Pytorch框架下应用Bi-LSTM实现汽车评论文本关键词抽取_LSTM实现

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Pytorch框架下应用框架下应用Bi-LSTM实现汽车评论文本关键词抽取实现汽车评论文本关键词抽取

需要调用的模块及整体Bi-lstm流程

import torch

import pandas as pd

import numpy as np

from tensorflow import keras

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

from torch.utils.data import DataLoader

from torch.utils.data import TensorDataset

import gensim

from sklearn.model_selection import train_test_split

class word_extract(nn.Module):

def __init__(self,d_model,embedding_matrix):

super(word_extract, self).__init__()

self.d_model=d_model

self.embedding=nn.Embedding(num_embeddings=len(embedding_matrix),embedding_dim=200)

self.embedding.weight.data.copy_(embedding_matrix)

self.embedding.weight.requires_grad=False

self.lstm1=nn.LSTM(input_size=200,hidden_size=50,bidirectional=True)

self.lstm2=nn.LSTM(input_size=2*self.lstm1.hidden_size,hidden_size=50,bidirectional=True)

self.linear=nn.Linear(2*self.lstm2.hidden_size,4)

def forward(self,x):

w_x=self.embedding(x)

first_x,(first_h_x,first_c_x)=self.lstm1(w_x)

second_x,(second_h_x,second_c_x)=self.lstm2(first_x)

output_x=self.linear(second_x)

return output_x

将文本转换为数值形式

def trans_num(word2idx,text):

text_list=[] for i in text:

s=i.rstrip().replace('','').replace('','').split(' ')

numtext=[word2idx[j] if j in word2idx.keys() else word2idx['_PAD'] for j in s ] text_list.append(numtext)

return text_list

将Gensim里的词向量模型转为矩阵形式，后续导入到LSTM模型中

def establish_word2vec_matrix(model): #负责将数值索引转为要输入的数据

word2idx = {"_PAD": 0} # 初始化 `[word : token]` 字典，后期 tokenize 语料库就是用该词典。

num2idx = {0: "_PAD"}

vocab_list = [(k, model.wv[k]) for k, v in model.wv.vocab.items()]

# 存储所有 word2vec 中所有向量的数组，留意其中多一位，词向量全为 0，用于 padding

embeddings_matrix = np.zeros((len(model.wv.vocab.items()) + 1, model.vector_size))

for i in range(len(vocab_list)):

word = vocab_list[i][0] word2idx[word] = i + 1

num2idx[i + 1] = word

embeddings_matrix[i + 1] = vocab_list[i][1] embeddings_matrix = torch.Tensor(embeddings_matrix)

return embeddings_matrix, word2idx, num2idx

训练过程

def train(model,epoch,learning_rate,batch_size,x, y, val_x, val_y):

optimizor = optim.Adam(model.parameters(), lr=learning_rate)

data = TensorDataset(x, y)

data = DataLoader(data, batch_size=batch_size)

for i in range(epoch):

for j, (per_x, per_y) in enumerate(data):

output_y = model(per_x)

loss = F.cross_entropy(output_y.view(-1,output_y.size(2)), per_y.view(-1))

optimizor.zero_grad()

loss.backward()

optimizor.step()

arg_y=output_y.argmax(dim=2)

fit_correct=(arg_y==per_y).sum()

fit_acc=fit_correct.item()/(per_y.size(0)*per_y.size(1))

print('##################################')

print('第{}次迭代第{}批次的训练误差为{}'.format(i + 1, j + 1, loss), end=' ')

print('第{}次迭代第{}批次的训练准确度为{}'.format(i + 1, j + 1, fit_acc))

val_output_y = model(val_x)

val_loss = F.cross_entropy(val_output_y.view(-1,val_output_y.size(2)), val_y.view(-1))

arg_val_y=val_output_y.argmax(dim=2)

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