自然语言处理入门练习（二）：基于深度学习的文本分类及实战（附代码）_深度学习 自然语言处理 题目

自然语言处理入门练习（二）：基于深度学习的文本分类及实战（附代码）

任务要求

  熟悉Pytorch，用Pytorch重写《任务一》，实现CNN、RNN的文本分类。

知识点总结

  参考自然语言处理入门练习（一）：基于机器学习的文本分类及实站（附代码）

数据

数据集采用gaussic的数据集，https://github.com/gaussic/text-classification-cnn-rnn
链接: https://pan.baidu.com/s/1hugrfRu 密码: qfud
下载所有文件，放到cnews目录下即可

运行

  训练：python torch_train.py
  测试：python torch_test.py

【核心代码】

import torch
from torch import nn
import numpy as np
from torch.autograd import Variable
import torch.nn.functional as F

class TextRNN(nn.Module):
    """文本分类，RNN模型"""

    def __init__(self):
        super(TextRNN, self).__init__()
        # 三个待输入的数据
        self.embedding = nn.Embedding(5000, 64)  # 进行词嵌入
        self.rnn = nn.LSTM(input_size=64, hidden_size=128, bidirectional=True)
        # self.rnn = nn.GRU(input_size=64, hidden_size=128, num_layers=2, bidirectional=True)
        self.f1 = nn.Sequential(nn.Linear(256, 10),
                                nn.Softmax())

    def forward(self, x):
        x = self.embedding(x) # batch_size x text_len x embedding_size 64*600*64
        x= x.permute(1, 0, 2) # text_len x batch_size x embedding_size 600*64*64
        x, (h_n, c_n)= self.rnn(x) #x为600*64*256, h_n为2*64*128 lstm_out       Sentence_length * Batch_size * (hidden_layers * 2 [bio-direct]) h_n           （num_layers * 2） * Batch_size * hidden_layers
        final_feature_map = F.dropout(h_n, 0.8)
        feature_map = torch.cat([final_feature_map[i, :, :] for i in range(final_feature_map.shape[0])], dim=1) #64*256 Batch_size * (hidden_size * hidden_layers * 2)
        final_out = self.f1(feature_map) #64*10 batch_size * class_num
        return final_out

class TextCNN(nn.Module):
    def __init__(self):
        super(TextCNN, self).__init__()
        self.embedding = nn.Embedding(5000, 64)
        self.conv = nn.Sequential(nn.Conv1d(in_channels=64,
                                        out_channels=256,
                                        kernel_size=5),
                              nn.ReLU(),
                              nn.MaxPool1d(kernel_size=596))

        self.f1 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.embedding(x) # batch_size x text_len x embedding_size 64*600*64
        x = x.permute(0, 2, 1) #64*64*600

        x = self.conv(x)  #Conv1后64*256*596,ReLU后不变,NaxPool1d后64*256*1

        x = x.view(-1, x.size(1)) #64*256
        x = F.dropout(x, 0.8)
        x = self.f1(x)    #64*10 batch_size * class_num
        return x

if __name__ == '__main__':
    net = TextRNN()
    print(net)
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【完整代码github地址】

https://github.com/chenlian-zhou/nlp/tree/master/nlp_induction_training/task2

【参考资料】

1 论文：Convolutional Neural Networks for Sentence Classification
2 论文Convolutional Neural Networks for Sentence Classification的翻译版
3 https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/
4. https://github.com/FudanNLP/nlp-beginner