实战总结"/>
word2vec pytorch代码实战总结
这是我在b站看到的一条关于word2vec 代码实战的视频的笔记~
b站链接在这Word2Vec的PyTorch实现_哔哩哔哩_bilibili
下面是我自己写的一个简单的语料库,大家可以自己加上一些句子,或者自己写一个简单的语料库
sentences = ["i am a student ","i am a boy ","studying is not a easy work ","japanese are bad guys ","we need peace ","computer version is increasingly popular ","the word will get better and better "
]
我们需要对原始语料库的句子进行分割,得到一个个单词
sentence_list = "".join(sentences).split() # 语料库---有重复单词
这样我们就得到了分割后的单词语料库,但是需要注意的是,这样的得到的列表含有很多重复的单词,因此我们需要用集合set进行去重处理
vocab = list(set(sentence_list)) # 词汇表---没有重复单词
我们将去重的单词库命名为词汇表vocab,我们还需要单词和索引的对应关系,所以我们定义一个字典变量word2idx
word2idx = {w: i for i, w in enumerate(vocab)} # 词汇表生成的字典,包含了单词和索引的键值对
我们定义一个列表变量将中心词和上下文的索引都保存进去
skip_grams = []
for word_idx in range(w_size, len(sentence_list)-w_size): # word_idx---是原语料库中的词索引center_word_vocab_idx = word2idx[sentence_list[word_idx]] # 中心词在词汇表里的索引context_word_idx = list(range(word_idx-w_size, word_idx)) + list(range(word_idx+1, word_idx+w_size+1)) # 上下文词在语料库里的索引context_word_vocab_idx = [word2idx[sentence_list[i]] for i in context_word_idx] # 上下文词在词汇表里的索引for idx in context_word_vocab_idx:skip_grams.append([center_word_vocab_idx, idx]) # 加入进来的都是索引值
w_size是上下文的窗口大小
好了,中心词和上下文的索引都有了,接下来就是取出对应的单词作为输入了
def make_data(skip_grams):input_data = []output_data = []for center, context in skip_grams:input_data.append(np.eye(vocab_size)[center])output_data.append(context)return input_data, output_data
个人认为这里的”输入“”输出“没有实际意义,只是因为我们要做的是skip_gram,根据中心词预测上下文
加载数据
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=dataset, batch_size=batch_size, shuffle=True)
建立模型
class Word2Vec(nn.Module):def __init__(self):super(Word2Vec, self).__init__()self.W = nn.Parameter(torch.randn(vocab_size, w_size).type(dtype))self.V = nn.Parameter(torch.randn(w_size, vocab_size).type(dtype))def forward(self, X):hidden = torch.mm(X, self.W)output = torch.mm(hidden, self.V)return output
开始训练
model = 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()
以下是全部代码
import matplotlib.pyplot as plt
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 = ["i am a student ","i am a boy ","studying is not a easy work ","japanese are bad guys ","we need peace ","computer version is increasingly popular ","the word will get better and better "
]
sentence_list = "".join(sentences).split() # 语料库---有重复单词
vocab = list(set(sentence_list)) # 词汇表---没有重复单词
word2idx = {w: i for i, w in enumerate(vocab)} # 词汇表生成的字典,包含了单词和索引的键值对
vocab_size = len(vocab)w_size = 2 # 上下文单词窗口大小
batch_size = 8
word_dim = 2 # 词向量维度
skip_grams = []
for word_idx in range(w_size, len(sentence_list)-w_size): # word_idx---是原语料库中的词索引center_word_vocab_idx = word2idx[sentence_list[word_idx]] # 中心词在词汇表里的索引context_word_idx = list(range(word_idx-w_size, word_idx)) + list(range(word_idx+1, word_idx+w_size+1)) # 上下文词在语料库里的索引context_word_vocab_idx = [word2idx[sentence_list[i]] for i in context_word_idx] # 上下文词在词汇表里的索引for idx in context_word_vocab_idx:skip_grams.append([center_word_vocab_idx, idx]) # 加入进来的都是索引值def make_data(skip_grams):input_data = []output_data = []for center, context in skip_grams:input_data.append(np.eye(vocab_size)[center])output_data.append(context)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=dataset, batch_size=batch_size, shuffle=True)class Word2Vec(nn.Module):def __init__(self):super(Word2Vec, self).__init__()self.W = nn.Parameter(torch.randn(vocab_size, w_size).type(dtype))self.V = nn.Parameter(torch.randn(w_size, vocab_size).type(dtype))def forward(self, X):hidden = torch.mm(X, self.W)output = torch.mm(hidden, self.V)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()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()
效果图
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word2vec pytorch代码实战总结
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