def embed_tokens(self, is_training, config, embedding_initializer):
"""Embedds input tokens.
"""
vocab_size = config.vocab_size
size = config.word_embed_size
max_question_length = self.max_question_length
max_sentence_length = self.max_sentence_length
max_sentence_num = self.max_sentence_num
with tf.variable_scope("embed"):
with tf.device("/cpu:0"):
embedding = tf.get_variable(
"embedding_mat", [vocab_size, size],
initializer=embedding_initializer,
dtype=config.data_type,
trainable=False # Continue to train pretrained word2vec
# trainable=True # Continue to train pretrained word2vec
)
self._embedding = embedding
embed_question= []
for i in xrange(max_question_length):
embed_question.append(
tf.nn.embedding_lookup(embedding, self._input_question[i]))
if is_training and config.w_embed_keep_prob < 1:
embed_question[i] = tf.nn.dropout(embed_question[i],
config.w_embed_keep_prob)
if NUMERIC_CHECK:
embed_question[i] = \
tf.check_numerics(embed_question[i],
"embed_question[{}][{}] numeric error".format(i))
embed_sentences = []
for i in xrange(max_sentence_num):
embed_sentences.append([])
for j in xrange(max_sentence_length):
embed_sentences[i].append(
tf.nn.embedding_lookup(embedding, self._input_sentences[i][j]))
if is_training and config.w_embed_keep_prob < 1:
embed_sentences[i][j] = tf.nn.dropout(embed_sentences[i][j],
config.w_embed_keep_prob)
if NUMERIC_CHECK:
embed_sentences[i][j] = \
tf.check_numerics(embed_sentences[i][j],
"embed_sentences[{}][{}] numeric error".format(i, j))
return embed_question, embed_sentences
# RESULTS
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