pytorch入门学习第二课词向量和语言模型

01词向量

在计算机中表示一个词

离散表示: One-hot表示

离散表示：Bag of Words

离散表示：Bi-gram和N-gram

离散表示的问题

词编码需要保证词的相似性

简单 词/短语 翻译

词嵌入效果评估: 词类比任务

02语言模型

语言模型

语言模型的评价

基于神经网络的语言模型(Neural Language Model)

循环神经网络(Recurrent Neural Network)

h0一般就是一个全部为0的向量


如何训练？Cross Entropy损失函数

随机梯度下降 SGD

梯度消失和爆炸问题

根据反向传播（链式法则），梯度会不断相乘，很容易梯度消失或者爆炸

训练RNN
长短记忆网络(Long Short-term Memory)
LSTM是RNN的一种，大体结构几乎一样。区别是？
它的“记忆细胞”改造过。
该记的信息会一直传递，不该记的会被“门”截断。

RNN记忆细胞

LSTM记忆细胞

Gated Recurrent Unit

第二课课后复习思维导图

代码

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as tud
from torch.nn.parameter import Parameter

from collections import Counter
import numpy as np
import random
import math

import pandas as pd
import scipy
import sklearn
from sklearn.metrics.pairwise import cosine_similarity

USE_CUDA = torch.cuda.is_available()

# 为了保证实验结果可以复现，我们经常会把各种random seed固定在某一个值
random.seed(53113)
np.random.seed(53113)
torch.manual_seed(53113)
if USE_CUDA:
    torch.cuda.manual_seed(53113)
    
# 设定一些超参数
    
K = 100 # number of negative samples
C = 3 # nearby words threshold
NUM_EPOCHS = 2 # The number of epochs of training
MAX_VOCAB_SIZE = 30000 # the vocabulary size
BATCH_SIZE = 128 # the batch size
LEARNING_RATE = 0.2 # the initial learning rate
EMBEDDING_SIZE = 100
       
    
LOG_FILE = "word-embedding.log"

# tokenize函数，把一篇文本转化成一个个单词
def word_tokenize(text):
    return text.split()
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1. 从文本文件中读取所有的文字，通过这些文本创建一个vocabulary
2. 由于单词数量可能太大，我们只选取最常见的MAX_VOCAB_SIZE个单词
3. 我们添加一个UNK单词表示所有不常见的单词
4. 我们需要记录单词到index的mapping，以及index到单词的mapping，单词的count，单词的(normalized)
   frequency，以及单词总数。

with open("text8.train.txt", "r") as fin:
    text = fin.read()
    
text = [w for w in word_tokenize(text.lower())]
vocab = dict(Counter(text).most_common(MAX_VOCAB_SIZE-1))
vocab["<unk>"] = len(text) - np.sum(list(vocab.values()))
idx_to_word = [word for word in vocab.keys()] 
word_to_idx = {word:i for i, word in enumerate(idx_to_word)}

word_counts = np.array([count for count in vocab.values()], dtype=np.float32)
word_freqs = word_counts / np.sum(word_counts)
word_freqs = word_freqs ** (3./4.)
word_freqs = word_freqs / np.sum(word_freqs) # 用来做 negative sampling
VOCAB_SIZE = len(idx_to_word)
VOCAB_SIZE
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output：30000
1

实现Dataloader
一个dataloader需要以下内容：

1. 把所有text编码成数字，然后用subsampling预处理这些文字。
2. 保存vocabulary，单词count，normalized word frequency
3. 每个iteration sample一个中心词
4. 根据当前的中心词返回context单词
5. 根据中心词sample一些negative单词
6. 返回单词的counts
7. 这里有一个好的tutorial介绍如何使用PyTorch dataloader.
   为了使用dataloader，我们需要定义以下两个function:
8. len function需要返回整个数据集中有多少个item
9. get 根据给定的index返回一个item

有了dataloader之后，我们可以轻松随机打乱整个数据集，拿到一个batch的数据等等。

class WordEmbeddingDataset(tud.Dataset):
    def __init__(self, text, word_to_idx, idx_to_word, word_freqs, word_counts):
        ''' text: a list of words, all text from the training dataset
            word_to_idx: the dictionary from word to idx
            idx_to_word: idx to word mapping
            word_freq: the frequency of each word
            word_counts: the word counts
        '''
        super(WordEmbeddingDataset, self).__init__()
        self.text_encoded = [word_to_idx.get(t, VOCAB_SIZE-1) for t in text]
        self.text_encoded = torch.Tensor(self.text_encoded).long()
        self.word_to_idx = word_to_idx
        self.idx_to_word = idx_to_word
        self.word_freqs = torch.Tensor(word_freqs)
        self.word_counts = torch.Tensor(word_counts)
        
