转：pytorch版的bilstm+crf实现sequence label

在理解CRF的时候费了一些功夫，将一些难以理解的地方稍微做了下标注，隔三差五看看加强记忆, 代码是pytorch文档上的example

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim

def to_scalar(var): #var是Variable,维度是１
    # returns a python float
    return var.view(-1).data.tolist()[0]

def argmax(vec):
    # return the argmax as a python int
    _, idx = torch.max(vec, 1)
    return to_scalar(idx)

def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    tensor = torch.LongTensor(idxs)
    return autograd.Variable(tensor)

# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec): #vec是1*5, type是Variable

    max_score = vec[0, argmax(vec)]
    #max_score维度是１，　max_score.view(1,-1)维度是１＊１，max_score.view(1, -1).expand(1, vec.size()[1])的维度是１＊５
    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1]) # vec.size()维度是1*5
    return max_score + torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))#为什么指数之后再求和，而后才log呢

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)

        # Maps the output of the LSTM into tag space.
        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)

        # Matrix of transition parameters.  Entry i,j is the score of
        # transitioning *to* i *from* j.
        self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))

        # These two statements enforce the constraint that we never transfer
        # to the start tag and we never transfer from the stop tag
        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):
        return (autograd.Variable(torch.randn(2, 1, self.hidden_dim // 2)),
                autograd.Variable(torch.randn(2, 1, self.hidden_dim // 2)))
    #预测序列的得分
    def _forward_alg(self, feats):
        # Do the forward algorithm to compute the partition function
        init_alphas = torch.Tensor(1,self.tagset_size).fill_(-10000.)# START_TAG has all of the score.
        init_alphas[0][self.tag_to_ix[START_TAG]]=0.# Wrap in a variable so that we will get automatic backprop
        forward_var = autograd.Variable(init_alphas)#初始状态的forward_var，随着step t变化# Iterate through the sentencefor feat in feats:#feat的维度是５alphas_t=[]# The forward variables at this timestepfor next_tag in range(self.tagset_size):# broadcast the emission score: it is the same regardless of# the previous tag
                emit_score = feat[next_tag].view(1,-1).expand(1,self.tagset_size)#维度是1*5# the ith entry of trans_score is the score of transitioning to# next_tag from i
                trans_score =self.transitions[next_tag].view(1,-1)#维度是１＊５# The ith entry of next_tag_var is the value for the# edge (i -> next_tag) before we do log-sum-exp#第一次迭代时理解：# trans_score所有其他标签到Ｂ标签的概率# 由lstm运行进入隐层再到输出层得到标签Ｂ的概率，emit_score维度是１＊５，5个值是相同的
                next_tag_var = forward_var + trans_score + emit_score
                # The forward variable for this tag is log-sum-exp of all the# scores.alphas_t.append(log_sum_exp(next_tag_var))

            forward_var = torch.cat(alphas_t).view(1,-1)#到第（t-1）step时５个标签的各自分数
        terminal_var = forward_var +self.transitions[self.tag_to_ix[STOP_TAG]]
        alpha = log_sum_exp(terminal_var)return alpha

    #得到featsdef_get_lstm_features(self, sentence):self.hidden =self.init_hidden()#embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
        embeds =self.word_embeds(sentence)

        embeds = embeds.unsqueeze(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

    #得到gold_seq tag的scoredef_score_sentence(self, feats, tags):# Gives the score of a provided tag sequence
        score = autograd.Variable(torch.Tensor([0]))
        tags = torch.cat([torch.LongTensor([self.tag_to_ix[START_TAG]]), tags])#将START_TAG的标签３拼接到tag序列上for i, feat in enumerate(feats):#self.transitions[tags[i + 1], tags[i]] 实际得到的是从标签i到标签i+1的转移概率#feat[tags[i+1]], feat是step i 的输出结果，有５个值，对应B, I, E, START_TAG, END_TAG, 取对应标签的值

            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
    #解码，得到预测的序列，以及预测序列的得分def_viterbi_decode(self, feats):
        backpointers =[]# Initialize the viterbi variables in log space
        init_vvars = torch.Tensor(1,self.tagset_size).fill_(-10000.)
        init_vvars[0][self.tag_to_ix[START_TAG]]=0# forward_var at step i holds the viterbi variables for step i-1
        forward_var = autograd.Variable(init_vvars)for feat in feats:bptrs_t=[]# holds the backpointers for this stepviterbivars_t=[]# holds the viterbi variables for this stepfor next_tag in range(self.tagset_size):# next_tag_var[i] holds the viterbi variable for tag i at the# previous step, plus the score of transitioning# from tag i to next_tag.# We don‘t include the emission scores here because the max# does not depend on them (we add them in below)
                next_tag_var = forward_var +self.transitions[next_tag]#其他标签（B,I,E,Start,End）到标签next_tag的概率
                best_tag_id = argmax(next_tag_var)bptrs_t.append(best_tag_id)viterbivars_t.append(next_tag_var[0][best_tag_id])# Now add in the emission scores, and assign forward_var to the set# of viterbi variables we just computed
            forward_var =(torch.cat(viterbivars_t)+ feat).view(1,-1)#从step0到step(i-1)时5个序列中每个序列的最大score
            backpointers.append(bptrs_t)#bptrs_t有５个元素# Transition to STOP_TAG
        terminal_var = forward_var +self.transitions[self.tag_to_ix[STOP_TAG]]#其他标签到STOP_TAG的转移概率
        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]forbptrs_tin reversed(backpointers):#从后向前走，找到一个best路径
            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()# 把从后向前的路径正过来return path_score, best_path

    defneg_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

    defforward(self, sentence):# dont confuse this with _forward_alg above.# Get the emission scores from the BiLSTM
        lstm_feats =self._get_lstm_features(sentence)# Find the best path, given the features.
        score, tag_seq =self._viterbi_decode(lstm_feats)return score, tag_seq

START_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 notin 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# precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)# precheck_tags = torch.LongTensor([tag_to_ix[t] for t in training_data[0][1]])# print(model(precheck_sent))# Make sure prepare_sequence from earlier in the LSTM section is loadedfor epoch in range(1):# 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 instance
        model.zero_grad()# Step 2. Get our inputs ready for the network, that is,# turn them into Variables of word indices.
        sentence_in = prepare_sequence(sentence, word_to_ix)
        targets = torch.LongTensor([tag_to_ix[t]for t in tags])# Step 3. Run our forward pass.
        neg_log_likelihood = model.neg_log_likelihood(sentence_in, targets)# Step 4. Compute the loss, gradients, and update the parameters by# calling optimizer.step()
        neg_log_likelihood.backward()
        optimizer.step()# Check predictions after training
precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)print(model(precheck_sent)[0])#得分print(‘^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^‘)print(model(precheck_sent)[1])#tag sequence