class BiLSTM_CRF_CNN(nn.Module):
def __init__(self,
embedding_dim,
hidden_dim,
vocab_size,
tagset_size,
char_size,
pretrained_weight_embeddings,
USE_CRF,
BIDIRECTIONAL=False,
USE_BIGRAM=False,
bigram_size=0,
CNN=False,
use_gpu=0):
super(BiLSTM_CRF_CNN, self).__init__()
# include start and end tags
self.gpu = use_gpu
self.bidirectional = BIDIRECTIONAL
self.lstm_cnn = BILSTM_CNN(embedding_dim,
hidden_dim,
vocab_size,
tagset_size,
char_size,
pretrained_weight_embeddings,
USE_CRF=False,
BIDIRECTIONAL=self.bidirectional,
USE_BIGRAM=False,
bigram_size=0,
CNN=False,
use_gpu=0)
self.crf = CRF(tagset_size, self.gpu)
def neg_ll_loss(self, sentence, gold_labels, chars):
feats = self.lstm_cnn.forward(sentence, chars)
return self.crf.neg_ll_loss(sentence, gold_labels, feats)
def forward(self, sentence, feats):
feats = self.lstm.forward(sentence)
score, tag_seq = self.crf.forward(sentence, feats)
return score, tag_seq
class LSTM_CRF(nn.Module):
def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size, tag_to_ix, USE_CRF=False,
BIDIRECTIONAL=False, USE_BIGRAM=False, bigram_size=0):
super(LSTM_CRF, self).__init__()
self.hidden_dim = hidden_dim
self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
# self.word_embeddings.weight.requires_grad = False
print("Entered!!!!")
# if pretrained_weight_embeddings != None:
# self.word_embeddings.weight.data.copy_(torch.from_numpy(pretrained_weight_embeddings))
if BIDIRECTIONAL:
self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2, bidirectional=BIDIRECTIONAL)
else:
self.lstm = nn.LSTM(embedding_dim, hidden_dim)
self.crf = CRF(tagset_size, tag_to_ix, 0)
self.bidirectional = BIDIRECTIONAL
self.append_bigram = USE_BIGRAM
self.hidden = self.init_hidden()
if self.append_bigram:
self.hidden2tag = nn.Linear(hidden_dim + bigram_size, tagset_size)
else:
self.hidden2tag = nn.Linear(hidden_dim, tagset_size)
def init_hidden(self):
if not self.bidirectional:
return (autograd.Variable(torch.randn(1, 1, self.hidden_dim)),
autograd.Variable(torch.randn(1, 1, self.hidden_dim)))
else:
return (autograd.Variable(torch.randn(2, 1, self.hidden_dim // 2)),
autograd.Variable(torch.randn(2, 1, self.hidden_dim // 2)))
def forward_lstm(self, sentence, bigram_one_hot=None):
self.hidden = self.init_hidden()
embeds = self.word_embeddings(sentence) # shape seq_length * emb_size
# print(embeds.view(len(sentence), 1, -1).shape)
lstm_out, self.hidden = self.lstm(
embeds.view(len(sentence), 1, -1), self.hidden)
# print("original shape before MLP "+str(lstm_out.view(len(sentence), -1).shape))
# print("shape of onehot bigram "+str(bigram_one_hot))
if self.append_bigram:
# print("concatednated vector"+str(lstm_out.view(len(sentence), -1)))
tag_space=self.hidden2tag(torch.cat([lstm_out.view(len(sentence), -1),bigram_one_hot],dim=1))
else:
tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1))
# tag_scores = F.log_softmax(tag_space, dim=1)
return tag_space
def neg_ll_loss(self, sentence, gold_labels):
feats = self.forward_lstm(sentence)
return self.crf.neg_ll_loss(sentence, gold_labels, feats)
def forward(self, sentence):
feats = self.forward_lstm(sentence)
score, tag_seq = self.crf.forward(sentence, feats)
return score, tag_seq