Pytorch文本分类入门

🍨 本文为[🔗365天深度学习训练营学习记录博客

🍦 参考文章：365天深度学习训练营

🍖 原作者：[K同学啊 | 接辅导、项目定制]\n🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb45)

一、加载数据

import os
import sys
import PIL
from PIL import Image
import time
import copy
import random
import pathlib
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchtext.datasets import AG_NEWS
import torchvision
from torchinfo import summary
import torchsummary
import matplotlib.pyplot as plt
import numpy as np
import warnings''' 下载或读取AG News数据集中的训练集与测试集 '''
def getDataset(root, dataset):if not os.path.exists(root) or not os.path.isdir(root):os.makedirs(root)if not os.path.exists(dataset) or not os.path.isdir(dataset):print('Downloading dataset...\n')# 下载AG News数据集 直接运行会报网络错误 无法下载  train_ds, test_ds = AG_NEWS(root=root, split=("train", "test"))else:print('Dataset already downloaded, reading...\n')# 读取本地AG News数据集 手动下载了train.csv和test.csv后可从本地加载数据train_ds, test_ds = AG_NEWS(root=dataset, split=("train", "test"))#print("Train:", next(train_ds), len(list(train_ds))+1)#print("Test :", next(test_ds), len(list(test_ds))+1)return train_ds, test_ds''' 设置GPU '''
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using {} device\n".format(device))
''' 加载数据 '''
root = './data/'
data_dir = os.path.join(root, 'AG_NEWS.data')
train_ds, test_ds = getDataset(root, data_dir)

运行结果：

Using cuda deviceDataset already downloaded, reading...Train: (3, "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.") 120000
Test : (3, "Fears for T N pension after talks Unions representing workers at Turner   Newall say they are 'disappointed' after talks with stricken parent firm Federal Mogul.") 7600

二、构建词典

''' 构建词典 '''
def buildDict(train_ds):tokenizer  = get_tokenizer('basic_english') # 返回分词器函数def yield_tokens(data_iter):for _, text in data_iter:yield tokenizer(text)vocab = build_vocab_from_iterator(yield_tokens(train_ds))text_pipeline  = lambda x: vocab.lookup_indices(tokenizer(x))label_pipeline = lambda x: int(x)#print(vocab.UNK, vocab._default_unk_index())# 打印默认索引，如果找不到单词，则会选择默认索引#print(vocab.lookup_indices(['here', 'is', 'an', 'example']))#print(text_pipeline('here is the an example'))#print(label_pipeline('10'))return vocab, text_pipeline, label_pipeline# 构建词典
text_pipeline, label_pipeline = buildDict(train_ds)

运行结果：

120001lines [00:04, 27817.88lines/s]
<unk> 0
[471, 22, 31, 5177]
[471, 22, 3, 31, 5177]
10

三、生成数据批次和迭代器

''' 加载数据，并设置batch_size '''
def loadData(train_ds, test_ds, batch_size=8, device='cpu'):# 构建词典vocab, text_pipeline, label_pipeline = buildDict(train_ds)# 生成数据批次和迭代器def collate_batch(batch):label_list, text_list, offsets = [], [], [0]for (_label, _text) in batch:# 标签列表label_list.append(label_pipeline(_label))# 文本列表processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)text_list.append(processed_text)# 偏移量，即语句的总词汇量offsets.append(processed_text.size(0))label_list = torch.tensor(label_list, dtype=torch.int64)text_list  = torch.cat(text_list)offsets    = torch.tensor(offsets[:-1]).cumsum(dim=0) #返回维度dim中输入元素的累计和return label_list.to(device), text_list.to(device), offsets.to(device)# 从 train_ds 加载训练集train_dl = torch.utils.data.DataLoader(train_ds,batch_size=batch_size,shuffle=False,collate_fn=collate_batch,num_workers=0)# 从 test_ds 加载测试集test_dl  = torch.utils.data.DataLoader(test_ds,batch_size=batch_size,shuffle=False,collate_fn=collate_batch,num_workers=0)# 取一个批次查看数据格式#data = train_dl.__iter__()#print(type(data), data, '\n')return vocab, train_dl, test_dl# 生成数据批次和迭代器
batch_size = 64
train_dl, test_dl = loadData(train_ds, test_ds, batch_size=batch_size, device=device)

运行结果：

120001lines [00:04, 27749.13lines/s]
<class 'torch.utils.data.dataloader._SingleProcessDataLoaderIter'> <torch.utils.data.dataloader._SingleProcessDataLoaderIter object at 0x00000266556204C0>

