参考

https://blog.csdn.net/Metal1/article/details/132886936

https://blog.csdn.net/L_goodboy/article/details/136347947

导入包

import jieba
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
from sklearn.decomposition import PCA
from tqdm import tqdm, trange
torch.manual_seed(1)

加载数据和切词

# 加载停用词词表
def load_stop_words():"""停用词是指在信息检索中，为节省存储空间和提高搜索效率，在处理自然语言数据（或文本）之前或之后会自动过滤掉某些字或词"""with open('data/stopwords.txt', "r", encoding="utf-8") as f:return f.read().split("\n")# 加载文本,切词
def cut_words():stop_words = load_stop_words()with open('data/zh.txt', encoding='utf8') as f:allData = f.readlines()result = []for words in allData:c_words = jieba.lcut(words)for word in c_words:if word not in stop_words and word != "\n":result.append(word)return result# 加载文本,切词
def cut_sentense(str):stop_words = load_stop_words()with open('data/zh.txt', encoding='utf8') as f:allData = f.readlines()result = []c_words = jieba.lcut(str)for word in c_words:if word not in stop_words and word != "\n":result.append(word)return result

获取wordList、raw_text

wordList = []
data = cut_words()
data

count = 0
for words in data:if words not in wordList:wordList.append(words)
print("wordList=", wordList)raw_text = wordList
print("raw_text=", raw_text)

# 超参数
learning_rate = 0.003
# 放cuda或者cpu里
device = torch.device('cpu')
# 上下文信息，即涉及文本的前n个和后n个
context_size = 2
# 词嵌入的维度，即一个单词用多少个浮点数表示比如 the=[10.2323,12.132133,4.1219774]...
embedding_dim = 100
epoch = 10

def make_context_vector(context, word_to_ix):idxs = [word_to_ix[w] for w in context]return torch.tensor(idxs, dtype=torch.long)

获取vocab、vocab_size

# 把所有词集合转成dict
vocab = set(wordList)
vocab_size = len(vocab)
vocab,vocab_size

word_to_idx、idx_to_word

word_to_idx = {word: i for i, word in enumerate(vocab)}
idx_to_word = {i: word for i, word in enumerate(vocab)}

准备训练数据

data3 = []
window_size1=2
for i,word in enumerate(raw_text):target = raw_text[i]contexts=raw_text[max(i - window_size1, 0): min(i + window_size1 + 1, len(raw_text))]for context in contexts:if target!=context:data3.append((context,target))
data3,len(data3)

准备模型和参数

class SkipGramModel(nn.Module):def __init__(self, vocab_size, embedding_dim):super(SkipGramModel, self).__init__()self.embedding = nn.Embedding(vocab_size, embedding_dim)self.linear = nn.Linear(embedding_dim, vocab_size)def forward(self, center_word):embedded = self.embedding(center_word)output = self.linear(embedded)return outputmodel = SkipGramModel(vocab_size, embedding_dim)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

训练模型

# Training
for epoch in tqdm(range(2000)):loss_sum = 0for target,input in data3:targetidx=word_to_idx[target]inputidx=word_to_idx[input]output=model(torch.tensor(inputidx,dtype=torch.long))loss=criterion(output,torch.tensor(targetidx))optimizer.zero_grad()  # 清空梯度loss.backward()  # 反向传播optimizer.step()  # 更新参数loss_sum += loss.item()if (epoch+1) % 10 == 0:print("loss is ",loss_sum/len(data2),loss.item())

保存模型

torch.save(model.state_dict(),"skipgram.pth")

简单预测

inputidx=word_to_idx["refresh"]output=model(torch.tensor(inputidx,dtype=torch.long))
print(output.topk(4))
cc,index=output.topk(4)
idx_to_word[index[0].item()],idx_to_word[index[1].item()],idx_to_word[index[2].item()],idx_to_word[index[3].item()]def predict(centerword):inputidx=word_to_idx[centerword]output=model(torch.tensor(inputidx,dtype=torch.long))print(output.topk(4))cc,index=output.topk(4)idx_to_word[index[0].item()],idx_to_word[index[1].item()],idx_to_word[index[2].item()],idx_to_word[index[3].item()]

获取训练后的词向量

trained_vector_dic={}
for word, idx in word_to_idx.items(): # 输出每个词的嵌入向量trained_vector_dic[word]=model.embedding.weight[idx]

trained_vector_dic

画图看下分布

fig, ax = plt.subplots() 
for word, idx in word_to_idx.items():# 获取每个单词的嵌入向量vec = model.embedding.weight[:,idx].detach().numpy() ax.scatter(vec[0], vec[1]) # 在图中绘制嵌入向量的点ax.annotate(word, (vec[0], vec[1]), fontsize=12) # 点旁添加单词标签
plt.title(' 二维词嵌入 ') # 图题
plt.xlabel(' 向量维度 1') # X 轴 Label
plt.ylabel(' 向量维度 2') # Y 轴 Label
plt.show() # 显示图

利用词向量计算相似度

余弦

# https://blog.csdn.net/qq_41487299/article/details/106299882

import torch
import torch.nn.functional as F# 计算余弦相似度
cosine_similarity = F.cosine_similarity(x.unsqueeze(0), y.unsqueeze(0))print(cosine_similarity)cosine_similarity1 = F.cosine_similarity(torch.tensor(trained_vector_dic["保持数据"].unsqueeze(0)), torch.tensor(trained_vector_dic["打印信息"]).unsqueeze(0))
print(cosine_similarity1)

点积

dot_product = torch.dot(torch.tensor(trained_vector_dic["保持数据"]), torch.tensor(trained_vector_dic["打印信息"]))
x_length = torch.norm(torch.tensor(trained_vector_dic["保持数据"]))
y_length = torch.norm(torch.tensor(trained_vector_dic["打印信息"]))
similarity = dot_product / (x_length * y_length)print(similarity)

torch.tensor(trained_vector_dic["参数值"]),len(trained_vector_dic)

c1=cos(trained_vector_dic["删除"],trained_vector_dic["服务"])
print(c1)