html网站后台管理系统,网站规划的特点,网站首页幻灯片不显示,dede手机网站跳转一共六个脚本,分别是: ①generateDictionary.py用于生成词典 ②datasets.py定义了数据集加载的方法 ③models.py定义了网络模型 ④configs.py配置一些参数 ⑤run_train.py训练模型 ⑥run_test.py测试模型
数据集https://download.csdn.net/download/Victor_Li_/88486959?spm1…一共六个脚本,分别是: ①generateDictionary.py用于生成词典 ②datasets.py定义了数据集加载的方法 ③models.py定义了网络模型 ④configs.py配置一些参数 ⑤run_train.py训练模型 ⑥run_test.py测试模型
数据集https://download.csdn.net/download/Victor_Li_/88486959?spm1001.2014.3001.5501停用词表https://download.csdn.net/download/Victor_Li_/88486973?spm1001.2014.3001.5501
generateDictionary.py如下
import jiebadata_path ./weibo_senti_100k.csv
data_stop_path ./hit_stopwords.txt
data_list open(data_path,encodingutf-8).readlines()[1:]
stops_word open(data_stop_path,encodingutf-8).readlines()
stops_word [line.strip() for line in stops_word]
stops_word.append( )
stops_word.append(\n)voc_dict {}
min_seq 1
top_n 1000
UNK UNK
PAD PAD
for item in data_list:label item[0]content item[2:].strip()seg_list jieba.cut(content,cut_allFalse)seg_res []for seg_item in seg_list:if seg_item in stops_word:continueseg_res.append(seg_item)if seg_item in voc_dict.keys():voc_dict[seg_item] 1else:voc_dict[seg_item] 1# print(content)# print(seg_res)voc_list sorted([_ for _ in voc_dict.items() if _[1] min_seq],keylambda x:x[1],reverseTrue)[:top_n]voc_dict {word_count[0]:idx for idx,word_count in enumerate(voc_list)}voc_dict.update({UNK:len(voc_dict),PAD:len(voc_dict)1})ff open(./dict,w)
for item in voc_dict.keys():ff.writelines({},{}\n.format(item,voc_dict[item]))
ff.close()
datasets.py如下
from torch.utils.data import Dataset, DataLoader
import jieba
import numpy as npdef read_dict(voc_dict_path):voc_dict {}with open(voc_dict_path, r) as f:for line in f:line line.strip()if line :continueword, index line.split(,)voc_dict[word] int(index)return voc_dictdef load_data(data_path, data_stop_path,isTest):data_list open(data_path, encodingutf-8).readlines()[1:]stops_word open(data_stop_path, encodingutf-8).readlines()stops_word [line.strip() for line in stops_word]stops_word.append( )stops_word.append(\n)voc_dict {}data []max_len_seq 0for item in data_list:label item[0]content item[2:].strip()seg_list jieba.cut(content, cut_allFalse)seg_res []for seg_item in seg_list:if seg_item in stops_word:continueseg_res.append(seg_item)if seg_item in voc_dict.keys():voc_dict[seg_item] 1else:voc_dict[seg_item] 1if len(seg_res) max_len_seq:max_len_seq len(seg_res)if isTest:data.append([label, seg_res,content])else:data.append([label, seg_res])return data, max_len_seqclass text_ClS(Dataset):def __init__(self, data_path, data_stop_path,voc_dict_path,isTestFalse):self.isTest isTestself.data_path data_pathself.data_stop_path data_stop_pathself.voc_dict read_dict(voc_dict_path)self.data, self.max_len_seq load_data(self.data_path, self.data_stop_path,isTest)np.random.shuffle(self.data)def __len__(self):return len(self.data)def __getitem__(self, item):data self.data[item]label int(data[0])word_list data[1]if self.isTest:content data[2]input_idx []for word in word_list:if word in self.voc_dict.keys():input_idx.append(self.voc_dict[word])else:input_idx.append(self.voc_dict[UNK])if len(input_idx) self.max_len_seq:input_idx [self.voc_dict[PAD] for _ in range(self.max_len_seq - len(input_idx))]data np.array(input_idx)if self.isTest:return label,data,contentelse:return label, datadef data_loader(dataset,config):return DataLoader(dataset,batch_sizeconfig.batch_size,shuffleconfig.is_shuffle,num_workers4,pin_memoryTrue)
models.py如下
