先膜拜一波大佬，太牛了
对照Pytorch代码实现BiLSTM+CRF与大佬的解析相信看到这篇文章的你也能解惑。
下面是华丽的分割线
大佬对BiLSTM+CRF的解析非常透彻，解决了我很多迷惑的地方。。。详细的步骤我就不介绍了请参阅参考文献。
pytorch 实例
# -*- coding: utf-8 -*-
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
torch.manual_seed(1)
def argmax(vec):
# return the argmax as a python int
_, idx = torch.max(vec, 1)
# 返回两个列表，一个是最大值列表，另一个是dim = 1,
# 目的是取出当前的最大值在行向量中位置。
return idx.item()
def prepare_sequence(seq, to_ix):
idxs = [to_ix[w] for w in seq]
return torch.tensor(idxs, dtype=torch.long)
# Compute log sum exp in a numerically stable way for the forward algorithm
# 以前向算法的数值稳定方式计算log-sum-exp.
def log_sum_exp(vec):
max_score = vec[0, argmax(vec)]
max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
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 # 5
self.hidden_dim = hidden_dim# 3
self.vocab_size = vocab_size
self.tag_to_ix = tag_to_ix
self.tagset_size = len(tag_to_ix) # 5
self.word_embeds = nn.Embedding(vocab_size, embedding_dim)#(25,5)
self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
num_layers=1, bidirectional=True)#(5, 1, num_layers =1, Bi=True)
# Maps the output of the LSTM into tag space.将LSTM的输出映射到标记空间。
self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)#(3,5)
# Matrix of transition parameters. Entry i,j is the score of
# transitioning *to* i *from* j.
# 转移参数矩阵. 条目i，j是*从* j转换*到* i 的分数。
# 转移矩阵是训练的得来的，它是随机初始化的。
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
# 这两个语句强制执行约束: 我们从不转移到start-tag的，我们永远也不会从stop-tag转移.
# 强制设置START和STOP的值为-10000
self.transitions.data[tag_to_ix[START_TAG], :] = -10000 #也就是第4行的位置全部设置为-10000
self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000 #也就是第5列的位置全部设置为-10000
self.hidden = self.init_hidden()
def init_hidden(self):
return (torch.randn(2, 1, self.hidden_dim // 2),# (2,1,1)正态标准分布
torch.randn(2, 1, self.hidden_dim // 2))# (2,1,1)
def _forward_alg(self, feats):
# Do the forward algorithm to compute the partition function
# 使用前向算法来计算分区函数(部分函数)
#torch.full:Returns a tensor of size :attr:`size` filled with :attr:`fill_value`.
init_alphas = torch.full((1, self.tagset_size), -10000.)
# START_TAG has all of the score. START_TAG拥有所有分数。
# tensor([[-10000., -10000., -10000., 0., -10000.]])
init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
# Wrap in a variable so that we will get automatic backprop
# 包装一个变量，以便我们获得自动反向提升(反向传播)
forward_var = init_alphas
# Iterate through the sentence
for feat in feats:
alphas_t = [] # The forward tensors at this timestep 在这个时间步长的前向张量
for next_tag in range(self.tagset_size):
# 发射得分。
# broadcast the emission score: it is the same regardless of
# the previous tag
# 广播发射得分：不管以前的标记是什么都是相同的,发射得分来自BiLSTM训练的标签。
emit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size)
#print(emit_score)
# the ith entry of trans_score is the score of transitioning to
# next_tag from i
# trans_score的第i个entry的tag是从i转换到next_tag(也就是i+i的tag)的分数。#(1, 自动计算)-->(1, 5)
trans_score = self.transitions[next_tag].view(1, -1)
#下一个标签的变量
# The ith entry of next_tag_var is the value for the
# edge (i -> next_tag) before we do log-sum-exp
# next_tag_var的第i个条目是我们执行log-sum-exp之前的边（i - > next_tag）的值
next_tag_var = forward_var + trans_score + emit_score
# 所有得分。
# The forward variable for this tag is log-sum-exp of all the scores.
# 此标记的前向变量是所有分数的log-sum-exp的值。
print(log_sum_exp(next_tag_var).view(1))
alphas_t.append(log_sum_exp(next_tag_var).view(1))
forward_var = torch.cat(alphas_t).view(1, -1)
print(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):
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):
# Gives the score of a provided tag sequence
# 给出提供的标签序列的分数
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):
"""
feats: LSTM 隐层到标签转变的向量。
该函数使用于解码的，也就是说当训练时，是不需要使用的。
"""
backpointers = []
# Initialize the viterbi variables in log space
# 初始化viterbi向量在log空间
init_vvars = torch.full((1, self.tagset_size), -10000.)