    def __len__(self):
        ''' 返回整个数据集（所有单词）的长度
        '''
        return len(self.text_encoded)
        
    def __getitem__(self, idx):
        ''' 这个function返回以下数据用于训练
            - 中心词
            - 这个单词附近的(positive)单词
            - 随机采样的K个单词作为negative sample
        '''
        center_word = self.text_encoded[idx]
        pos_indices = list(range(idx-C, idx)) + list(range(idx+1, idx+C+1))
        pos_indices = [i%len(self.text_encoded) for i in pos_indices]
        pos_words = self.text_encoded[pos_indices] 
        neg_words = torch.multinomial(self.word_freqs, K * pos_words.shape[0], True)
        
        return center_word, pos_words, neg_words 

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创建dataset和dataloader

dataset = WordEmbeddingDataset(text, word_to_idx, idx_to_word, word_freqs, word_counts)
dataloader = tud.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)
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定义PyTorch模型

class EmbeddingModel(nn.Module):
    def __init__(self, vocab_size, embed_size):
        ''' 初始化输出和输出embedding
        '''
        super(EmbeddingModel, self).__init__()
        self.vocab_size = vocab_size
        self.embed_size = embed_size
        
        initrange = 0.5 / self.embed_size
        self.out_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False)
        self.out_embed.weight.data.uniform_(-initrange, initrange)
        
        
        self.in_embed = nn.Embedding(self.vocab_size, self.embed_size, sparse=False)
        self.in_embed.weight.data.uniform_(-initrange, initrange)
        
        
    def forward(self, input_labels, pos_labels, neg_labels):
        '''
        input_labels: 中心词, [batch_size]
        pos_labels: 中心词周围 context window 出现过的单词 [batch_size * (window_size * 2)]
        neg_labelss: 中心词周围没有出现过的单词，从 negative sampling 得到 [batch_size, (window_size * 2 * K)]
        
        return: loss, [batch_size]
        '''
        
        batch_size = input_labels.size(0)
        
        input_embedding = self.in_embed(input_labels) # B * embed_size
        pos_embedding = self.out_embed(pos_labels) # B * (2*C) * embed_size
        neg_embedding = self.out_embed(neg_labels) # B * (2*C * K) * embed_size
      
        log_pos = torch.bmm(pos_embedding, input_embedding.unsqueeze(2)).squeeze() # B * (2*C)
        log_neg = torch.bmm(neg_embedding, -input_embedding.unsqueeze(2)).squeeze() # B * (2*C*K)

        log_pos = F.logsigmoid(log_pos).sum(1)
        log_neg = F.logsigmoid(log_neg).sum(1) # batch_size
       
        loss = log_pos + log_neg
        
        return -loss
    
    def input_embeddings(self):
        return self.in_embed.weight.data.cpu().numpy()
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定义一个模型以及把模型移动到GPU

model = EmbeddingModel(VOCAB_SIZE, EMBEDDING_SIZE)
if USE_CUDA:
    model = model.cuda()
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下面是评估模型的代码，以及训练模型的代码

def evaluate(filename, embedding_weights): 
    if filename.endswith(".csv"):
        data = pd.read_csv(filename, sep=",")
    else:
        data = pd.read_csv(filename, sep="\t")
    human_similarity = []
    model_similarity = []
    for i in data.iloc[:, 0:2].index:
        word1, word2 = data.iloc[i, 0], data.iloc[i, 1]
        if word1 not in word_to_idx or word2 not in word_to_idx:
            continue
        else:
            word1_idx, word2_idx = word_to_idx[word1], word_to_idx[word2]
            word1_embed, word2_embed = embedding_weights[[word1_idx]], embedding_weights[[word2_idx]]
            model_similarity.append(float(sklearn.metrics.pairwise.cosine_similarity(word1_embed, word2_embed)))
            human_similarity.append(float(data.iloc[i, 2]))

    return scipy.stats.spearmanr(human_similarity, model_similarity)# , model_similarity

def find_nearest(word):
    index = word_to_idx[word]
    embedding = embedding_weights[index]
    cos_dis = np.array([scipy.spatial.distance.cosine(e, embedding) for e in embedding_weights])
    return [idx_to_word[i] for i in cos_dis.argsort()[:10]]
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训练模型：

模型一般需要训练若干个epoch
每个epoch我们都把所有的数据分成若干个batch
把每个batch的输入和输出都包装成cuda tensor
forward pass，通过输入的句子预测每个单词的下一个单词
用模型的预测和正确的下一个单词计算cross entropy loss
清空模型当前gradient
backward pass
更新模型参数
每隔一定的iteration输出模型在当前iteration的loss，以及在验证数据集上做模型的评估
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optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
for e in range(NUM_EPOCHS):
    for i, (input_labels, pos_labels, neg_labels) in enumerate(dataloader):
        
