])
print(de.get_shape())
p = tf.matmul(tf.matmul(self.hi, w), de)
print("p", p.get_shape())
output = tf.sigmoid(p) # ignore bias
print("output", output.get_shape())
print("y", self.y.get_shape())
self.logits = tf.reshape(p, shape=[batch_size, num_cands])
def _build_model_op(self):
with tf.variable_scope('embeddings'):
_word_emb = tf.Variable(self.config.emb,
name='_word_emb',
dtype=tf.float32,
trainable=self.config.finetune_emb)
self.sl_emb = tf.nn.embedding_lookup(_word_emb,
self.sl,
name='sent_left_emb')
self.sr_emb = tf.nn.embedding_lookup(_word_emb,
self.sr,
name='sent_right_emb')
self.desc_emb_c1 = tf.nn.embedding_lookup(_word_emb,
self.desc_c1,
name='desc_emb')
self.desc_emb_c2 = tf.nn.embedding_lookup(_word_emb,
self.desc_c2,
name='desc_emb')
self.desc_emb_c3 = tf.nn.embedding_lookup(_word_emb,
self.desc_c3,
name='desc_emb')
with tf.variable_scope('lexical_encoder'):
lexical_rnn = bidirectional_dynamic_rnn(self.config.num_units,
use_peepholes=True,
scope='lexical_rnn')
self.de1 = lexical_rnn(
self.desc_emb_c1,
self.desc_seq_len_c1,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
self.de2 = lexical_rnn(
self.desc_emb_c2,
self.desc_seq_len_c2,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
self.de3 = lexical_rnn(
self.desc_emb_c3,
self.desc_seq_len_c3,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
with tf.variable_scope('concat_sentence'):
de1 = tf.expand_dims(self.de1, axis=1)
de2 = tf.expand_dims(self.de2, axis=1)
de3 = tf.expand_dims(self.de3, axis=1)
s_emb1 = tf.concat([self.sl_emb, de1, self.sr_emb],
axis=1) # (batch_size, seq_len, word_dim)
s_emb2 = tf.concat([self.sl_emb, de2, self.sr_emb],
axis=1) # (batch_size, seq_len, word_dim)
s_emb3 = tf.concat([self.sl_emb, de3, self.sr_emb],
axis=1) # (batch_size, seq_len, word_dim)
with tf.variable_scope('context_encoder'):
context_rnn = bidirectional_dynamic_rnn(self.config.num_units,
use_peepholes=True,
scope='context_rnn')
self.hi_c1 = context_rnn(
s_emb1,
self.sent_seq_len,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
self.hi_c2 = context_rnn(
s_emb2,
self.sent_seq_len,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
self.hi_c3 = context_rnn(
s_emb3,
self.sent_seq_len,
return_last_state=True,
keep_prob=self.keep_prob,
is_train=self.is_train) # (batch_size, 2 * num_units)
"""
with tf.variable_scope('project'):
w = tf.get_variable(name='W', shape=[2 * self.config.num_units, 2 * self.config.num_units],
dtype=tf.float32)
b = tf.get_variable("b", shape=[2 * self.config.num_units], dtype=tf.float32,
initializer=tf.constant_initializer(0.))
tmp = tf.nn.bias_add(tf.matmul(self.hi, w), b) # (batch_size, 2 * num_units)
tmp = tf.multiply(tmp, self.de) # (batch_size, 2 * num_units)
self.logits = tf.sigmoid(tf.reduce_sum(tmp, axis=-1)) # (batch_size, 1)
# self.logits = dense(tmp, hidden_dim=2, use_bias=True, scope='compute_logits')
"""
with tf.variable_scope('project'):
W = tf.get_variable(
name='W',
shape=[2 * self.config.num_units, 2 * self.config.num_units],
dtype=tf.float32)
b = tf.get_variable("b",
shape=[2 * self.config.num_units],
dtype=tf.float32,
initializer=tf.constant_initializer(0.))
tmp_c1 = tf.nn.bias_add(tf.matmul(self.hi_c1, W),
b) # (batch_size, 2 * num_units)
tmp_c2 = tf.nn.bias_add(tf.matmul(self.hi_c2, W), b)
tmp_c3 = tf.nn.bias_add(tf.matmul(self.hi_c3, W), b)
tmp_c1 = tf.reduce_sum(tf.matmul(tmp_c1, tf.transpose(self.de1)),
axis=-1) # (batch_size, 1)
tmp_c2 = tf.reduce_sum(tf.matmul(tmp_c2, tf.transpose(self.de2)),
axis=-1)
tmp_c3 = tf.reduce_sum(tf.matmul(tmp_c3, tf.transpose(self.de3)),
axis=-1)
tmp_c1 = tf.reshape(tmp_c1, shape=[-1, 1]) # (batch_size, 1)
tmp_c2 = tf.reshape(tmp_c2, shape=[-1, 1]) # (batch_size, 1)
tmp_c3 = tf.reshape(tmp_c3, shape=[-1, 1]) # (batch_size, 1)
self.logits = tf.concat(
[tf.sigmoid(tmp_c1),
tf.sigmoid(tmp_c2),
tf.sigmoid(tmp_c3)],
axis=1) # (batch_size, 3)