• 周六. 7月 2nd, 2022

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文本分类(六):pytorch实现DPCNN

admin

11月 28, 2021

一、简介

ACL2017年中,腾讯AI-lab提出了Deep Pyramid Convolutional Neural Networks for Text Categorization(DPCNN)。论文中提出了一种基于word-level级别的网络-DPCNN,由于上一篇文章介绍的TextCNN 不能通过卷积获得文本的长距离依赖关系,而论文中DPCNN通过不断加深网络,可以抽取长距离的文本依赖关系。实验证明在不增加太多计算成本的情况下,增加网络深度就可以获得最佳的准确率。‍

DPCNN结构

究竟是多么牛逼的网络呢?我们下面来窥探一下模型的芳容。

DPCNN结构细节

模型是如何通过加深网络来捕捉文本的长距离依赖关系的呢?下面我们来一一道来。为了更加简单的解释DPCNN,这里我先不解释是什么是Region embedding,我们先把它当作word embedding。

等长卷积

首先交代一下卷积的的一个基本概念。一般常用的卷积有以下三类:

假设输入的序列长度为n,卷积核大小为m,步长(stride)为s,输入序列两端各填补p个零(zero padding),那么该卷积层的输出序列为(n-m+2p)/s+1。

(1) 窄卷积(narrow convolution): 步长s=1,两端不补零,即p=0,卷积后输出长度为n-m+1。

(2) 宽卷积(wide onvolution) :步长s=1,两端补零p=m-1,卷积后输出长度 n+m-1。

(3) 等长卷积(equal-width convolution): 步长s=1,两端补零p=(m-1)/2,卷积后输出长度为n。如下图所示,左右两端同时补零p=1,s=3。

 

池化

那么DPCNN是如何捕捉长距离依赖的呢?这里我直接引用文章的小标题——Downsampling with the number of feature maps fixed。

作者选择了适当的两层等长卷积来提高词位embedding的表示的丰富性。然后接下来就开始 Downsampling (池化)。再每一个卷积块(两层的等长卷积)后,使用一个size=3和stride=2进行maxpooling进行池化。序列的长度就被压缩成了原来的一半。其能够感知到的文本片段就比之前长了一倍。

例如之前是只能感知3个词位长度的信息,经过1/2池化层后就能感知6个词位长度的信息啦,这时把1/2池化层和size=3的卷积层组合起来如图所示

固定feature maps(filters)的数量

为什么要固定feature maps的数量呢?许多模型每当执行池化操作时,增加feature maps的数量,导致总计算复杂度是深度的函数。与此相反,作者对feature map的数量进行了修正,他们实验发现增加feature map的数量只会大大增加计算时间,而没有提高精度。

另外,夕小瑶小姐姐在知乎也详细的解释了为什么要固定feature maps的数量。有兴趣的可以去知乎搜一搜,讲的非常透彻。

固定了feature map的数量,每当使用一个size=3和stride=2进行maxpooling进行池化时,每个卷积层的计算时间减半(数据大小减半),从而形成一个金字塔。

这就是论文题目所谓的 Pyramid。

好啦,看似问题都解决了,目标成功达成。剩下的我们就只需要重复的进行等长卷积+等长卷积+使用一个size=3和stride=2进行maxpooling进行池化就可以啦,DPCNN就可以捕捉文本的长距离依赖啦!

 

Shortcut connections with pre-activation

但是!如果问题真的这么简单的话,深度学习就一下子少了超级多的难点了。

(1) 初始化CNN的时,往往各层权重都初始化为很小的值,这导致了最开始的网络中,后续几乎每层的输入都是接近0,这时的网络输出没有意义;

(2) 小权重阻碍了梯度的传播,使得网络的初始训练阶段往往要迭代好久才能启动;

(3) 就算网络启动完成,由于深度网络中仿射矩阵(每两层间的连接边)近似连乘,训练过程中网络也非常容易发生梯度爆炸或弥散问题。

当然,上述这几点问题本质就是梯度弥散问题。那么如何解决深度CNN网络的梯度弥散问题呢?当然是膜一下何恺明大神,然后把ResNet的精华拿来用啦! ResNet中提出的shortcut-connection/ skip-connection/ residual-connection(残差连接)就是一种非常简单、合理、有效的解决方案。

