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Word2Vec的PyTorch实现(乞丐版)

June 11, 2020 • Read: 2955 • Deep Learning阅读设置

B站视频讲解

源码来自于nlp-tutorial,我在其基础上进行了修改

'''
  code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
  6/11/2020
'''
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.utils.data as Data

dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

文本预处理

sentences = ["jack like dog", "jack like cat", "jack like animal",
  "dog cat animal", "banana apple cat dog like", "dog fish milk like",
  "dog cat animal like", "jack like apple", "apple like", "jack like banana",
  "apple banana jack movie book music like", "cat dog hate", "cat dog like"]

word_sequence = " ".join(sentences).split() # ['jack', 'like', 'dog', 'jack', 'like', 'cat', 'animal',...]
vocab = list(set(word_sequence)) # build words vocabulary
word2idx = {w: i for i, w in enumerate(vocab)} # {'jack':0, 'like':1,...}

模型相关参数

# Word2Vec Parameters
batch_size = 8
embedding_size = 2  # 2 dim vector represent one word
C = 2 # window size
voc_size = len(vocab)

数据预处理

# 1.
skip_grams = []
for idx in range(C, len(word_sequence) - C):
  center = word2idx[word_sequence[idx]] # center word
  context_idx = list(range(idx - C, idx)) + list(range(idx + 1, idx + C + 1)) # context word idx
  context = [word2idx[word_sequence[i]] for i in context_idx]
  for w in context:
    skip_grams.append([center, w])

# 2.
def make_data(skip_grams):
  input_data = []
  output_data = []
  for i in range(len(skip_grams)):
    input_data.append(np.eye(voc_size)[skip_grams[i][0]])
    output_data.append(skip_grams[i][1])
  return input_data, output_data

# 3.
input_data, output_data = make_data(skip_grams)
input_data, output_data = torch.Tensor(input_data), torch.LongTensor(output_data)
dataset = Data.TensorDataset(input_data, output_data)
loader = Data.DataLoader(dataset, batch_size, True)

假设所有文本分词,转为索引之后的list如下图所示

根据论文所述,我这里设定window size=2,即每个中心词左右各取2个词作为背景词,那么对于上面的list,窗口每次滑动,选定的中心词和背景词如下图所示

那么skip_grams变量里存的就是中心词和背景词一一配对后的list,例如中心词2,有背景词0,1,0,1,一一配对以后就会产生[2,0],[2,1],[2,0],[2,1]。skip_grams如下图所示

由于Word2Vec的输入是one-hot表示,所以我们先构建一个对角全1的矩阵,利用np.eye(rows)方法,其中的参数rows表示全1矩阵的行数,对于这个问题来说,语料库中总共有多少个单词,就有多少行

然后根据skip_grams每行第一列的值,取出相应全1矩阵的行。将这些取出的行,append到一个list中去,最终的这个list就是所有的样本X。标签不需要one-hot表示,只需要类别值,所以只用把skip_grams中每行的第二列取出来存起来即可

最后第三步就是构建dataset,然后定义DataLoader

构建模型

# Model
class Word2Vec(nn.Module):
  def __init__(self):
    super(Word2Vec, self).__init__()

    # W and V is not Traspose relationship
    self.W = nn.Parameter(torch.randn(voc_size, embedding_size).type(dtype))
    self.V = nn.Parameter(torch.randn(embedding_size, voc_size).type(dtype))

  def forward(self, X):
    # X : [batch_size, voc_size] one-hot
    # torch.mm only for 2 dim matrix, but torch.matmul can use to any dim
    hidden_layer = torch.matmul(X, self.W) # hidden_layer : [batch_size, embedding_size]
    output_layer = torch.matmul(hidden_layer, self.V) # output_layer : [batch_size, voc_size]
    return output_layer

model = Word2Vec().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)

训练

# Training
for epoch in range(2000):
  for i, (batch_x, batch_y) in enumerate(loader):
    batch_x = batch_x.to(device)
    batch_y = batch_y.to(device)
    pred = model(batch_x)
    loss = criterion(pred, batch_y)
    if (epoch + 1) % 1000 == 0:
      print(epoch + 1, i, loss.item())
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