四、构建模型

class TextClassificationModel(nn.Module):def __init__(self, vocab_size, embed_dim, num_class):super(TextClassificationModel, self).__init__()self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)self.fc = nn.Linear(embed_dim, num_class)self.init_weights()def init_weights(self):initrange = 0.5self.embedding.weight.data.uniform_(-initrange, initrange)      # 将tensor用从均匀分布中抽样得到的值填充self.fc.weight.data.uniform_(-initrange, initrange)self.fc.bias.data.zero_()def forward(self, text, offsets):embedded = self.embedding(text, offsets)        # torch.Size([64, 64])output = self.fc(embedded)      # torch.Size([64, 4])return output
''' 定义实例 '''
train_iter = AG_NEWS(root='./data/AG_NEWS.data', split=("train"))
num_class  = len(set([label for (label, text) in train_iter]))
vocab_size = len(vocab)
em_size    = 64
model      = TextClassificationModel(vocab_size, em_size, num_class).to(device)
print('num_class', num_class)
print('vocab_size', vocab_size)
print(model)
def train(dataloader):model.train()       # 训练模式total_acc, total_count = 0, 0log_interval = 500start_time = time.time()for idx, (label, text, offsets) in enumerate(dataloader):optimizer.zero_grad()predited_label = model(text, offsets)loss = criterion(predited_label, label)loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)     # 规定了最大不能超过的max_normoptimizer.step()total_acc += (predited_label.argmax(1) == label).sum().item()total_count += label.size(0)if idx % log_interval == 0 and idx > 0:elapsed = time.time() - start_timeprint('| epoch {:3d} | {:5d}/{:5d} batches, accuracy {:8.3f}'.format(epoch, idx, len(dataloader), total_acc / total_count))total_acc, total_count = 0, 0start_time = time.time()
def evaluate(dataloader):model.eval()total_acc, total_count = 0, 0with torch.no_grad():for idx, (label, text, offsets) in enumerate(dataloader):predited_label = model(text, offsets)# loss = criterion(predited_label, label)total_acc += (predited_label.argmax(1) == label).sum().item()total_count += label.size(0)return total_acc / total_count

五、拆分数据集和运行模型

if __name__ == '__main__':# 超参数(Hyperparameters)EPOCHS = 10  # epochLR = 5  # learning rateBATCH_SIZE = 64  # batch size for trainingcriterion = torch.nn.CrossEntropyLoss()optimizer = torch.optim.SGD(model.parameters(), lr=LR)scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)total_accu = Nonetrain_iter, test_iter = AG_NEWS(root=path)train_dataset = list(train_iter)test_dataset = list(test_iter)num_train = int(len(train_dataset) * 0.95)split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)      # shuffle表示随机打乱valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)for epoch in range(1, EPOCHS + 1):epoch_start_time = time.time()train(train_dataloader)accu_val = evaluate(valid_dataloader)if total_accu is not None and total_accu > accu_val:scheduler.step()else:total_accu = accu_valprint('-' * 59)print('| end of epoch {:3d} | time: {:5.2f}s | ''valid accuracy {:8.3f} '.format(epoch, time.time() - epoch_start_time, accu_val))print('-' * 59)torch.save(model.state_dict(), 'output\\model_TextClassification.pth')

| epoch   1 |   500/ 1782 batches, accuracy    0.687
| epoch   1 |  1000/ 1782 batches, accuracy    0.856
| epoch   1 |  1500/ 1782 batches, accuracy    0.875
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| end of epoch   1 | time: 23.15s | valid accuracy    0.881
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| epoch   2 |   500/ 1782 batches, accuracy    0.898
| epoch   2 |  1000/ 1782 batches, accuracy    0.898
| epoch   2 |  1500/ 1782 batches, accuracy    0.903
-----------------------------------------------------------
| end of epoch   2 | time: 16.20s | valid accuracy    0.897
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| epoch   3 |   500/ 1782 batches, accuracy    0.917
| epoch   3 |  1000/ 1782 batches, accuracy    0.915
| epoch   3 |  1500/ 1782 batches, accuracy    0.914
-----------------------------------------------------------
| end of epoch   3 | time: 15.98s | valid accuracy    0.902
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| epoch   4 |   500/ 1782 batches, accuracy    0.924
| epoch   4 |  1000/ 1782 batches, accuracy    0.924
| epoch   4 |  1500/ 1782 batches, accuracy    0.922
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| end of epoch   4 | time: 16.63s | valid accuracy    0.901
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| epoch   5 |   500/ 1782 batches, accuracy    0.937
| epoch   5 |  1000/ 1782 batches, accuracy    0.937
| epoch   5 |  1500/ 1782 batches, accuracy    0.938
-----------------------------------------------------------
| end of epoch   5 | time: 16.37s | valid accuracy    0.912
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| epoch   6 |   500/ 1782 batches, accuracy    0.938
| epoch   6 |  1000/ 1782 batches, accuracy    0.939
| epoch   6 |  1500/ 1782 batches, accuracy    0.940
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| end of epoch   6 | time: 16.17s | valid accuracy    0.912
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| epoch   7 |   500/ 1782 batches, accuracy    0.940
| epoch   7 |  1000/ 1782 batches, accuracy    0.938
| epoch   7 |  1500/ 1782 batches, accuracy    0.943
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| end of epoch   7 | time: 16.20s | valid accuracy    0.911
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| epoch   8 |   500/ 1782 batches, accuracy    0.941
| epoch   8 |  1000/ 1782 batches, accuracy    0.940
| epoch   8 |  1500/ 1782 batches, accuracy    0.942
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| end of epoch   8 | time: 16.46s | valid accuracy    0.911
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| epoch   9 |   500/ 1782 batches, accuracy    0.941
| epoch   9 |  1000/ 1782 batches, accuracy    0.941
| epoch   9 |  1500/ 1782 batches, accuracy    0.943
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| end of epoch   9 | time: 17.50s | valid accuracy    0.912
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| epoch  10 |   500/ 1782 batches, accuracy    0.940
| epoch  10 |  1000/ 1782 batches, accuracy    0.942
| epoch  10 |  1500/ 1782 batches, accuracy    0.942
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| end of epoch  10 | time: 16.12s | valid accuracy    0.912
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