        
        # TODO
        input_labels = input_labels.long()
        pos_labels = pos_labels.long()
        neg_labels = neg_labels.long()
        if USE_CUDA:
            input_labels = input_labels.cuda()
            pos_labels = pos_labels.cuda()
            neg_labels = neg_labels.cuda()
            
        optimizer.zero_grad()
        loss = model(input_labels, pos_labels, neg_labels).mean()
        loss.backward()
        optimizer.step()

        if i % 100 == 0:
            with open(LOG_FILE, "a") as fout:
                fout.write("epoch: {}, iter: {}, loss: {}\n".format(e, i, loss.item()))
                print("epoch: {}, iter: {}, loss: {}".format(e, i, loss.item()))
            
        
        if i % 2000 == 0:
            embedding_weights = model.input_embeddings()
            sim_simlex = evaluate("simlex-999.txt", embedding_weights)
            sim_men = evaluate("men.txt", embedding_weights)
            sim_353 = evaluate("wordsim353.csv", embedding_weights)
            with open(LOG_FILE, "a") as fout:
                print("epoch: {}, iteration: {}, simlex-999: {}, men: {}, sim353: {}, nearest to monster: {}\n".format(
                    e, i, sim_simlex, sim_men, sim_353, find_nearest("monster")))
                fout.write("epoch: {}, iteration: {}, simlex-999: {}, men: {}, sim353: {}, nearest to monster: {}\n".format(
                    e, i, sim_simlex, sim_men, sim_353, find_nearest("monster")))
                
    embedding_weights = model.input_embeddings()
    np.save("embedding-{}".format(EMBEDDING_SIZE), embedding_weights)
    torch.save(model.state_dict(), "embedding-{}.th".format(EMBEDDING_SIZE))
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在 MEN 和 Simplex-999 数据集上做评估

embedding_weights = model.input_embeddings()
print("simlex-999", evaluate("simlex-999.txt", embedding_weights))
print("men", evaluate("men.txt", embedding_weights))
print("wordsim353", evaluate("wordsim353.csv", embedding_weights))
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simlex-999 SpearmanrResult(correlation=0.17251697429101504, pvalue=7.863946056740345e-08)
men SpearmanrResult(correlation=0.1778096817088841, pvalue=7.565661657312768e-20)
wordsim353 SpearmanrResult(correlation=0.27153702278146635, pvalue=8.842165885381714e-07)
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寻找nearest neighbors

for word in ["good", "fresh", "monster", "green", "like", "america", "chicago", "work", "computer", "language"]:
    print(word, find_nearest(word))
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good ['good', 'bad', 'perfect', 'hard', 'questions', 'alone', 'money', 'false', 'truth', 'experience']
fresh ['fresh', 'grain', 'waste', 'cooling', 'lighter', 'dense', 'mild', 'sized', 'warm', 'steel']
monster ['monster', 'giant', 'robot', 'hammer', 'clown', 'bull', 'demon', 'triangle', 'storyline', 'slogan']
green ['green', 'blue', 'yellow', 'white', 'cross', 'orange', 'black', 'red', 'mountain', 'gold']
like ['like', 'unlike', 'etc', 'whereas', 'animals', 'soft', 'amongst', 'similarly', 'bear', 'drink']
america ['america', 'africa', 'korea', 'india', 'australia', 'turkey', 'pakistan', 'mexico', 'argentina', 'carolina']
chicago ['chicago', 'boston', 'illinois', 'texas', 'london', 'indiana', 'massachusetts', 'florida', 'berkeley', 'michigan']
work ['work', 'writing', 'job', 'marx', 'solo', 'label', 'recording', 'nietzsche', 'appearance', 'stage']
computer ['computer', 'digital', 'electronic', 'audio', 'video', 'graphics', 'hardware', 'software', 'computers', 'program']
language ['language', 'languages', 'alphabet', 'arabic', 'grammar', 'pronunciation', 'dialect', 'programming', 'chinese', 'spelling']
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单词之间的关系

man_idx = word_to_idx["man"] 
king_idx = word_to_idx["king"] 
woman_idx = word_to_idx["woman"]
embedding = embedding_weights[woman_idx] - embedding_weights[man_idx] + embedding_weights[king_idx]
cos_dis = np.array([scipy.spatial.distance.cosine(e, embedding) for e in embedding_weights])
for i in cos_dis.argsort()[:20]:
    print(idx_to_word[i])
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king
henry
charles
pope
queen
iii
prince
elizabeth
alexander
constantine
edward
son
iv
louis
emperor
mary
james
joseph
frederick
francis
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