由于我这里每个词是用的2维的向量去表示,所以可以将每个词在平面直角坐标系中标记出来,看看各个词之间的距离

for i, label in enumerate(vocab):
  W, WT = model.parameters()
  x,y = float(W[i][0]), float(W[i][1])
  plt.scatter(x, y)
  plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
plt.show()

完整代码如下:

'''
  code by Tae Hwan Jung(Jeff Jung) @graykode, modify by wmathor
  6/11/2020
'''
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.utils.data as Data

dtype = torch.FloatTensor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

sentences = ["jack like dog", "jack like cat", "jack like animal",
  "dog cat animal", "banana apple cat dog like", "dog fish milk like",
  "dog cat animal like", "jack like apple", "apple like", "jack like banana",
  "apple banana jack movie book music like", "cat dog hate", "cat dog like"]

word_sequence = " ".join(sentences).split() # ['jack', 'like', 'dog', 'jack', 'like', 'cat', 'animal',...]
vocab = list(set(word_sequence)) # build words vocabulary
word2idx = {w: i for i, w in enumerate(vocab)} # {'jack':0, 'like':1,...}

# Word2Vec Parameters
batch_size = 8
embedding_size = 2  # 2 dim vector represent one word
C = 2 # window size
voc_size = len(vocab)

skip_grams = []
for idx in range(C, len(word_sequence) - C):
  center = word2idx[word_sequence[idx]] # center word
  context_idx = list(range(idx - C, idx)) + list(range(idx + 1, idx + C + 1)) # context word idx
  context = [word2idx[word_sequence[i]] for i in context_idx]
  for w in context:
    skip_grams.append([center, w])

def make_data(skip_grams):
  input_data = []
  output_data = []
  for i in range(len(skip_grams)):
    input_data.append(np.eye(voc_size)[skip_grams[i][0]])
    output_data.append(skip_grams[i][1])
  return input_data, output_data

input_data, output_data = make_data(skip_grams)
input_data, output_data = torch.Tensor(input_data), torch.LongTensor(output_data)
dataset = Data.TensorDataset(input_data, output_data)
loader = Data.DataLoader(dataset, batch_size, True)

# Model
class Word2Vec(nn.Module):
  def __init__(self):
    super(Word2Vec, self).__init__()

    # W and V is not Traspose relationship
    self.W = nn.Parameter(torch.randn(voc_size, embedding_size).type(dtype))
    self.V = nn.Parameter(torch.randn(embedding_size, voc_size).type(dtype))

  def forward(self, X):
    # X : [batch_size, voc_size] one-hot
    # torch.mm only for 2 dim matrix, but torch.matmul can use to any dim
    hidden_layer = torch.matmul(X, self.W) # hidden_layer : [batch_size, embedding_size]
    output_layer = torch.matmul(hidden_layer, self.V) # output_layer : [batch_size, voc_size]
    return output_layer

model = Word2Vec().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)

# Training
for epoch in range(2000):
  for i, (batch_x, batch_y) in enumerate(loader):
    batch_x = batch_x.to(device)
    batch_y = batch_y.to(device)
    pred = model(batch_x)
    loss = criterion(pred, batch_y)
    if (epoch + 1) % 1000 == 0:
      print(epoch + 1, i, loss.item())
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
for i, label in enumerate(vocab):
  W, WT = model.parameters()
  x,y = float(W[i][0]), float(W[i][1])
  plt.scatter(x, y)
  plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
plt.show()

Last Modified: April 29, 2021
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6 Comments
  1. Fantsy Fantsy

    看完你的视频之后,觉得你对代码很熟悉,对pytorch很熟悉。能不能出一期视频或者博客介绍一下学习pytorch的方法?感谢!

    1. mathor mathor

      @Fantsy好的,考虑一下

  2. Compass Compass

    博主你好,在构建数据集计算中心词和context那里,应该对每一句话分别计算中心词和context吧,应该for sen in sentences,再在每一个sen中去构建中心词和context,而不是整个word_sequence

    1. 尘世猫 尘世猫

      @Compass我也是这么觉得,楼主看到了麻烦回复一下

  3. zzflybird zzflybird

    你好,这篇博客的图片没有了。

    1. mathor mathor

      @zzflybird还有,请刷新一下