c 网站开发教程,售后好的品牌策划公司,石狮app网站开发,金融电子商务网站建设论文链接#xff1a;https://arxiv.org/pdf/1408.5882.pdf TextCNN 是一种用于文本分类的卷积神经网络模型。它在卷积神经网络的基础上进行了一些修改#xff0c;以适应文本数据的特点。 TextCNN 的主要思想是使用一维卷积层来提取文本中的局部特征#xff0c;并通过池化操…论文链接https://arxiv.org/pdf/1408.5882.pdf TextCNN 是一种用于文本分类的卷积神经网络模型。它在卷积神经网络的基础上进行了一些修改以适应文本数据的特点。 TextCNN 的主要思想是使用一维卷积层来提取文本中的局部特征并通过池化操作来减少特征的维度。这些局部特征可以捕获词语之间的关系和重要性从而帮助模型进行分类。 nn.Conv2d nn.Conv2d 的构造函数包含以下参数 in_channels输入数据的通道数。out_channels卷积核的数量也是输出数据的通道数。kernel_size卷积核的大小可以是一个整数或一个元组表示宽度和高度。stride卷积核的步幅可以是一个整数或一个元组表示水平和垂直方向的步幅。 nn.Conv2d(1, config.num_filters, (k, config.embed)) 输入通道是1 输出通道的维度 卷积核k, config.embed) 代码部分
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from torch.utils.data import Datasetfrom datetime import timedeltafrom sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
from collections import defaultdict
from torch.optim import AdamWdf pd.read_csv(./data/online_shopping_10_cats.csv)
UNK, PAD UNK, PAD # 未知字padding符号
RANDOM_SEED 2023file_path ./data/online_shopping_10_cats.csv
vocab_file ./data/vocab.pkl
emdedding_file ./data/embedding_SougouNews.npz
vocab pkl.load(open(vocab_file, rb))class MyDataSet(Dataset):def __init__(self, df, vocab,pad_sizeNone):self.data_info dfself.data_info[review] self.data_info[review].apply(lambda x:str(x).strip())self.data_info self.data_info[[review,label]].valuesself.vocab vocab self.pad_size pad_sizeself.buckets 250499 def biGramHash(self,sequence, t):t1 sequence[t - 1] if t - 1 0 else 0return (t1 * 14918087) % self.bucketsdef triGramHash(self,sequence, t):t1 sequence[t - 1] if t - 1 0 else 0t2 sequence[t - 2] if t - 2 0 else 0return (t2 * 14918087 * 18408749 t1 * 14918087) % self.bucketsdef __getitem__(self, item):result {}view, label self.data_info[item]result[view] view.strip()result[label] torch.tensor(label,dtypetorch.long)token [i for i in view.strip()]seq_len len(token)# 填充if self.pad_size:if len(token) self.pad_size:token.extend([PAD] * (self.pad_size - len(token)))else:token token[:self.pad_size]seq_len self.pad_sizeresult[seq_len] seq_len# 词表的转换words_line []for word in token:words_line.append(self.vocab.get(word, self.vocab.get(UNK)))result[input_ids] torch.tensor(words_line, dtypetorch.long) # bigram []trigram []for i in range(self.pad_size):bigram.append(self.biGramHash(words_line, i))trigram.append(self.triGramHash(words_line, i))result[bigram] torch.tensor(bigram, dtypetorch.long)result[trigram] torch.tensor(trigram, dtypetorch.long)return resultdef __len__(self):return len(self.data_info)#myDataset[0]
df_train, df_test train_test_split(df, test_size0.1, random_stateRANDOM_SEED)
df_val, df_test train_test_split(df_test, test_size0.5, random_stateRANDOM_SEED)
df_train.shape, df_val.shape, df_test.shape#((56496, 3), (3139, 3), (3139, 3))def create_data_loader(df,vocab,pad_size,batch_size4):ds MyDataSet(df,vocab,pad_sizepad_size)return DataLoader(ds,batch_sizebatch_size)MAX_LEN 256
BATCH_SIZE 4
train_data_loader create_data_loader(df_train,vocab,pad_sizeMAX_LEN, batch_sizeBATCH_SIZE)
val_data_loader create_data_loader(df_val,vocab,pad_sizeMAX_LEN, batch_sizeBATCH_SIZE)