类似地,为了使深度网络的训练成为可能,作者为了恒等映射,所以使用加法进行shortcut connections,即z+f(z),其中 f 用的是两层的等长卷积。这样就可以极大的缓解了梯度消失问题。

另外,作者也使用了 pre-activation,这个最初在何凯明的“Identity Mappings in Deep Residual Networks上提及,有兴趣的大家可以看看这个的原理。直观上,这种“线性”简化了深度网络的训练,类似于LSTM中constant error carousels的作用。而且实验证明 pre-activation优于post-activation。

整体来说,巧妙的结构设计,使得这个模型不需要为了维度匹配问题而担忧。

 

Region embedding

同时DPCNN的底层貌似保持了跟TextCNN一样的结构,这里作者将TextCNN的包含多尺寸卷积滤波器的卷积层的卷积结果称之为Region embedding,意思就是对一个文本区域/片段(比如3gram)进行一组卷积操作后生成的embedding。

另外,作者为了进一步提高性能,还使用了tv-embedding (two-views embedding)进一步提高DPCNN的accuracy。

上述介绍了DPCNN的整体架构,可见DPCNN的架构之精美。本文是在原始论文以及知乎上的一篇文章的基础上进行整理。本文可能也会有很多错误,如果有错误,欢迎大家指出来!建议大家为了更好的理解DPCNN ,看一下原始论文和参考里面的知乎。

二、pytorch实现

1、DPCNN.py

# coding: UTF-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class Config(object):

    """配置参数"""
    def __init__(self, dataset, embedding):
        self.model_name = 'DPCNN'
        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', encoding='utf-8').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_name
        self.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 = 0.2                                              # 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.num_filters = 250                                          # 卷积核数量(channels数)


'''Deep Pyramid Convolutional Neural Networks for Text Categorization'''


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.conv_region = nn.Conv2d(1, config.num_filters, (3, config.embed), stride=1)
        self.conv = nn.Conv2d(config.num_filters, config.num_filters, (3, 1), stride=1)
        self.max_pool = nn.MaxPool2d(kernel_size=(3, 1), stride=2)
        self.padding1 = nn.ZeroPad2d((0, 0, 1, 1))  # top bottom
        self.padding2 = nn.ZeroPad2d((0, 0, 0, 1))  # bottom
        self.relu = nn.ReLU()
        self.fc = nn.Linear(config.num_filters, config.num_classes)

    def forward(self, x):
        x = x[0]
        x = self.embedding(x)
        x = x.unsqueeze(1)  # [batch_size, 250, seq_len, 1]
        x = self.conv_region(x)  # [batch_size, 250, seq_len-3+1, 1]

        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        x = self.padding1(x)  # [batch_size, 250, seq_len, 1]
        x = self.relu(x)
        x = self.conv(x)  # [batch_size, 250, seq_len-3+1, 1]
        while x.size()[2] > 2:
            x = self._block(x)
        x = x.squeeze()  # [batch_size, num_filters(250)]
        x = self.fc(x)
        return x

    def _block(self, x):
        x = self.padding2(x)
        px = self.max_pool(x)

        x = self.padding1(px)
        x = F.relu(x)
        x = self.conv(x)

        x = self.padding1(x)
        x = F.relu(x)
        x = self.conv(x)

        x = x + px
        return x

2、run.py

# coding: UTF-8
import time
import torch
import numpy as np
from importlib import import_module
from utils import build_dataset, build_iterator, get_time_dif
from train_eval import train, init_network

import argparse

parser = argparse.ArgumentParser(description='Text Classification')
parser.add_argument('--model', default = "DPCNN", type=str, help='choose a model: DPCNN, BERT')
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 = 'gongqing'  # 数据集
    # 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random
    embedding = 'embedding_SougouNews.npz'
    if args.embedding == 'random':
        embedding = 'random'
    model_name = args.model  # DPCNN, Transformer

    x = 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)

    # train
    config.n_vocab = len(vocab)
    model = x.Model(config).to(config.device)
    init_network(model)
    print(model.parameters)
    train(config, model, train_iter, dev_iter, test_iter)

