标签：实战 seq lengths self torch Kaggle 情感 hidden size

前言

这几天持续摆烂了几天，原因是我自己对于Kaggle电影评论情感分析的这个赛题敲出来的代码无论如何没办法运行，其中数据变换的维度我无法把握好，所以总是在函数中传错数据。今天痛定思痛，重新写了一遍代码，终于成功。

从国籍分类入手

在这个题目之前，给了一个按照姓名分类国籍的写法
https://www.bilibili.com/video/BV1Y7411d7Ys?p=13
按照这个写法我来写这个赛题，代码以及注释如下

'''''''''
构建一个RNN分类器
任务：一个名称分类器，根据输入的名字判断其国籍，数据集有Name与Country
在这个场景中，由于输出无法通过线性层映射到某个维度，所以可以只用hn来连接线性层，对这个输入做一个18维的分类
'''''''''
import csv
import gzip
import torch
import matplotlib.pyplot as plt
import numpy as np
from torch.nn.utils.rnn import pack_padded_sequence
from torch.utils.data import Dataset, DataLoader

device = torch.device('cuda:0')

HIDDEN_SIZE = 100
BATCH_SIZE = 256
N_LAYER = 2
N_EPOCHS = 100
N_CHARS = 128 # 字符集字典维度


class NameDataset(Dataset): #数据集类
    def __init__(self,is_train_set = True):
        filename = 'names_train.csv.gz' if is_train_set else 'names_test.csv.gz'
        with gzip.open(filename,'rt') as f:
            reader = csv.reader(f)
            rows = list(reader)
        self.names = [row[0] for row in rows] # 把名字字符串存到列表
        self.len = len(self.names) # 数据集长度
        self.countries = [row[1] for row in rows] # 所有国家字符串存到列表
        self.country_list = list(sorted(set(self.countries))) # unique国家列表
        self.country_dict = self.getCountryDict()
        self.country_num = len(self.country_list) # unique国家数
    def __getitem__(self,index):
        return self.names[index],self.country_dict[self.countries[index]]
        # 返回名称和国家，国家先通过index找到国家，再通过字典映射返回国家的序号
    def __len__(self):
        return self.len
    def getCountryDict(self): # 把unique国家做成字典
        country_dict = dict()
        for idx,counrty_name in enumerate(self.country_list,0):
            country_dict[counrty_name] = idx
        return country_dict
    def idx2country(self,index):
        return self.country_list[index]
    def getCountriesNum(self):
        return self.country_num # 返回unique国家数

trainset = NameDataset(is_train_set=True)
trainloader = DataLoader(trainset,batch_size=BATCH_SIZE,shuffle=True)
testset = NameDataset(is_train_set=False)
testloader = DataLoader(testset,batch_size=BATCH_SIZE,shuffle=False)

N_COUNTRY = trainset.getCountriesNum()
class RNNClassifier(torch.nn.Module):
    def __init__(self,input_size,hidden_size,output_size,n_layers=1,bidirectional=True):
        super(RNNClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.n_layers = n_layers
        self.n_directions = 2 if bidirectional else 1

        self.embedding = torch.nn.Embedding(input_size,hidden_size) # inputs_size是名称字符集长度
        self.gru = torch.nn.GRU(hidden_size,hidden_size,n_layers,bidirectional=bidirectional)
        self.fc = torch.nn.Linear(hidden_size*self.n_directions,output_size) # output_size是N_COUNTRY

    def _init_hidden(self,batch_size):
        hidden = torch.zeros(self.n_layers*self.n_directions,batch_size,self.hidden_size)
        #layers*batch_size*hidden_size
        return hidden.to(device)

    def forward(self,input,seq_lengths):
        input = input.t() # b*s to s*b
        batch_size = input.size(1)
        hidden = self._init_hidden(batch_size)
        embedding = self.embedding(input)

        gru_input = pack_padded_sequence(embedding,seq_lengths.cpu())
        # 这是gru和lstm可以接受的一种输入, PackedSequence object
        output,hidden = self.gru(gru_input,hidden)
        if self.n_directions == 2:
            hidden_cat = torch.cat((hidden[-1],hidden[-2]),dim=1)
        else:
            hiddden_cat = hidden[-1]
        fc_output = self.fc(hidden_cat)
        return fc_output

