本文主要是介绍pytorch之BI-LSTM CRF（六），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
1、计算CRF的条件概率
CRF计算条件概率 y 是标签序列， x输入的单词序列
2、分数计算由对数函数确定
3、Bi-LSTM CRF中分数的确定
在Bi-LSTM CRF中,定义了两种状态: emission 和 transition状态；i位置的emission状态来自Bi-LSTM在时间步的隐藏状态 i。转换分数存储在|T|x|T|矩阵 P，T是标签集；Pj,k是标签k过渡到标签j的分数
4、实例
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optimtorch.manual_seed(1)
def argmax(vec):'''返回最大下标'''_, idx = torch.max(vec, 1) #dim=1取行的最大值return idx.item()def prepare_sequence(seq, to_ix):'''获取句子的编号'''idxs = [to_ix[w] for w in seq] return torch.tensor(idxs, dtype=torch.long)def log_sum_exp(vec):'''计算log(sum(exp(vec-max_score)))'''max_score = vec[0, argmax(vec)]max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1]) #expand扩充tensor的第二维为vec.size()return max_score + \torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))class BiLSTM_CRF(nn.Module):def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):super(BiLSTM_CRF, self).__init__()self.embedding_dim = embedding_dimself.hidden_dim = hidden_dimself.vocab_size = vocab_sizeself.tag_to_ix = tag_to_ixself.tagset_size = len(tag_to_ix)self.word_embeds = nn.Embedding(vocab_size, embedding_dim)self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,num_layers=1, bidirectional=True)# 从LSTM到标签的概率self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)# 定义从标签i到标签j的转移矩阵self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))#start标签和stop标签都无转移或被转移，设为负无穷self.transitions.data[tag_to_ix[START_TAG], :] = -10000self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000self.hidden = self.init_hidden()def init_hidden(self):'''初始化隐层'''return (torch.randn(2, 1, self.hidden_dim // 2),torch.randn(2, 1, self.hidden_dim // 2))def _forward_alg(self, feats):#初始化前向传播分数init_alphas = torch.full((1, self.tagset_size), -10000.)init_alphas[0][self.tag_to_ix[START_TAG]] = 0.# Wrap到变量中，以便自动反向传播forward_var = init_alphas# 通过句子迭代for feat in feats:alphas_t = [] # t时刻的前向张量for next_tag in range(self.tagset_size):# 传播emission score；不考虑前一个标签，分数不变emit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size)# 第i个实体的trans_score是从i过渡到next_tag的分数trans_score = self.transitions[next_tag].view(1, -1)# 第i个实体的next_tag_var是进行log-sum-exp之前的边的值（i-> next_tag）next_tag_var = forward_var + trans_score + emit_score# 此标签的前向变量是所有分数的log-sum-exp。alphas_t.append(log_sum_exp(next_tag_var).view(1))forward_var = torch.cat(alphas_t).view(1, -1)#print('_forward_alg forward_var:',feat,forward_var)terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]alpha = log_sum_exp(terminal_var)return alphadef _get_lstm_features(self, sentence):#获取lstm的参数self.hidden = self.init_hidden()embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)lstm_out, self.hidden = self.lstm(embeds, self.hidden)lstm_out = lstm_out.view(len(sentence), self.hidden_dim)lstm_feats = self.hidden2tag(lstm_out)return lstm_featsdef _score_sentence(self, feats, tags):#给出所提供标签序列的分数score = torch.zeros(1)tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])for i, feat in enumerate(feats):score = score + \self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]return scoredef _viterbi_decode(self, feats):backpointers = []# 在日志空间中初始化viterbi变量init_vvars = torch.full((1, self.tagset_size), -10000.)init_vvars[0][self.tag_to_ix[START_TAG]] = 0# forward_var在步骤i中保存步骤i-1的viterbi变量forward_var = init_vvarsfor feat in feats:bptrs_t = [] # 保留此步骤的反向指针viterbivars_t = [] # 持有此步骤的维特比变量for next_tag in range(self.tagset_size):# next_tag_var [i]在上一步中保存标签i的viterbi变量，以及从标签i过渡到next_tag的分数。# 我们不在此包括排放分数，因为最大值不取决于它们（我们在下面添加它们）next_tag_var = forward_var + self.transitions[next_tag]best_tag_id = argmax(next_tag_var)bptrs_t.append(best_tag_id)viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))#现在添加排放分数，并将forward_var分配给我们刚刚计算的维特比变量集forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)backpointers.append(bptrs_t)# Transition to STOP_TAGterminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]best_tag_id = argmax(terminal_var)path_score = terminal_var[0][best_tag_id]# Follow the back pointers to decode the best path.best_path = [best_tag_id]for bptrs_t in reversed(backpointers):best_tag_id = bptrs_t[best_tag_id]best_path.append(best_tag_id)# Pop off the start tag (we dont want to return that to the caller)start = best_path.pop()assert start == self.tag_to_ix[START_TAG] # Sanity checkbest_path.reverse()return path_score, best_pathdef neg_log_likelihood(self, sentence, tags):feats = self._get_lstm_features(sentence)forward_score = self._forward_alg(feats)gold_score = self._score_sentence(feats, tags)return forward_score - gold_scoredef forward(self, sentence): # dont confuse this with _forward_alg above.# Get the emission scores from the BiLSTMlstm_feats = self._get_lstm_features(sentence)print("lstm_feats:",lstm_feats.size())# Find the best path, given the features.score, tag_seq = self._viterbi_decode(lstm_feats)print("score:",score)print("best_path:",tag_seq)return score, tag_seqSTART_TAG = "<START>"
STOP_TAG = "<STOP>"
EMBEDDING_DIM = 5
HIDDEN_DIM = 4# Make up some training data
training_data = [("the wall street journal reported today that apple corporation made money".split(),"B I I I O O O B I O O".split()
), ("georgia tech is a university in georgia".split(),"B I O O O O B".split()
)]word_to_ix = {}
for sentence, tags in training_data:for word in sentence:if word not in word_to_ix:word_to_ix[word] = len(word_to_ix)tag_to_ix = {"B": 0, "I": 1, "O": 2, START_TAG: 3, STOP_TAG: 4}model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)# Check predictions before training
with torch.no_grad():precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)print("before training predict:",model(precheck_sent))# Make sure prepare_sequence from earlier in the LSTM section is loaded
for epoch in range(300): # again, normally you would NOT do 300 epochs, it is toy datafor sentence, tags in training_data:# Step 1. Remember that Pytorch accumulates gradients.# We need to clear them out before each instancemodel.zero_grad()# Step 2. Get our inputs ready for the network, that is,# turn them into Tensors of word indices.sentence_in = prepare_sequence(sentence, word_to_ix)targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)# Step 3. Run our forward pass.loss = model.neg_log_likelihood(sentence_in, targets)# Step 4. Compute the loss, gradients, and update the parameters by# calling optimizer.step()loss.backward()optimizer.step()# Check predictions after training
with torch.no_grad():precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)print("after training predict:",model(precheck_sent))
参考网址：
https://zhuanlan.zhihu.com/p/29989121（原理篇；刘建平老师简单的线性CRF）
https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py (pytorch实战篇)
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