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AI学习记录 - Word2Vec 超详细解析

news 2025/12/2 2:14:18

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我们有一堆文本，词汇拆分

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"]
sentence_list = " ".join(sentences).split() # ['jack', 'like', 'dog']
vocab = list(set(sentence_list))
word2idx = {w:i for i, w in enumerate(vocab)}
print("word2idx", word2idx)
vocab_size = len(vocab)
print("vocab_size", vocab_size)

打印如下，一共有13个词汇：

word2idx {'banana': 0, 'animal': 1, 'hate': 2, 'like': 3, 'jack': 4, 'dog': 5, 'fish': 6, 'apple': 7, 'book': 8, 'milk': 9, 'music': 10, 'cat': 11, 'movie': 12}
vocab_size 13

生成训练集合，生成规则是【目标词，上一个词】，【目标词，下一个词】，下面使用的是下标

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

skip_grams变量打印如下：

[[5, 4], [5, 3], [5, 4], [5, 3], [4, 3], [4, 5], [4, 3], [4, 11], [3, 5], [3, 4], [3, 11], [3, 4], [11, 4], [11, 3], [11, 4], [11, 3], [4, 3], [4, 11], [4, 3], [4, 1], [3, 11], [3, 4], [3, 1], [3, 5], [1, 4], [1, 3], [1, 5], [1, 11], [5, 3], [5, 1], [5, 11], [5, 1], [11, 1], [11, 5], [11, 1], [11, 0], [1, 5], [1, 11], [1, 0], [1, 7], [0, 11], [0, 1], [0, 7], [0, 11], [7, 1], [7, 0], [7, 11], [7, 5], [11, 0], [11, 7], [11, 5], [11, 3], [5, 7], [5, 11], [5, 3], [5, 5],

我们现在有了训练集，那么现在需要构造模型结构，假设原one-hot编码是13位，训练出来的词向量位2位，这样子我们就减少了词汇的维度并建立词与词的关联，如下图：

在这里插入图片描述

代码为： W矩阵就是我们需要训练的矩阵，V矩阵在训练完成之后我们就丢弃了。

class Word2Vec(nn.Module):def __init__(self):super(Word2Vec, self).__init__()self.W = nn.Parameter(torch.randn(vocab_size, m).type(dtype))self.V = nn.Parameter(torch.randn(m, vocab_size).type(dtype))def forward(self, X):# X : [batch_size, vocab_size]hidden = torch.mm(X, self.W) # [batch_size, m]output = torch.mm(hidden, self.V) # [batch_size, vocab_size]return output

反向传播阶段照样是使用 CrossEntropyLoss 计算误差，关于 CrossEntropyLoss 函数在上一个章节有介绍，利用实际输出和标签的损失进行反向传播，如果你想了解权重怎么调整，继续往前翻找也可查看。

在这里插入图片描述

完整的word2Vec代码


import torch
import numpy as np
import torch.nn as nn
import torch.optim as optimizer
import torch.utils.data as Datadevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.FloatTensorsentences = ["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"]
sentence_list = " ".join(sentences).split() # ['jack', 'like', 'dog']
vocab = list(set(sentence_list))
word2idx = {w:i for i, w in enumerate(vocab)}
vocab_size = len(vocab)# model parameters
C = 2 # window size
batch_size = 8
m = 2 # word embedding dimskip_grams = []
for idx in range(C, len(sentence_list) - C):center = word2idx[sentence_list[idx]]context_idx = list(range(idx - C, idx)) + list(range(idx + 1, idx + C + 1))context = [word2idx[sentence_list[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 a, b in skip_grams:input_data.append(np.eye(vocab_size)[a])output_data.append(b)return input_data, output_datainput_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)class Word2Vec(nn.Module):def __init__(self):super(Word2Vec, self).__init__()self.W = nn.Parameter(torch.randn(vocab_size, m).type(dtype))self.V = nn.Parameter(torch.randn(m, vocab_size).type(dtype))def forward(self, X):# X : [batch_size, vocab_size]hidden = torch.mm(X, self.W) # [batch_size, m]output = torch.mm(hidden, self.V) # [batch_size, vocab_size]return outputmodel = Word2Vec().to(device)
loss_fn = nn.CrossEntropyLoss().to(device)
optim = optimizer.Adam(model.parameters(), lr=1e-3)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 = loss_fn(pred, batch_y)if (epoch + 1) % 1000 == 0:print(epoch + 1, i, loss.item())optim.zero_grad()loss.backward()optim.step()import matplotlib.pyplot as plt
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()