因為之前在專案中一直使用Tensorflow，最近需要處理NLP問題，對Pytorch框架還比較陌生，所以特地再學習一下pytorch在自然語言處理問題中的簡單使用，這裡做一個記錄。

一、Pytorch基礎

首先，第一步是匯入pytorch的一系列包

import torch
import torch.autograd as autograd #Autograd為Tensor所有操作提供自動求導方法
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

1）Tensor張量

a) 建立Tensors

#tensor
x = torch.Tensor([[1,2,3],[4,5,6]])
#size為2x3x4的隨機數隨機數
x = torch.randn((2,3,4))

b) Tensors計算

x = torch.Tensor([[1,2],[3,4]])
y = torch.Tensor([[5,6],[7,8]])
z = x+y

c) Reshape Tensors

x = torch.randn(2,4)
#拉直
x = x.view(-1)
#4*6維度
x = x.view(4,6)

2）計算圖和自動微分

a) Variable變數

#將Tensor變為Variable
x = autograd.Variable(torch.Tensor([1,3]),requires_grad = True)
#將Variable變為Tensor
y = x.data

b) 反向梯度演算法

x = autograd.Variable(torch.Tensor([1,2]),requires_grad=True)
y = autograd.Variable(torch.Tensor([3,4]),requires_grad=True)
z = x+y
#求和
s = z.sum()
#反向梯度傳播
s.backward()
print(x.grad)

c) 線性對映

linear = nn.Linear(3,5) #三維線性對映到五維
x = autograd.Variable(torch.randn(4,3))
#輸出為（4,5）維
y = linear(x)

d) 非線性對映（啟用函式的使用）

x = autograd.Variable(torch.randn(5))
#relu啟用函式
x_relu = F.relu(x)
print(x_relu)
x_soft = F.softmax(x)
#softmax啟用函式
print(x_soft)
print(x_soft.sum())

output:

Variable containing:
-0.9347
-0.9882
 1.3801
-0.1173
 0.9317
[torch.FloatTensor of size 5]
 
Variable containing:
 0.0481
 0.0456
 0.4867
 0.1089
 0.3108
[torch.FloatTensor of size 5]
 
Variable containing:
 1
[torch.FloatTensor of size 1]
 
Variable containing:
-3.0350
-3.0885
-0.7201
-2.2176
-1.1686
[torch.FloatTensor of size 5]

二、Pytorch建立網路

1) word embedding詞嵌入

通過nn.Embedding(m,n)實現，m表示所有的單詞數目，n表示詞嵌入的維度。

word_to_idx = {'hello':0,'world':1}
embeds = nn.Embedding(2,5) #即兩個單詞，單詞的詞嵌入維度為5
hello_idx = torch.LongTensor([word_to_idx['hello']])
hello_idx = autograd.Variable(hello_idx)
hello_embed = embeds(hello_idx)
print(hello_embed)

output:

Variable containing:
-0.6982 0.3909 -1.0760 -1.6215 0.4429
[torch.FloatTensor of size 1x5]

2) N-Gram 語言模型

先介紹一下N-Gram語言模型，給定一個單詞序列 ，計算 ，其中 是序列的第 個單詞。

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
import torch.optim as optim
 
from six.moves import xrange

對句子進行分詞：

context_size = 2
embed_dim = 10
text_sequence = """When forty winters shall besiege thy brow,And dig deep trenches in thy beauty's field,Thy youth's proud livery so gazed on now,Will be a totter'd weed of small worth held:
Then being asked,where all thy beauty lies,Where all the treasure of thy lusty days;
To say,within thine own deep sunken eyes,Were an all-eating shame,and thriftless praise.
How much more praise deserv'd thy beauty's use,If thou couldst answer 'This fair child of mine
Shall sum my count,and make my old excuse,'
Proving his beauty by succession thine!
This were to be new made when thou art old,And see thy blood warm when thou feel'st it cold.""".split()
#分詞
trigrams = [ ([text_sequence[i],text_sequence[i+1]],text_sequence[i+2]) for i in xrange(len(text_sequence) - 2) ]
trigrams[:10]

分詞的形式為：

#建立vocab索引
vocab = set(text_sequence)
word_to_ix = {word: i for i,word in enumerate(vocab)}

建立N-Gram Language model

#N-Gram Language model
class NGramLanguageModeler(nn.Module): 
 def __init__(self,vocab_size,embed_dim,context_size):
  super(NGramLanguageModeler,self).__init__()
  #詞嵌入
  self.embedding = nn.Embedding(vocab_size,embed_dim)
  #兩層線性分類器
  self.linear1 = nn.Linear(embed_dim*context_size,128)
  self.linear2 = nn.Linear(128,vocab_size)
  
 def forward(self,input):
  embeds = self.embedding(input).view((1,-1)) #2,10拉直為20
  out = F.relu(self.linear1(embeds))
  out = F.relu(self.linear2(out))
  log_probs = F.log_softmax(out)
  return log_probs  

輸出模型看一下網路結構

#輸出模型看一下網路結構
model = NGramLanguageModeler(96,10,2)
print(model)

定義損失函式和優化器

#定義損失函式以及優化器
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(),lr = 0.01)
model = NGramLanguageModeler(len(vocab),context_size)
losses = []

模型訓練

#模型訓練
for epoch in xrange(10):
 total_loss = torch.Tensor([0])
 for context,target in trigrams:
  #1.處理資料輸入為索引向量
  #print(context)
  #注：python3中map函式前要加上list()轉換為列表形式
  context_idxs = list(map(lambda w: word_to_ix[w],context))
  #print(context_idxs)
  context_var = autograd.Variable( torch.LongTensor(context_idxs) )
 
  
  #2.梯度清零
  model.zero_grad()
  
  #3.前向傳播，計算下一個單詞的概率
  log_probs = model(context_var)
  
  #4.損失函式
  loss = loss_function(log_probs,autograd.Variable(torch.LongTensor([word_to_ix[target]])))
  
  #反向傳播及梯度更新
  loss.backward()
  optimizer.step()
  
  total_loss += loss.data 
 losses.append(total_loss)
print(losses)

以上這篇Pytorch在NLP中的簡單應用詳解就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。