【Pytorch】学习记录分享10——TextCNN用于文本分类处理

1. TextCNN用于文本分类

具体流程：

2. 代码实现

# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as npclass Config(object):"""配置参数"""def __init__(self, dataset, embedding):self.model_name = 'TextCNN'self.train_path = dataset + '/data/train.txt'                                # 训练集self.dev_path = dataset + '/data/dev.txt'                                    # 验证集self.test_path = dataset + '/data/test.txt'                                  # 测试集self.class_list = [x.strip() for x in open(dataset + '/data/class.txt').readlines()]                                # 类别名单self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果self.log_path = dataset + '/log/' + self.model_nameself.embedding_pretrained = torch.tensor(np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\if embedding != 'random' else None                                       # 预训练词向量self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备self.dropout = 0.5                                              # 随机失活self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练self.num_classes = len(self.class_list)                         # 类别数self.n_vocab = 0                                                # 词表大小，在运行时赋值self.num_epochs = 20                                            # epoch数self.batch_size = 128                                           # mini-batch大小self.pad_size = 32                                              # 每句话处理成的长度(短填长切)self.learning_rate = 1e-3                                       # 学习率self.embed = self.embedding_pretrained.size(1)\if self.embedding_pretrained is not None else 300           # 字向量维度self.filter_sizes = (2, 3, 4)                                   # 卷积核尺寸self.num_filters = 256                                          # 卷积核数量(channels数)'''Convolutional Neural Networks for Sentence Classification'''class Model(nn.Module):def __init__(self, config):super(Model, self).__init__()if config.embedding_pretrained is not None:self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)else:self.embedding = nn.Embedding(config.n_vocab, config.embed, padding_idx=config.n_vocab - 1)self.convs = nn.ModuleList([nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])self.dropout = nn.Dropout(config.dropout)self.fc = nn.Linear(config.num_filters * len(config.filter_sizes), config.num_classes)def conv_and_pool(self, x, conv):x = F.relu(conv(x)).squeeze(3)x = F.max_pool1d(x, x.size(2)).squeeze(2)return xdef forward(self, x):#print (x[0].shape)out = self.embedding(x[0])out = out.unsqueeze(1)out = torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)out = self.dropout(out)out = self.fc(out)return out

该代码对应上述的图像中的模块实现，CNN用于处理文本数据