test_data_loader create_data_loader(df_test,vocab,pad_sizeMAX_LEN, batch_sizeBATCH_SIZE)class Config(object):配置参数def __init__(self):self.model_name FastTextself.embedding_pretrained torch.tensor(np.load(./data/embedding_SougouNews.npz)[embeddings].astype(float32)) # 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 2 # 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.learning_rate 1e-4 # 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.hidden_size 256 # 隐藏层大小self.n_gram_vocab 250499 # ngram 词表大小self.filter_sizes [2,3,4]self.num_filters 256 # 卷积核数量(channels数)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, freezeFalse)else:self.embedding nn.Embedding(config.n_vocab, config.embed, padding_idxconfig.n_vocab - 1)self.convs nn.ModuleList([nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])# self.convs nn.ModuleList(# [nn.Conv1D(1, config.num_filters, k) 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):out self.embedding(x[input_ids])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 outconfig Config()
model Model(config)
sample next(iter(train_data_loader))device torch.device(cuda:0 if torch.cuda.is_available() else cpu)
model model.to(device)EPOCHS 5 # 训练轮数
optimizer AdamW(model.parameters(),lr2e-4)
total_steps len(train_data_loader) * EPOCHS
# schedule get_linear_schedule_with_warmup(optimizer,num_warmup_steps0,
# num_training_stepstotal_steps)
loss_fn nn.CrossEntropyLoss().to(device)def train_epoch(model,data_loader,loss_fn,device, optimizer,n_examples,scheduleNone):model model.train()losses []correct_predictions 0for d in tqdm(data_loader):# input_ids d[input_ids].to(device)# attention_mask d[attention_mask].to(device)targets d[label]#.to(device)outputs model(d)_,preds torch.max(outputs, dim1)loss loss_fn(outputs,targets)losses.append(loss.item())correct_predictions torch.sum(predstargets)loss.backward()nn.utils.clip_grad_norm_(model.parameters(), max_norm1.0)optimizer.step()#scheduler.step()optimizer.zero_grad()#break#print(n_examples)return correct_predictions.double().item() / n_examples, np.mean(losses)def eval_model(model, data_loader, loss_fn, device, n_examples):model model.eval() # 验证预测模式losses []correct_predictions 0with torch.no_grad():for d in data_loader:targets d[label]#.to(device)outputs model(d)_, preds torch.max(outputs, dim1)loss loss_fn(outputs, targets)correct_predictions torch.sum(preds targets)losses.append(loss.item())return correct_predictions.double() / n_examples, np.mean(losses)# train model
EPOCHS 10
history defaultdict(list) # 记录10轮loss和acc
best_accuracy 0for epoch in range(EPOCHS):print(fEpoch {epoch 1}/{EPOCHS})print(- * 10)train_acc, train_loss train_epoch(model,train_data_loader,loss_fn loss_fn,optimizeroptimizer,device device,n_exampleslen(df_train))print(fTrain loss {train_loss} accuracy {train_acc})val_acc, val_loss eval_model(model,val_data_loader,loss_fn,device,len(df_val))print(fVal loss {val_loss} accuracy {val_acc})print()history[train_acc].append(train_acc)history[train_loss].append(train_loss)history[val_acc].append(val_acc)history[val_loss].append(val_loss)if val_acc best_accuracy:torch.save(model.state_dict(), best_model_state.bin)best_accuracy val_acc
一维卷积模型,直接替换就行了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, freezeFalse)else:self.embedding nn.Embedding(config.n_vocab, config.embed, padding_idxconfig.n_vocab - 1)# self.convs nn.ModuleList(# [nn.Conv2d(1, config.num_filters, (k, config.embed)) for k in config.filter_sizes])self.convs nn.ModuleList([nn.Conv1d(MAX_LEN, config.num_filters, k) 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):#print(x.shape)x F.relu(conv(x))#.squeeze(3)#print(x.shape)x F.max_pool1d(x, x.size(2))#.squeeze(2)return xdef forward(self, x):out self.embedding(x[input_ids])#print(out.shape)#out out.unsqueeze(1)out torch.cat([self.conv_and_pool(out, conv) for conv in self.convs], 1)out out.squeeze(-1)#print(out.shape)out self.fc(out)return out Epoch 1/10
----------100%|█████████████████████████████████████| 14124/14124 [08:1900:00, 28.29it/s]Train loss 0.32963800023092527 accuracy 0.889903709997168
