pytorch之BI-LSTM CRF（六）

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 optim

torch.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_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.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], :] = -10000
        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000

        self.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 alpha

    def _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_feats

    def _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 score

    def _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_vvars
        for 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_TAG
        terminal_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 check
        best_path.reverse()
        return path_score, best_path

    def 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_score

    def forward(self, sentence):  # dont confuse this with _forward_alg above.
        # Get the emission scores from the BiLSTM
        lstm_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_seq
    
START_TAG = ""
STOP_TAG = ""
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 data
    for sentence, tags in training_data:
        # Step 1. Remember that Pytorch accumulates gradients.
        # We need to clear them out before each instance
        model.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)