pytorch中lstm的公式表示為：
pytorch中lstm的定義如下：
輸入資料格式：
input(seq_len, batch, input_size)
h0(num_layers * num_directions, batch, hidden_size)
c0(num_layers * num_directions, batch, hidden_size)
輸出資料格式：
output(seq_len, batch, hidden_size * num_directions)
hn(num_layers * num_directions, batch, hidden_size)
cn(num_layers * num_directions, batch, hidden_size)
import torch
import gensim
torch.manual_seed(2)
datas=[('你 叫 什麼 名字 ?','n v n n f'),('今天 天氣 怎麼樣 ?','n n adj f'),]
words=[ data[0].split() for data in datas]
tags=[ data[1].split() for data in datas]
id2word=gensim.corpora.dictionary(words)
word2id=id2word.token2id
id2tag=gensim.corpora.dictionary(tags)
tag2id=id2tag.token2id
def sen2id(inputs):
return [word2id[word] for word in inputs]
def tags2id(inputs):
return [tag2id[word] for word in inputs]
# print(sen2id('你 叫 什麼 名字'.split()))
def formart_input(inputs):
return torch.autograd.variable(torch.longtensor(sen2id(inputs)))
def formart_tag(inputs):
return torch.autograd.variable(torch.longtensor(tags2id(inputs)),)
class lstmtagger(torch.nn.module):
def __init__(self,embedding_dim,hidden_dim,***cb_size,target_size):
super(lstmtagger,self).__init__()
self.embedding_dim=embedding_dim
self.hidden_dim=hidden_dim
self.***cb_size=***cb_size
self.target_size=target_size
self.lstm=torch.nn.lstm(self.embedding_dim,self.hidden_dim)
self.log_softmax=torch.nn.logsoftmax()
self.embedding=torch.nn.embedding(self.***cb_size,self.embedding_dim)
self.hidden=(torch.autograd.variable(torch.zeros(1,1,self.hidden_dim)),torch.autograd.variable(torch.zeros(1,1,self.hidden_dim)))
self.out2tag=torch.nn.linear(self.hidden_dim,self.target_size)
def forward(self,inputs):
input=self.embedding((inputs))
out,self.hidden=self.lstm(input.view(-1,1,self.embedding_dim),self.hidden)
tags=self.log_softmax(self.out2tag(out.view(-1,self.hidden_dim)))
return tags
model=lstmtagger(3,3,len(word2id),len(tag2id))
loss_function=torch.nn.nllloss()
optimizer=torch.optim.sgd(model.parameters(),lr=0.1)
for _ in range(100):
model.zero_grad()
input=formart_input('你 叫 什麼 名字'.split())
tags=formart_tag('n n adj f'.split())
out=model(input)
loss=loss_function(out,tags)
loss.backward(retain_variables=true)
optimizer.step()
print(loss.data[0])
input=formart_input('你 叫 什麼'.split())
out=model(input)
out=torch.max(out,1)[1]
print([id2tag[out.data[i]] for i in range(0,out.size()[0])])
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