def siamese(self, seg):
"""
孪生网络 transformer + rnn
:param seg:
:return:
"""
x = tf.concat([self.left_x, self.right_x], axis=0)
seq_lens = tf.concat([self.left_seq_lens, self.right_seq_lens], axis=0)
# layers embedding multi_head_attention rnn
left_embed = embedding(self.left_x,
vocab_size=self.vocab_size,
num_units=hp.num_units,
scale=True,
scope="lembed")
right_embed = embedding(self.right_x,
vocab_size=self.vocab_size,
num_units=hp.num_units,
scale=True,
scope="rembed")
query, key = self.transformer(left_embed, right_embed)
# output = self.rnn_layer(embed, seq_lens, seg)
query = self.attention(query, query)
key = self.attention(key, key)
return query, key
def match_pyramid(self): """ pyramid :return: """ left_embed = embedding(self.left_x, vocab_size=self.vocab_size, num_units=self.embedding_size, scale=True, scope="left_embed") right_embed = embedding(self.right_x, vocab_size=self.vocab_size, num_units=self.embedding_size, scale=True, scope="right_embed") outputs = self.match_text(left_embed, right_embed) outputs = self.cnn_layer(outputs, 1) outputs = self.cnn_layer(outputs, 2) return outputs
def __init__(self, vocab_size, num_tags):
self.vocab_size = vocab_size
self.num_tags = num_tags
self.graph = tf.Graph()
with self.graph.as_default():
self.x = tf.placeholder(dtype=tf.int32, shape=[None, hp.max_len])
self.y = tf.placeholder(dtype=tf.int32, shape=[None, hp.max_len])
self.seq_lens = tf.placeholder(dtype=tf.int32, shape=[None])
self.dropout_keep_prob = tf.placeholder(tf.float32,
name="keep_prob")
self.global_step = tf.train.create_global_step()
outputs = embedding(self.x,
vocab_size=self.vocab_size,
num_units=hp.num_units,
scale=True,
scope="embed")
# cnn rnn 层
outputs = self.rnn_layer(outputs)
outputs = self.cnn_layer(outputs)
self.logits = self.logits_layer(outputs)
# crf 层
self.loss, self.transition = self.crf_layer()
# 优化器
self.train_op = self.optimize()
def siamese(self): """ 孪生网络 transformer + rnn :return: """ x = tf.concat([self.left_x, self.right_x], axis=0) seq_lens = tf.concat([self.left_seq_lens, self.right_seq_lens], axis=0) # layers embedding multi_head_attention rnn embed = embedding(x, vocab_size=self.vocab_size, num_units=self.embedding_size, scale=True, scope="embed") # output = self.transformer(embed, x) inputs = tf.expand_dims(embed, -1) output = self.cnn_layer(inputs, 1) output = tf.expand_dims(output, -1) output = self.cnn_layer(output, 2) output = self.attention(embed, output) key, value = tf.split(output, 2, axis=0) return key, value