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as npclass Model(nn.Module):def __init__(self,config):super(Model,self).__init__()self.embeding nn.Embedding(config.n_vocab,config.embed_size,padding_idxconfig.n_vocab - 1)self.lstm nn.LSTM(config.embed_size,config.hidden_size,config.num_layers,batch_firstTrue,bidirectionalTrue,dropoutconfig.dropout)self.maxpool nn.MaxPool1d(config.pad_size)self.fc nn.Linear(config.hidden_size * 2 config.embed_size,config.num_classes)self.softmax nn.Softmax(dim1)def forward(self,x):embed self.embeding(x)out, _ self.lstm(embed)out torch.cat((embed, out), 2)out F.relu(out)out out.permute(0, 2, 1)out self.maxpool(out).reshape(out.size()[0],-1)out self.fc(out)out self.softmax(out)return outconfigs.py如下
import torch.typesclass Config():def __init__(self):self.n_vocab 1002self.embed_size 256self.hidden_size 256self.num_layers 5self.dropout 0.8self.num_classes 2self.pad_size 32self.batch_size 32self.is_shuffle Trueself.learning_rate 0.001self.num_epochs 100self.devices torch.device(cuda if torch.cuda.is_available() else cpu)
run_train.py如下
import torch
import torch.nn as nn
from torch import optim
from models import Model
from datasets import data_loader,text_ClS
from configs import Config
import time
import torch.multiprocessing as mpif __name__ __main__:mp.freeze_support()cfg Config()data_path ./weibo_senti_100k.csvdata_stop_path ./hit_stopwords.txtdict_path ./dictdataset text_ClS(data_path, data_stop_path, dict_path)train_dataloader data_loader(dataset,cfg)cfg.pad_size dataset.max_len_seqmodel_text_cls Model(cfg)model_text_cls.to(cfg.devices)loss_func nn.CrossEntropyLoss()optimizer optim.Adam(model_text_cls.parameters(), lrcfg.learning_rate)scheduler optim.lr_scheduler.StepLR(optimizer, step_size1, gamma0.9)for epoch in range(cfg.num_epochs):running_loss 0correct 0total 0epoch_start_time time.time()for i,(labels,datas) in enumerate(train_dataloader):datas datas.to(cfg.devices)labels labels.to(cfg.devices)pred model_text_cls.forward(datas)loss_val loss_func(pred,labels)running_loss loss_val.item()loss_val.backward()if ((i 1) % 4 0) or (i 1 len(train_dataloader)):optimizer.step()optimizer.zero_grad()_, predicted torch.max(pred.data, 1)correct (predicted labels).sum().item()total labels.size(0)scheduler.step()accuracy_train 100 * correct / totalepoch_end_time time.time()epoch_time epoch_end_time - epoch_start_timetain_loss running_loss / len(train_dataloader)print(Epoch [{}/{}],Time: {:.4f}s,Loss: {:.4f},Acc: {:.2f}%.format(epoch 1, cfg.num_epochs, epoch_time, tain_loss,accuracy_train))torch.save(model_text_cls.state_dict(),./text_cls_model/text_cls_model{}.pth.format(epoch))
run_test.py如下
import torch
import torch.nn as nn
from torch import optim
from models import Model
from datasets import data_loader,text_ClS
from configs import Config
import time
import torch.multiprocessing as mpif __name__ __main__:mp.freeze_support()cfg Config()data_path ./test.csvdata_stop_path ./hit_stopwords.txtdict_path ./dictcfg.batch_size 1dataset text_ClS(data_path, data_stop_path, dict_path,isTestTrue)dataloader data_loader(dataset,cfg)cfg.pad_size dataset.max_len_seqmodel_text_cls Model(cfg)model_text_cls.load_state_dict(torch.load(./text_cls_model/text_cls_model0.pth))model_text_cls.to(cfg.devices)classes_name [负面的,正面的]for i,(label,input,content) in enumerate(dataloader):label label.to(cfg.devices)input input.to(cfg.devices)pred model_text_cls.forward(input)_, predicted torch.max(pred.data, 1)print(内容:{}, 实际结果:{}, 预测结果:{}.format(content,classes_name[label],classes_name[predicted[0]]))测试结果如下