3、train_eval.py

# coding: UTF-8
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics
import time
from utils import get_time_dif
from tensorboardX import SummaryWriter


# 权重初始化,默认xavier
def init_network(model, method='xavier', exclude='embedding', seed=123):
    for name, w in model.named_parameters():
        if exclude not in name:
            if 'weight' in name:
                if method == 'xavier':
                    nn.init.xavier_normal_(w)
                elif method == 'kaiming':
                    nn.init.kaiming_normal_(w)
                else:
                    nn.init.normal_(w)
            elif 'bias' in name:
                nn.init.constant_(w, 0)
            else:
                pass


def train(config, model, train_iter, dev_iter, test_iter):
    start_time = time.time()
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)

    # 学习率指数衰减,每次epoch:学习率 = gamma * 学习率
    # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
    total_batch = 0  # 记录进行到多少batch
    dev_best_loss = float('inf')
    last_improve = 0  # 记录上次验证集loss下降的batch数
    flag = False  # 记录是否很久没有效果提升
    writer = SummaryWriter(log_dir=config.log_path + '/' + time.strftime('%m-%d_%H.%M', time.localtime()))
    for epoch in range(config.num_epochs):
        print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
        # scheduler.step() # 学习率衰减
        for i, (trains, labels) in enumerate(train_iter):
            outputs = model(trains)
            model.zero_grad()
            loss = F.cross_entropy(outputs, labels)
            loss.backward()
            optimizer.step()
            if total_batch % 100 == 0:
                # 每多少轮输出在训练集和验证集上的效果
                true = labels.data.cpu()
                predic = torch.max(outputs.data, 1)[1].cpu()
                train_acc = metrics.accuracy_score(true, predic)
                dev_acc, dev_loss = evaluate(config, model, dev_iter)
                if dev_loss < dev_best_loss:
                    dev_best_loss = dev_loss
                    torch.save(model.state_dict(), config.save_path)
                    improve = '*'
                    last_improve = total_batch
                else:
                    improve = ''
                time_dif = get_time_dif(start_time)
                msg = 'Iter: {0:>6},  Train Loss: {1:>5.2},  Train Acc: {2:>6.2%},  Val Loss: {3:>5.2},  Val Acc: {4:>6.2%},  Time: {5} {6}'
                print(msg.format(total_batch, loss.item(), train_acc, dev_loss, dev_acc, time_dif, improve))
                writer.add_scalar("loss/train", loss.item(), total_batch)
                writer.add_scalar("loss/dev", dev_loss, total_batch)
                writer.add_scalar("acc/train", train_acc, total_batch)
                writer.add_scalar("acc/dev", dev_acc, total_batch)
                model.train()
            total_batch += 1
            if total_batch - last_improve > config.require_improvement:
                # 验证集loss超过1000batch没下降,结束训练
                print("No optimization for a long time, auto-stopping...")
                flag = True
                break
        if flag:
            break
    writer.close()
    test(config, model, test_iter)


def test(config, model, test_iter):
    # test
    model.load_state_dict(torch.load(config.save_path))
    model.eval()
    start_time = time.time()
    test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
    msg = 'Test Loss: {0:>5.2},  Test Acc: {1:>6.2%}'
    print(msg.format(test_loss, test_acc))
    print("Precision, Recall and F1-Score...")
    print(test_report)
    print("Confusion Matrix...")
    print(test_confusion)
    time_dif = get_time_dif(start_time)
    print("Time usage:", time_dif)


def evaluate(config, model, data_iter, test=False):
    model.eval()
    loss_total = 0
    predict_all = np.array([], dtype=int)
    labels_all = np.array([], dtype=int)
    with torch.no_grad():
        for texts, labels in data_iter:
            outputs = model(texts)
            loss = F.cross_entropy(outputs, labels)
            loss_total += loss
            labels = labels.data.cpu().numpy()
            predic = torch.max(outputs.data, 1)[1].cpu().numpy()
            labels_all = np.append(labels_all, labels)
            predict_all = np.append(predict_all, predic)

    acc = metrics.accuracy_score(labels_all, predict_all)
    if test:
        report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
        confusion = metrics.confusion_matrix(labels_all, predict_all)
        return acc, loss_total / len(data_iter), report, confusion
    return acc, loss_total / len(data_iter)