def name2list(name):
    arr = [ord(c) for c in name]
    return arr,len(arr)
    # 返回输入名字的asci码值的列表，和名字长度
def make_tensors(names,countries): # 把输入数据处理为tensor
    sequences_and_lengths = [name2list(name) for name in names]
    name_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths]) # seq_lengths to longTensor
    countries = countries.long() # index of country to longTensor
    # make tensor of name, batchsize*seqlen
    seq_tensor = torch.zeros(len(name_sequences),seq_lengths.max()).long()
    # 这一句先生成一个二维全0张量，高是名称序列数，宽是最长的名字长度，做成longTensor
    for idx,(seq,seq_len) in enumerate(zip(name_sequences,seq_lengths),0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq) # 复制到上面的全0张量中
    # sort sequences by length to use pack_padded_sequence
    seq_lengths,perm_idx = seq_lengths.sort(dim=0,descending=True)
    # torch中tensor类的tensor返回的是排完序列和索引
    seq_tensor = seq_tensor[perm_idx]
    countries = countries[perm_idx]
    return seq_tensor.to(device),seq_lengths.to(device),countries.to(device)

classifier = RNNClassifier(N_CHARS,HIDDEN_SIZE,N_COUNTRY,N_LAYER,True).to(device)
#classifier = classifier.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(),lr=0.001)
#start = time.time()

def trainModel():
    total_loss = 0
    for i,(names,countries) in enumerate(trainloader,1): # i从1开始数
        inputs,seq_lengths,target = make_tensors(names,countries)
        output = classifier(inputs,seq_lengths) # 这是送的forward的参数.
        loss = criterion(output,target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        if i%10 == 0:
            print(f'Epoch{epoch}',end='')
            print(f'[{i*len(inputs)}/{len(trainset)}]',end='')
            print(f'loss={total_loss/(i*len(inputs))}')
    return total_loss

def testModel():
    correct = 0
    total = len(testset)
    print('evaluating trained model ...')
    with torch.no_grad():
        for i,(names,countries) in enumerate(testloader,1):
            inputs,seq_lengths,target = make_tensors(names,countries)
            output = classifier(inputs,seq_lengths)
            pred = output.max(dim=1,keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()
        percent = '%.2f' % (100*correct/total)
        print(f'Test set: Accuracy{correct}/{total} {percent}%')
    return  correct/total



print('Training for %d epochs...' % N_EPOCHS)
acc_list = []
for epoch in range(1,30):
    trainModel()
    acc = testModel()
    acc_list.append(acc)


epoch = np.arange(1,len(acc_list)+1,1)
acc_list = np.array(acc_list)
plt.plot(epoch,acc_list)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.grid()
plt.show()

好，下面我们进入赛题部分

赛题理解

赛题以及数据的下载地址如下：https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews
数据就是给出id，评论内容，以及标注好的情感极性，然后经过训练，测试集传入模型判断测试集评论的情感，给出csv格式文件，来进行评分，数据长得就是下面这个样子：

数据处理

由于给出的是tsv格式，所以我们用pandas自带的read来读取，只需要把phrase和sentiment取出来就行了，代码如下：

class NameDataset(Dataset): #数据集类
    def __init__(self, is_train_set=True):
        train = pd.read_csv('train.tsv', sep='\t')  # 分隔符是空格
        self.phrase = train['Phrase']
        self.sentiment = train['Sentiment']
        self.len = len(self.phrase)

    def __getitem__(self, index):
        return self.phrase[index], self.sentiment[index]

    def __len__(self):
        return self.len

由于评论是string字符，我们需要把它转成可以被接受的向量

def phrase2list(phrase):
    arr = [ord(c) for c in phrase]
    return arr, len(arr)
## 用ASCILL编码来转换字符

def make_tensors(phrase, sentiment):
    sequences_and_lengths = [phrase2list(phrase) for phrase in phrase]
    phrase_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths])
    sentiment = sentiment.long()

    seq_tensor = torch.zeros(len(phrase_sequences), seq_lengths.max()).long()
    for idx, (seq, seq_len) in enumerate(zip(phrase_sequences, seq_lengths), 0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)
    seq_lengths, prem_idx = seq_lengths.sort(dim=0, descending=True)
    seq_tensor = seq_tensor[prem_idx]
    sentiment = sentiment[prem_idx]
    return seq_tensor.to(device), seq_lengths.to(device), sentiment.to(device)

