PyTorch Exercise: A new loss function for discriminative tagging

学习PyTorch教程，其中biLSTM-CRF中给出了Exercise，此处解决方案为：在 class BiLSTM_CRF 中添加成员函数neg_log_likelihood2()，使用viterbi译码的得分与gold score的差作为loss。
It wasn’t really necessary for us to create a computation graph when doing decoding, since we do not backpropagate from the viterbi path score. Since we have it anyway, try training the tagger where the loss function is the difference between the Viterbi path score and the score of the gold-standard path. It should be clear that this function is non-negative and 0 when the predicted tag sequence is the correct tag sequence. This is essentially structured perceptron.
This modification should be short, since Viterbi and score_sentence are already implemented. This is an example of the shape of the computation graph depending on the training instance. Although I haven’t tried implementing this in a static toolkit, I imagine that it is possible but much less straightforward.Pick up some real data and do a comparison!
代码如下：import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim

torch.manual_seed(1)

def to_scalar(var):
# 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):
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
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)))

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)

# Iterate through the sentence
for feat in feats:
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)
# 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)
# 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 = 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)
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
score = autograd.Variable(torch.Tensor([0]))
tags = torch.cat([torch.LongTensor([self.tag_to_ix[START_TAG]]), 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 = []

# 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]
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)
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
4000
_lstm_features(sentence)
forward_score = self._forward_alg(feats)
gold_score = self._score_sentence(feats, tags)
return forward_score - gold_score

def neg_log_likelihood2(self, sentence, tags):
feats = self._get_lstm_features(sentence)
viterbi_score, _ = self._viterbi_decode(feats)
gold_score = self._score_sentence(feats, tags)
return viterbi_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(
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 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_likelihood2(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))
# We got it!运行结果如下：(Variable containing:
12.6453
[torch.FloatTensor of size 1]
, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2])
(Variable containing:
6.5221
[torch.FloatTensor of size 1]
, [0, 0, 1, 1, 2, 2, 2, 0, 1, 2, 0])在此处没有得到正确答案（原程序运行的答案是正确的）。