Val loss 0.2872631916414839 accuracy 0.9197196559413826Epoch 2/10
----------100%|█████████████████████████████████████| 14124/14124 [08:1900:00, 28.25it/s]Train loss 0.26778308933985917 accuracy 0.925392948173322
Val loss 0.29051536209677714 accuracy 0.9238611022618668Epoch 3/10
----------100%|█████████████████████████████████████| 14124/14124 [08:1700:00, 28.39it/s]Train loss 0.23998896145841375 accuracy 0.9368450863777966
Val loss 0.29530937147389363 accuracy 0.9238611022618668Epoch 4/10
----------100%|█████████████████████████████████████| 14124/14124 [08:2100:00, 28.14it/s]Train loss 0.21924698638110582 accuracy 0.9446863494760691
Val loss 0.3079132618505083 accuracy 0.9260911118190507Epoch 5/10
----------100%|█████████████████████████████████████| 14124/14124 [08:2100:00, 28.15it/s]Train loss 0.1976975509786261 accuracy 0.9515717926932881
Val loss 0.3294101043627459 accuracy 0.9267282574068174Epoch 6/10
----------100%|█████████████████████████████████████| 14124/14124 [08:1400:00, 28.56it/s]Train loss 0.18130036814091913 accuracy 0.9575899178702917
Val loss 0.34197808585767564 accuracy 0.9260911118190507Epoch 7/10
----------100%|█████████████████████████████████████| 14124/14124 [09:0300:00, 26.00it/s]Train loss 0.16165128718584662 accuracy 0.9624044180118947
Val loss 0.34806641904714486 accuracy 0.924816820643517
conv1D Epoch 1/10
----------100%|█████████████████████████████████████| 14124/14124 [04:5300:00, 48.14it/s]Train loss 0.4587948323856965 accuracy 0.7931711979609176
Val loss 0.3846700458902963 accuracy 0.8738451736221726Epoch 2/10
----------100%|█████████████████████████████████████| 14124/14124 [05:2100:00, 43.93it/s]Train loss 0.3450994613828836 accuracy 0.8979219767771169
Val loss 0.39124348195663816 accuracy 0.8932781140490602Epoch 3/10
----------100%|█████████████████████████████████████| 14124/14124 [05:1400:00, 44.93it/s]Train loss 0.3135276534462201 accuracy 0.9156046445766072
Val loss 0.38953639226077036 accuracy 0.9041095890410958Epoch 4/10
----------100%|█████████████████████████████████████| 14124/14124 [04:3200:00, 51.76it/s]Train loss 0.29076329547278607 accuracy 0.926224865477202
Val loss 0.4083191853780146 accuracy 0.9063395985982797Epoch 5/10
----------100%|█████████████████████████████████████| 14124/14124 [04:3300:00, 51.70it/s]Train loss 0.2712314691068196 accuracy 0.9351989521382045
Val loss 0.44957431750859633 accuracy 0.9063395985982797Epoch 6/10
----------100%|█████████████████████████████████████| 14124/14124 [04:2800:00, 52.56it/s]Train loss 0.2521194787317903 accuracy 0.9424561030869442
Val loss 0.4837963371119771 accuracy 0.9082510353615801Epoch 7/10
----------100%|█████████████████████████████████████| 14124/14124 [04:2800:00, 52.64it/s]Train loss 0.2317749120263705 accuracy 0.9494831492495044
Val loss 0.5409662437294889 accuracy 0.9063395985982797Epoch 8/10
----------100%|█████████████████████████████████████| 14124/14124 [04:2900:00, 52.39it/s]Train loss 0.2093608888886245 accuracy 0.9562269895213821
Val loss 0.5704389385299592 accuracy 0.9037910162472125Epoch 9/10
----------100%|█████████████████████████████████████| 14124/14124 [04:2800:00, 52.68it/s]Train loss 0.1867563983566425 accuracy 0.9619088077032002
Val loss 0.6150021497048127 accuracy 0.9015610066900287Epoch 10/10
----------100%|█████████████████████████████████████| 14124/14124 [04:2900:00, 52.45it/s]Train loss 0.16439846786478746 accuracy 0.9669003115264797
Val loss 0.6261858006026605 accuracy 0.9098438993309972 使用Conv2D 的效果比Conv1D的效果好。
最近在忙着打一个数据挖掘的比赛后续会持续输出请大家关注谢谢