然后测试集是不需要转变sentiment的，因为根本没有，所以在搞一个专门给测试集传字符

def make_tensors1(phrase):
    sequences_and_lengths = [phrase2list(phrase) for phrase in phrase]
    phrase_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths])

    seq_tensor = torch.zeros(len(phrase_sequences), seq_lengths.max()).long()
    for idx, (seq, seq_len) in enumerate(zip(phrase_sequences, seq_lengths), 0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)
    seq_lengths, prem_idx = seq_lengths.sort(dim=0, descending=True)
    seq_tensor = seq_tensor[prem_idx]
    _, index = prem_idx.sort(descending=False)
    return seq_tensor.to(device), seq_lengths.to(device), index

设计模型

首先我们确定一些超参数

device = torch.device('cuda:0')
NUM_CHARS = 128
HIDDEN_SIZE = 100
NUM_CLASS = 5
NUM_LAYERS = 2
NUM_EPOCHS = 30
BATCH_SIZE = 512

然后我们使用GRU

class RNNClassifier(torch.nn.Module):
    def  __init__(self, input_size, hidden_size, output_size, n_layers=1, bidirectional=True):
        super(RNNClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.n_layers = n_layers
        self.n_direction = 2 if bidirectional else 1

        self.embedding = torch.nn.Embedding(input_size, hidden_size)
        self.gru = torch.nn.GRU(hidden_size, hidden_size, n_layers, bidirectional=bidirectional)
        self.fc = torch.nn.Linear(hidden_size*self.n_direction, output_size)

    def _init_hidden(self, batch_size):
        hidden = torch.zeros(self.n_layers*self.n_direction, batch_size, self.hidden_size)
        return hidden.to(device)

    def forward(self, input, seq_lengths):
        input = input.t()
        batch_size = input.size(1)
        hidden = self._init_hidden(batch_size)
        embedding = self.embedding(input)

        gru_input = pack_padded_sequence(embedding, seq_lengths.cpu())
        output, hidden = self.gru(gru_input, hidden)
        if self.n_direction == 2:
            hidden_cat = torch.cat((hidden[-1], hidden[-2]), dim=1)
        else:
            hidden_cat = hidden[-1]
        fc_output = self.fc(hidden_cat)
        return fc_output

模型实例化并设计损失函数和优化器

classifier = RNNClassifier(NUM_CHARS, HIDDEN_SIZE, NUM_CLASS, NUM_LAYERS, True).to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)

模型训练与测试

首先来设计训练函数

def train():
    total_loss = 0
    for i, (phrase, sentiment) in enumerate(train_loader, 1):
        inputs, seq_lengths, target = make_tensors(phrase, sentiment)
        output = classifier(inputs, seq_lengths)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.zero_grad()
        optimizer.step()
        total_loss += loss.item()
        if i % 10 == 0:
            print(f'Epoch{epoch}', end='')
            print(f'[{i * len(inputs)}/{len(train_set)}]', end='')
            print(f'loss={total_loss / (i * len(inputs))}')
    return total_loss

然后设计函数来获取测试集数据

def get_test_set():
    test_set = pd.read_csv('test.tsv', '\t')
    PhraseId = test_set['PhraseId']
    test_Phrase = test_set['Phrase']
    return PhraseId, test_Phrase

