NLP之Bi-LSTM(在长句中预测下一个单词)

Bi-LSTM

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目录

• Bi-LSTM
  • 1.理论
    • 1.1 基本模型
    • 1.2 Bi-LSTM的特点
  • 2.实验
    • 2.1 实验步骤
    • 2.2 实验模型

1.理论

1.1 基本模型

Bi-LSTM模型分为2个独立的LSTM,输入序列分别以正序和逆序输入至2个LSTM模型进行特征提取,将2个输出向量进行拼接后形成的词向量作为该词的最终特征表达(因此底层维度是普通LSTM隐藏层维度的两倍)

1.2 Bi-LSTM的特点

Bi-LSTM的模型设计理念是使t时刻所获得特征数据同时拥有过去和将来之间的信息

• 实验证明,Bi-LSTM模型对文本特征提取效率和性能要优于单个LSTM结构模型
• Bi-LSTM中的2个LSTM参数是相互独立的,它们只共享训练集的word-embedding词向量列表等训练集基本信息。

2.实验

2.1 实验步骤

1. 数据预处理,得到字典、样本数等基本数据
2. 构建Bi-LSTM模型,设置输入模型的嵌入向量维度,隐藏层α向量和记忆细胞c的维度
3. 训练
   1. 代入数据,设置每个样本的时间步长度
   2. 得到模型输出值,取其中最大值的索引,找到字典中对应的字母,即为模型预测的下一个字母.
   3. 把模型输出值和真实值相比,求得误差损失函数,运用Adam动量法梯度下降
4. 测试

2.2 实验模型

本实验句子长度70,训练长度70-1=69,即时间步长度n=69,使用了69个Bi-LSTM细胞单元

"""
Task: 基于Bi-LSTM的长句单词预测
Author: ChengJunkai @github.com/Cheng0829
Email: chengjunkai829@gmail.com
Date: 2022/09/09
"""

import numpy as np
import torch, os, sys, time, re
import torch.nn as nn
import torch.optim as optim

'''1.数据预处理'''
def pre_process(sentence):
    # sentence = re.sub("[.,!?\\-]", '', sentence.lower()).split(' ')
    word_list = []
    '''
    如果用list(set(word_sequence))来去重,得到的将是一个随机顺序的列表(因为set无序),
    这样得到的字典不同,保存的上一次训练的模型很有可能在这一次不能用
    (比如上一次的模型预测碰见a:0,b:1,就输出c:2,但这次模型c在字典3号位置,也就无法输出正确结果)
    '''
    for word in sentence.split():
        if word not in word_list:
            word_list.append(word)

    word_dict = {w:i for i, w in enumerate(word_list)}
    number_dict = {i:w for i, w in enumerate(word_list)}
    print(word_dict)
    word_dict["''"] = len(word_dict)
    number_dict[len(number_dict)] = "''"
    n_class = len(word_dict) # 词库大小:48
    max_len = len(sentence.split()) # 句子长度:70
    # print(max_len)
    return sentence, word_dict, number_dict, n_class, max_len

'''根据句子数据,构建词元的嵌入向量及目标词索引'''
def make_batch(sentence):
    input_batch = []
    target_batch = []
    input_print = []
    words = sentence.split()
    for i, word in enumerate(words[:-1]):
        input = [word_dict[n] for n in words[:(i+1)]]
        input = input + [0] * (max_len - 1 - len(input))
        # print(np.array(input).shape) # (69,)
        target = word_dict[words[i+1]]
        '''
        input_batch:
            由于要预测长句的每一个位置的单词,
            所以除了最后一个单词只被预测之外,
            所有单词都要参与预测.
            因此,训练样本数为:句子长度70-1=69
        target_batch:
            一个列表,分别存储69个训练样本的目标单词
        '''
        input_print.append(input)
        # np.eye(n_class)[input] : [69,48]
        # print(np.eye(n_class)[input].shape)
        input_batch.append(np.eye(n_class)[input])
        target_batch.append(target)
    # print(np.array(input_print)
    '''input_print: [69,69]'''
    '''input_batch: [69,69,48]'''
    input_batch = torch.FloatTensor(np.array(input_batch))
    # print(input_batch.shape)
    target_batch = torch.LongTensor(np.array(target_batch)) #(69,)
    input_batch = input_batch.to(device)
    target_batch = target_batch.to(device)
    return input_batch, target_batch

