1、tf.contrib.crf.crf_log_likelihood crf_log_likelihood(inputs,tag_indices,sequence_lengths,transition_params=None) 在一个条件随机场里面计算标签序列的log-likelihood 参数: inputs: 一个形状为[batch_size, max_seq_len, num_tags] 的tensor,一般使用BILSTM处理之后输出转换为他要求的形状作为CRF层的输入...
"""Computes the log-likelihood of tag sequences in a CRF. Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likelihood. ...
tag_indices,sequence_lengths,transition_params=None):"""Computes the log-likelihood of tag sequences in a CRF....
tensorflow实现crf就三个函数,crf_log_likelihood、viterbi_decode、crf_decode,他们都在tf.contrib.crf这个API里,搞懂这三个函数,不管事BiLSTM+CRF还是BERT+BiLSTM+CRF你都游刃有余了。 tf.contrib.crf.crf_log_likelihood crf_log_likelihood(inputs,tag_indices,sequence_lengths,transition_params=None) 通俗理解,...
然后就可以计算序列的的log-likelihood并获得转移概率: log_likelihood,tran_params=tf.contrib.crf.crf_log_likelihood(unary_scores,y_t,length_se) 上述tf.contrib.crf.crf_log_likelihood函数用于在一个条件随机场中计算标签序列的log-likelihood,其格式为: ...
to use as input to the CRF layer. (即[batch大小,最大句子长度,标记个数])这个向量往往是经过RNN(LSTM)之后的输出向量 tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likelihood. (即[batch大小,最大句子长度])这个向量保存batch中每个对象(可以理解为...
loss = tf.reduce_mean(-log_likelihood) self.viterbi_sequence, viterbi_score = tf.contrib.crf.crf_decode(self.bilstm_out, self.transition_params, self.seq_lens) 4、定义主函数 代码语言:javascript 复制 from config.globalConfig import * from config.msraConfig import Config from dataset.msra...
而crf的接口: 1 2 3 4 5 6 7 8 9 10 11 12 13 defcrf_log_likelihood(inputs,tag_indices,sequence_lengths,transition_params=None):...
log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(unary_scores, tags, sequence_lengths) loss = tf.reduce_mean(-log_likelihood) 1. 2. 3. 1 2 3 其中 tags:维度为[batch_size, max_seq_len]的矩阵,也就是Golden标签,注意这里的标签都是以索引方式表示的。
《tensorflow笔记3:CRF函数:tf.contrib.crf.crf_log_likelihood() - 细雨微光 - 博客园》http://t.cn/RrTF8NA