#tensor([[-10000., -10000., -10000., -10000., -10000.]])
init_vvars[0][self.tag_to_ix[START_TAG]] = 0 #设置开始标签为0
# tensor([[-10000., -10000., -10000., 0., -10000.]])
# forward_var at step i holds the viterbi variables for step i-1
# 步骤i中的forward_var变量保持步骤i-1的viterbi变量
forward_var = init_vvars # tensor([[-10000., -10000., -10000., 0., -10000.]])
for feat in feats:
bptrs_t = [] # holds the backpointers for this step# 保存这一步的后指针
viterbivars_t = [] # holds the viterbi variables for this step# 保存这一步的viterbi向量
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 [i]保存上一步标签i的维特比变量，加上从标签i转换到next_tag的分数。
# 我们这里不包括发射分数，因为最大值不依赖于它们（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))
# Now add in the emission scores, and assign forward_var to the set
# of viterbi variables we just computed
#现在添加发射分数，并将forward_var分配给我们刚刚计算的维特比变量
forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
backpointers.append(bptrs_t)
# Transition to STOP_TAG
# 转换到 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.
# 遵循后项指针来解码最佳路径
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)
# 弹出开始标签（我们不想把它归还给调用者）
start = best_path.pop()
assert start == self.tag_to_ix[START_TAG] # Sanity check #完整性检查
best_path.reverse()
return path_score, best_path
def neg_log_likelihood(self, sentence, tags):
print("test---1")
#1、LSTM
feats = self._get_lstm_features(sentence)
#2、前向算法
forward_score = self._forward_alg(feats)
#3、句子得分
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
# 从BiLSTM获得发射得分。
# 一、lstm
print("test---2")
lstm_feats = self._get_lstm_features(sentence)
# Find the best path, given the features.
# 找到最佳路径，给定的特征。
# 二、进行viterbi解码
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 创建一些训练数据集
training_data = [("which genre of album is harder ... ..faster ?".split(), "O O O O O I I I O".split()),
("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) #字典word_to_ix的length
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)#(25,tag_to_ix, 5, 4 )
#随机梯度进行优化
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)
# Check predictions before training 训练之前检查预测的结果
#使用 torch.no_grad()构建不需要追踪的上下文环境
with torch.no_grad():
i = 0
while i < 3:
#training_data[0][0] token列表
precheck_sent = prepare_sequence(training_data[i][0], word_to_ix)# [0,1,2,3,4,5,6,7,8,9,10]
precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[i][1]], dtype=torch.long)
#返回标签所对应的长整型值
print("before_train:", model(precheck_sent))
i+=1
#------------------------------------------------------------------------------
# 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
# 第一步，梯度清零
model.zero_grad()
# Step 2. Get our inputs ready for the network, that is,
# turn them into Tensors of word indices.
# 第二步，转换为词为张量，转换标签为张量
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.
# 第三步，前向传播
loss = model.neg_log_likelihood(sentence_in, targets)
model.train()
# Step 4. Compute the loss, gradients, and update the parameters by
# calling optimizer.step()
# 调用optimizer.step()来计算损失函数，梯度和更新参数
loss.backward()
optimizer.step()
#------------------------------------------------------------------------------
# Check predictions after training。训练之后检查预测的结果
with torch.no_grad():
i = 0
while i < 3:
precheck_sent = prepare_sequence(training_data[i][0], word_to_ix)
#此时会调用forward函数，返回 score,target_sequence 。
print("after_train:", model(precheck_sent))
i+=1
# We got it!
参考文献
大佬的介绍BiLSTM+CRF的博客地址（虽然是英文的，但是简单并不难懂）
pytorch实现BiLSTM_CRF的教程
又一条华丽的分割线。。。
膜拜大佬吧！！！
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