4、utils.py

# coding: UTF-8
import os
import torch
import numpy as np
import pickle as pkl
from tqdm import tqdm
import time
from datetime import timedelta


MAX_VOCAB_SIZE = 10000  # 词表长度限制
UNK, PAD = '<UNK>', '<PAD>'  # 未知字,padding符号


def build_vocab(file_path, tokenizer, max_size, min_freq):
    vocab_dic = {}
    with open(file_path, 'r', encoding='UTF-8') as f:
        for line in tqdm(f):
            lin = line.strip()
            if not lin:
                continue
            content = lin.split('	')[0]
            for word in tokenizer(content):
                vocab_dic[word] = vocab_dic.get(word, 0) + 1
        vocab_list = sorted([_ for _ in vocab_dic.items() if _[1] >= min_freq], key=lambda x: x[1], reverse=True)[:max_size]
        vocab_dic = {word_count[0]: idx for idx, word_count in enumerate(vocab_list)}
        vocab_dic.update({UNK: len(vocab_dic), PAD: len(vocab_dic) + 1})
    return vocab_dic


def build_dataset(config, ues_word):
    if ues_word:
        tokenizer = lambda x: x.split(' ')  # 以空格隔开,word-level
    else:
        tokenizer = lambda x: [y for y in x]  # char-level
    if os.path.exists(config.vocab_path):
        vocab = pkl.load(open(config.vocab_path, 'rb'))
        print(vocab)
    else:
        vocab = build_vocab(config.train_path, tokenizer=tokenizer, max_size=MAX_VOCAB_SIZE, min_freq=1)
        pkl.dump(vocab, open(config.vocab_path, 'wb'))
    print(f"Vocab size: {len(vocab)}")

    def load_dataset(path, pad_size=32):
        contents = []
        with open(path, 'r', encoding='UTF-8') as f:
            for line in tqdm(f):
                lin = line.strip()
                if not lin:
                    continue
                content, label = lin.split('	')
                words_line = []
                token = tokenizer(content)
                seq_len = len(token)
                if pad_size:
                    if len(token) < pad_size:
                        token.extend([PAD] * (pad_size - len(token)))
                    else:
                        token = token[:pad_size]
                        seq_len = pad_size
                # word to id
                for word in token:
                    words_line.append(vocab.get(word, vocab.get(UNK)))
                contents.append((words_line, int(label), seq_len))
        return contents  # [([...], 0), ([...], 1), ...]
    train = load_dataset(config.train_path, config.pad_size)
    dev = load_dataset(config.dev_path, config.pad_size)
    test = load_dataset(config.test_path, config.pad_size)
    return vocab, train, dev, test


class DatasetIterater(object):
    def __init__(self, batches, batch_size, device):
        self.batch_size = batch_size
        self.batches = batches
        self.n_batches = len(batches) // batch_size
        self.residue = False  # 记录batch数量是否为整数
        if len(batches) % self.n_batches != 0:
            self.residue = True
        self.index = 0
        self.device = device

    def _to_tensor(self, datas):
        x = torch.LongTensor([_[0] for _ in datas]).to(self.device)
        y = torch.LongTensor([_[1] for _ in datas]).to(self.device)

        # pad前的长度(超过pad_size的设为pad_size)
        seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device)
        return (x, seq_len), y

    def __next__(self):
        if self.residue and self.index == self.n_batches:
            batches = self.batches[self.index * self.batch_size: len(self.batches)]
            self.index += 1
            batches = self._to_tensor(batches)
            return batches

        elif self.index >= self.n_batches:
            self.index = 0
            raise StopIteration
        else:
            batches = self.batches[self.index * self.batch_size: (self.index + 1) * self.batch_size]
            self.index += 1
            batches = self._to_tensor(batches)
            return batches

    def __iter__(self):
        return self

    def __len__(self):
        if self.residue:
            return self.n_batches + 1
        else:
            return self.n_batches


def build_iterator(dataset, config):
    iter = DatasetIterater(dataset, config.batch_size, config.device)
    return iter


def get_time_dif(start_time):
    """获取已使用时间"""
    end_time = time.time()
    time_dif = end_time - start_time
    return timedelta(seconds=int(round(time_dif)))

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