def add_embedding(self):
"""Add embedding layer. that maps from vocabulary to vectors.
inputs: a list of tensors each of which have a size of [batch_size, embed_size]
"""
self.global_step = tf.Variable(0, name='global_step', trainable=False)
vocab_sz = max(self.config.vocab_dict.values())
with tf.variable_scope('embedding') as scp:
self.exclude_reg_scope(scp) ## exclude scope 추가
if self.config.pre_trained:
embed = utils.readEmbedding(
self.config.embed_path) ## embedding 파일 조회
embed_matrix, valid_mask = utils.mkEmbedMatrix(
embed,
dict(self.config.vocab_dict)) ## embedding matrix 생성
embedding = tf.Variable(embed_matrix,
'Embedding') ## embedding 생성
partial_update_embedding = entry_stop_gradients(
embedding, tf.expand_dims(valid_mask, 1)
) ## a tensor have the same value of target, but some entry will have no gradient during backprop
embedding = tf.cond(
self.on_epoch < self.config.partial_update_until_epoch,
lambda: partial_update_embedding, lambda: embedding
) ## https://www.tensorflow.org/api_docs/python/tf/cond
else:
embedding = tf.get_variable('Embedding',
[vocab_sz, self.config.embed_size],
trainable=True) ## embedding 생성
return embedding
def add_embedding(self):
"""Add embedding layer. that maps from vocabulary to vectors.
inputs: a list of tensors each of which have a size of [batch_size, embed_size]
"""
self.global_step = tf.Variable(0, name='global_step', trainable=False)
vocab_sz = max(self.config.vocab_dict.values())
with tf.variable_scope('embedding') as scp:
self.exclude_reg_scope(scp)
if self.config.pre_trained:
embed = utils.readEmbedding(self.config.embed_path)
embed_matrix, valid_mask = utils.mkEmbedMatrix(
embed, dict(self.config.vocab_dict))
embedding = tf.Variable(embed_matrix, 'Embedding')
partial_update_embedding = entry_stop_gradients(
embedding, tf.expand_dims(valid_mask, 1))
embedding = tf.cond(
self.on_epoch < self.config.partial_update_until_epoch,
lambda: partial_update_embedding, lambda: embedding)
else:
embedding = tf.get_variable('Embedding',
[vocab_sz, self.config.embed_size],
trainable=True)
return embedding