BiLSTM_CRF.py

# coding: utf-8

import  tensorflow as tf
from  tensorflow.contrib import  crf
import  random
tf.reset_default_graph()
class BiLSTM_CRF(object):
    def __init__(self,batch_size,tag_nums,hidden_nums,sentence_len,word_embeddings,device='/gpu:1'):
        self.batch_size=batch_size
        self.tag_nums = tag_nums
        self.hidden_nums = hidden_nums
        self.sentence_len = sentence_len
        self.word_embeddings = word_embeddings
        self.device = device
        
        with tf.device(device):
            #网络的变量
            word_embeddings =  tf.Variable(initial_value=word_embeddings,trainable=True) #参与训练
            #输入占位符
            self.input_x = tf.placeholder(dtype=tf.int32,shape=[None,self.sentence_len],name='input_word_id')#输入词的id
            self.input_y = tf.placeholder(dtype=tf.int32,shape=[None,self.sentence_len],name='input_labels')
            self.sequence_lengths=tf.placeholder(dtype=tf.int32,shape=[None],name='sequence_lengths_vector')
            self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

            with tf.name_scope('projection'):
                #投影层,先将输入的词投影成相应的词向量
                word_id = self.input_x
                word_vectors = tf.nn.embedding_lookup(word_embeddings,ids=word_id,name='word_vectors')
                #word_vectors = tf.nn.dropout(word_vectors,0.8)
            with tf.name_scope('bi-lstm'):

                #labels = tf.reshape(input_y,shape=[-1,self.sentence_len],name='labels')
                #labels = tf.reshape(input_y,shape=[-1,self.tag_nums],name='labels')
                labels = tf.reshape(self.input_y,shape=[self.batch_size,self.sentence_len],name='labels')
                fw_lstm_cell =tf.nn.rnn_cell.LSTMCell(self.hidden_nums)
                bw_lstm_cell = tf.nn.rnn_cell.LSTMCell(self.hidden_nums)
                #双向传播
                output,_state = tf.nn.bidirectional_dynamic_rnn(fw_lstm_cell,bw_lstm_cell,inputs=word_vectors,sequence_length=self.sequence_lengths,dtype=tf.float32)
                fw_output = output[0]#[batch_size,self.sentence_len,self.hidden_nums]
                bw_output =output[1]#[batch_size,self.sentence_len,self.hidden_nums]
                V1=tf.get_variable('V1',dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer(),shape=[self.hidden_nums,self.hidden_nums])
                V2=tf.get_variable('V2',dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer(),shape=[self.hidden_nums,self.hidden_nums])
                fw_output = tf.reshape(tf.matmul(tf.reshape(fw_output,[-1,self.hidden_nums],name='Lai') , V1),shape=tf.shape(output[0]))
                bw_output = tf.reshape(tf.matmul( tf.reshape(bw_output,[-1,self.hidden_nums],name='Rai') , V2),shape=tf.shape(output[1]))
                contact = tf.concat([fw_output,bw_output],-1,name='bi_lstm_concat')#[batch_size,self.sentence_len,2*self.hidden_nums]
                contact = tf.nn.dropout(contact,self.dropout_keep_prob)
                s=tf.shape(contact)
                contact_reshape=tf.reshape(contact,shape=[-1,2*self.hidden_nums],name='contact')
                W_lstm=tf.get_variable('W_lstm',dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer(),shape=[2*self.hidden_nums,self.tag_nums],trainable=True)
                b_lstm=tf.get_variable('b_lstm',initializer=tf.zeros(shape=[self.tag_nums]))
                p=tf.nn.relu(tf.matmul(contact_reshape,W_lstm)+b_lstm)
                #logit= tf.reshape(p,shape=[-1,s[1],self.tag_nums],name='omit_matrix')
                #logit= tf.reshape(p,shape=[-1,s[1],self.sentence_len],name='omit_matrix')
                self.logit= tf.reshape(p,shape=[-1,self.sentence_len,self.tag_nums],name='omit_matrix') 

            with tf.name_scope("crf") :
                log_likelihood,transition_matrix=crf.crf_log_likelihood(self.logit,labels,sequence_lengths=self.sequence_lengths)
                self.cost = -tf.reduce_mean(log_likelihood)
                self.crf_labels,_=crf.crf_decode(self.logit,transition_matrix,sequence_length=self.sequence_lengths) #返回的第一个值:decode_tags: A [batch_size, max_seq_len]