【Pytorch】学习记录分享9——新闻数据集文本分类任务实战

【Pytorch】学习记录分享9——PyTorch新闻数据集文本分类任务

- - 1. 认为主流程code
  - 2. NLP 对话和预测基本均属于分类任务详细见
  - 3. Tensorborad

1. 认为主流程code

import time
import torch
import numpy as np
from train_eval import train, init_network
from importlib import import_module
import argparse
from tensorboardX import SummaryWriter###制定参数 --model TextRNN
parser = argparse.ArgumentParser(description='Chinese Text Classification')
parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN, TextRNN, FastText, TextRCNN, TextRNN_Att, DPCNN, Transformer')
parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained')
parser.add_argument('--word', default=False, type=bool, help='True for word, False for char')
args = parser.parse_args()if __name__ == '__main__':dataset = 'THUCNews'  # 数据集# 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:randomembedding = 'embedding_SougouNews.npz'if args.embedding == 'random':embedding = 'random'model_name = args.model  #TextCNN, TextRNN,if model_name == 'FastText':from utils_fasttext import build_dataset, build_iterator, get_time_difembedding = 'random'else:from utils import build_dataset, build_iterator, get_time_difx = import_module('models.' + model_name)config = x.Config(dataset, embedding)np.random.seed(1)torch.manual_seed(1)torch.cuda.manual_seed_all(1)torch.backends.cudnn.deterministic = True  # 保证每次结果一样start_time = time.time()print("Loading data...")vocab, train_data, dev_data, test_data = build_dataset(config, args.word)train_iter = build_iterator(train_data, config)dev_iter = build_iterator(dev_data, config)test_iter = build_iterator(test_data, config)time_dif = get_time_dif(start_time)print("Time usage:", time_dif)# trainconfig.n_vocab = len(vocab)model = x.Model(config).to(config.device)writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))if model_name != 'Transformer':init_network(model)print(model.parameters)train(config, model, train_iter, dev_iter, test_iter,writer)

RNN


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.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,bidirectional=True, batch_first=True, dropout=config.dropout)self.fc = nn.Linear(config.hidden_size * 2, config.num_classes)def forward(self, x):x, _ = xout = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]out, _ = self.lstm(out)out = self.fc(out[:, -1, :])  # 句子最后时刻的 hidden statereturn out