测试函数设计如下

def testModel():
    PhraseId, test_Phrase = get_test_set()
    sentiment_list = []  # 定义预测结果列表
    batchNum = math.ceil(PhraseId.shape[0] / BATCH_SIZE)
    with torch.no_grad():
        for i in range(batchNum):
            print(i)
            if i == batchNum - 1:
                phraseBatch = test_Phrase[BATCH_SIZE * i:]  # 处理最后不足BATCH_SIZE的情况
            else:
                phraseBatch = test_Phrase[BATCH_SIZE * i:BATCH_SIZE * (i + 1)]
            inputs, seq_lengths, org_idx = make_tensors1(phraseBatch)
            output = classifier(inputs, seq_lengths)
            sentiment = output.max(dim=1, keepdim=True)[1]
            sentiment = sentiment[org_idx].squeeze(1)
            sentiment_list.append(sentiment.cpu().numpy().tolist())
    sentiment_list = list(chain.from_iterable(sentiment_list)) # 将sentiment_list按行拼成一维列表
    result = pd.DataFrame({'PhraseId': PhraseId, 'Sentiment': sentiment_list})
    result.to_csv('SA_predict.csv', index=False)

开始跑代码

if __name__ == '__main__':
    start = time.time()
    print('Training for %d epochs...' % NUM_EPOCHS)
    acc_list = []
    for epoch in range(1, NUM_EPOCHS + 1):
        train()
        acc = train()
        acc_list.append(acc)
        if acc <= min(acc_list):
            torch.save(classifier, 'sentimentAnalyst.pkl')
            print('Save Model!')
    testModel()
    epoch = [epoch + 1 for epoch in range(len(acc_list))]
    plt.plot(epoch, acc_list)
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.grid()
    plt.show()

结果

淦，跑完忘记截图platshow的图了，但是我上传kaggle，一跑完只有0.2分，哪里出了问题呢
改成14次，这次截图了


我陷入了沉思

inputs, seq_lengths, target = make_tensors(phrase, sentiment)
        output = classifier(inputs, seq_lengths)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.zero_grad()
        optimizer.step()
        total_loss += loss.item()

问题出在这里
我他妈的一不小心多写了一步 optimizer.zero_grad()，结果跑了几个小时都在原地踏步
现在我们用修改后的模型再试一下
跑完十次的结果和分数如下

完整代码

import math
from itertools import chain
import torch
import matplotlib.pyplot as plt
import pandas as pd
from torch.nn.utils.rnn import pack_padded_sequence
from torch.utils.data import Dataset, DataLoader


class NameDataset(Dataset): #数据集类
    def __init__(self):
        self.train = pd.read_csv('train.tsv', sep='\t')
        self.phrase = self.train['Phrase']
        self.sentiment = self.train['Sentiment']
        self.len = self.train.shape[0]

    def __getitem__(self, index):
        return self.phrase[index], self.sentiment[index]

    def __len__(self):
        return self.len


device = torch.device('cuda:0')
NUM_CHARS = 128
HIDDEN_SIZE = 128
NUM_LAYERS = 2
NUM_EPOCHS = 10
BATCH_SIZE = 512
train_set = NameDataset()
train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
NUM_CLASS = len(set(train_set.sentiment))


class RNNClassifier(torch.nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1, bidirectional=True):
        super(RNNClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.n_layers = n_layers
        self.n_direction = 2 if bidirectional else 1

        self.embedding = torch.nn.Embedding(input_size, hidden_size)
        self.gru = torch.nn.GRU(hidden_size, hidden_size, n_layers, bidirectional=bidirectional)
        self.fc = torch.nn.Linear(hidden_size*self.n_direction, output_size)

    def _init_hidden(self, batch_size):
        hidden = torch.zeros(self.n_layers*self.n_direction, batch_size, self.hidden_size)
        return hidden.to(device)

    def forward(self, input, seq_lengths):
        input = input.t()
        batch_size = input.size(1)
        hidden = self._init_hidden(batch_size)
        embedding = self.embedding(input)

        gru_input = pack_padded_sequence(embedding, seq_lengths.cpu())
        output, hidden = self.gru(gru_input, hidden)
        if self.n_direction == 2:
            hidden_cat = torch.cat((hidden[-1], hidden[-2]), dim=1)
        else:
            hidden_cat = hidden[-1]
        fc_output = self.fc(hidden_cat)
        return fc_output


def phrase2list(phrase):
    arr = [ord(c) for c in phrase]
    return arr, len(arr)


