http://blog.csdn.net/appleml/article/details/78664824

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 sentence for feat in feats: #feat的维度是５ alphas_t = [] # The forward variables at this timestep for 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feats def _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score def _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 step viterbivars_t = [] # holds the viterbi variables for this step for 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] for bptrs_t in 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 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) # 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 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 # 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 loaded for epoch in range(1): # 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 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