'''2.构建模型'''
class BiLSTM(nn.Module):
    def __init__(self):
        super(BiLSTM, self).__init__()
        # n_class是词库大小,即嵌入向量维度:48
        '''bidirectional=True'''
        self.lstm = nn.LSTM(input_size=n_class, hidden_size=hidden_size, bidirectional=True)
        self.W = nn.Linear(hidden_size*2, n_class, bias=False)
        self.b = nn.Parameter(torch.ones(n_class))

    def forward(self, X):
        '''训练样本数:69, 时间步长度(每一样本长度):69'''
        '''X:[batch_size, n_step, n_class] [样本数,时间步长度(每一样本长度),嵌入向量维度(词库大小)]'''
        # input : [n_step, batch_size, n_class]
        '''transpose转置 -> input:[69,69,48]'''
        # input : [输入序列长度(时间步长度),样本数,嵌入向量维度]
        input = X.transpose(0, 1) # [69,69,48]
        # hidden_state : [num_layers*num_directions, batch_size, hidden_size]
        # hidden_state : [层数*网络方向,样本数,隐藏层的维度(隐藏层神经元个数)]
        hidden_state = torch.zeros(1*2, len(X), hidden_size).to(device)
        # cell_state : [num_layers*num_directions, batch_size, hidden_size]
        # cell_state : [层数*网络方向,样本数,隐藏层的维度(隐藏层神经元个数)]
        cell_state = torch.zeros(1*2, len(X), hidden_size).to(device)
        '''
        一个Bi-LSTM细胞单元有三个输入,分别是$输入向量x^{<t>}、隐藏层向量a^{<t-1>}
        和记忆细胞c^{<t-1>}$;
        '''
        '''outputs:[时间步长度(每一样本长度),训练样本数,隐藏层向量维度*2] -> [69,69,256]'''
        # outputs:[69,69,256] final_hidden_state:[2,69,128] final_cell_state:[2,69,128]
        outputs, (final_hidden_state, final_cell_state) = self.lstm(input, (hidden_state, cell_state))
        outputs = outputs.to(device)
        '''
        由于是双向,outputs中各个值是由每一步的两个output拼接而成的,所以维度=2*128=256
        final_hidden_state只有final_output的一半参数,所以不能替换
        '''
        final_output = outputs[-1] # [batch_size, hidden_size*2] -> [69, 256]
        Y_t = self.W(final_output) + self.b  # Y_t : [batch_size, n_class]
        return Y_t

if __name__ == '__main__':
    hidden_size = 128 # 隐藏层神经元个数(向量维度)
    device = ['cuda:0' if torch.cuda.is_available() else 'cpu'][0]
    sentence = (
        'China is one of the four ancient civilizations in the world. '
        'Around 5800 years ago,  Yellow River, the middle and lower reaches of Yangtze River, '
        'and the West Liaohe River showed signs of origin of civilization; '
        'around 5,300 years ago, various regions of China entered the stage of civilization; '
        'around 3,800 years ago, Central Plains formed a more advanced stage. '
        'Mature form of civilization, and radiate cultural influence to Quartet;'
    )
    # 长句训练太麻烦,所以改用字母
    sentence = 'a b c d e f g h i j k l m n o p q r s t u v w x y z'
    # sentence = 'a b c d e f g h i'
    '''1.数据预处理'''
    sentence, word_dict, number_dict, n_class, max_len = pre_process(sentence)
    input_batch, target_batch = make_batch(sentence)

    '''2.构建模型'''
    '''模型加载'''
    model = BiLSTM()
    model.to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.0001)

    if os.path.exists('model_param.pt') == True:
        # 加载模型参数到模型结构
        model.load_state_dict(torch.load('model_param.pt', map_location=device))

    '''3.训练'''
    print('{}\nTrain\n{}'.format('*'*30, '*'*30))
    loss_record = []
    for epoch in range(10000):
        optimizer.zero_grad()
        output = model(input_batch)
        '''output:[25,27] target_batch:[25]'''
        loss = criterion(output, target_batch)
        loss.backward()
        optimizer.step()
        if loss >= 0.01: # 连续30轮loss小于0.01则提前结束训练
            loss_record = []
        else:
            loss_record.append(loss.item())
            if len(loss_record) == 30:
                torch.save(model.state_dict(), 'model_param.pt')
                break     

        if ((epoch+1) % 1000 == 0):
            print('Epoch:', '%04d' % (epoch + 1), 'Loss = {:.6f}'.format(loss))
            torch.save(model.state_dict(), 'model_param.pt')

    '''4.测试'''
    '''
    本实验与之前实验的不同之处在于,把句子单词挨个进行分解,所以看似只有一个样本,
    实际有max_len-1个样本,也就是说训练时预测了从首单词到尾单词前的所有单词,
    所以输入"a"到输入"a~y"均可输出"a~z"

    但由于样本少且高度相似,所以必须按照训练样本的位置进行预测,
    因为权重训练的是如何由"a"推出"b",如何由"a b"推出"a b c"......
    如果开始单词改成"b",则预测结果不会是"c"
    '''
    print('{}\nTest\n{}'.format('*'*30, '*'*30))
    sentence = 'a b c'
    print(sentence)
    length = 10
    while len(sentence.split()) < length:
        words = sentence.split()
        input_batch = []
        input = []
        # 把单词换成序号
        for word in words:
            if word not in word_dict:
                word = "''" # 把不存在赋值为空字符串
            input.append(word_dict[word])
        # 填充
        input = input + [0] * (max_len - 1 - len(input))
        input_batch.append(np.eye(n_class)[input])
        input_batch = torch.FloatTensor(np.array(input_batch))
        input_batch = input_batch.to(device)
        predict = model(input_batch).data.max(1, keepdim=True)[1]
        sentence = sentence + ' ' + number_dict[predict.item()]
        print(sentence)