def make_tensors(phrase, sentiment):
    sequences_and_lengths = [phrase2list(phrase) for phrase in phrase]
    phrase_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths])
    sentiment = sentiment.long()

    seq_tensor = torch.zeros(len(phrase_sequences), seq_lengths.max()).long()
    for idx, (seq, seq_len) in enumerate(zip(phrase_sequences, seq_lengths), 0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)
    seq_lengths, prem_idx = seq_lengths.sort(dim=0, descending=True)
    seq_tensor = seq_tensor[prem_idx]
    sentiment = sentiment[prem_idx]
    return seq_tensor.to(device), seq_lengths.to(device), sentiment.to(device)


def make_tensors1(phrase):
    sequences_and_lengths = [phrase2list(phrase) for phrase in phrase]
    phrase_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths])

    seq_tensor = torch.zeros(len(phrase_sequences), seq_lengths.max()).long()
    for idx, (seq, seq_len) in enumerate(zip(phrase_sequences, seq_lengths), 0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)
    seq_lengths, prem_idx = seq_lengths.sort(dim=0, descending=True)
    seq_tensor = seq_tensor[prem_idx]
    _, index = prem_idx.sort(descending=False)
    return seq_tensor.to(device), seq_lengths.to(device), index


def train():
    total_loss = 0
    for i, (phrase, sentiment) in enumerate(train_loader, 1):
        inputs, seq_lengths, target = make_tensors(phrase, sentiment)
        output = classifier(inputs, seq_lengths)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        if i % 10 == 0:
            print(f'Epoch{epoch}', end='')
            print(f'[{i * len(inputs)}/{len(train_set)}]', end='')
            print(f'loss={total_loss / (i * len(inputs))}')
    return total_loss


def get_test_set():
    test_set = pd.read_csv('test.tsv', '\t')
    PhraseId = test_set['PhraseId']
    test_Phrase = test_set['Phrase']
    return PhraseId, test_Phrase


def testModel():
    PhraseId, test_Phrase = get_test_set()
    sentiment_list = []  # 定义预测结果列表
    batchNum = math.ceil(PhraseId.shape[0] / BATCH_SIZE)
    with torch.no_grad():
        for i in range(batchNum):
            if i == batchNum - 1:
                phraseBatch = test_Phrase[BATCH_SIZE * i:]  # 处理最后不足BATCH_SIZE的情况
            else:
                phraseBatch = test_Phrase[BATCH_SIZE * i:BATCH_SIZE * (i + 1)]
            inputs, seq_lengths, org_idx = make_tensors1(phraseBatch)
            output = classifier(inputs, seq_lengths)
            sentiment = output.max(dim=1, keepdim=True)[1]
            sentiment = sentiment[org_idx].squeeze(1)
            sentiment_list.append(sentiment.cpu().numpy().tolist())

    sentiment_list = list(chain.from_iterable(sentiment_list))  # 将sentiment_list按行拼成一维列表
    result = pd.DataFrame({'PhraseId': PhraseId, 'Sentiment': sentiment_list})
    result.to_csv('SA_predict.csv', index=False)


if __name__ == '__main__':
    classifier = RNNClassifier(NUM_CHARS, HIDDEN_SIZE, NUM_CLASS, NUM_LAYERS).to(device)
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
    print('Training for %d epochs...' % NUM_EPOCHS)
    acc_list = []
    for epoch in range(1, NUM_EPOCHS + 1):
        train()
        acc = train()
        acc_list.append(acc)
        if acc <= min(acc_list):
            torch.save(classifier, 'sentimentAnalyst.pkl')
            print('Save Model!')

    testModel()
    epoch = [epoch + 1 for epoch in range(len(acc_list))]
    plt.plot(epoch, acc_list)
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.grid()
    plt.show()

其实这里可以感觉到验证集的重要性了，因为你很容易过拟合，所以这里附上大佬写的带验证集的代码链接
https://blog.csdn.net/qq_39187959/article/details/121102959
带有验证集可以让模型达到0.7分

标签：实战,seq,lengths,self,torch,Kaggle,情感,hidden,size
来源： https://www.cnblogs.com/lugendary/p/16192669.html

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。