pytorch 实现情感分类问题小结

from collections import defaultdict, Counter
 
class Vocab:
    def __init__(self, tokens=None):
        self.idx_to_token = list()
        self.token_to_idx = dict()
 
        if tokens is not None:
            if "" not in tokens:
                tokens = tokens + [""]
            for token in tokens:
                self.idx_to_token.append(token)
                self.token_to_idx[token] = len(self.idx_to_token) - 1
            self.unk = self.token_to_idx['']
 
    @classmethod
    def build(cls, text, min_freq=1, reserved_tokens=None):
        token_freqs = defaultdict(int)
        for sentence in text:
            for token in sentence:
                token_freqs[token] += 1
        uniq_tokens = [""] + (reserved_tokens if reserved_tokens else [])
        uniq_tokens += [token for token, freq in token_freqs.items() \
                        if freq >= min_freq and token != ""]
        return cls(uniq_tokens)
 
    def __len__(self):
        #返回词表大小，即词表有多少个互不相同的标记
        return len(self.idx_to_token)
 
    def __getitem__(self, token):
        #查找对应输入标记的索引
        #不存在，返回标记的索引
        return self.token_to_idx.get(token, self.unk)
 
    def convert_tokens_to_ids(self, tokens):
        #查找一系列输入标记对应的索引
        return [self[token] for token in tokens]
 
    def convert_ids_to_tokens(self, indices):
        #查找一系列索引值对应的标记
        return [self.idx_to_token[index] for index in indices]
 
 
def save_vocab(vocab, path):
    with open(path, 'w') as writer:
        writer.write("\n".join(vocab.idx_to_token))
 
 
def read_vocab(path):
    with open(path, 'r') as f:
        tokens = f.read().split('\n')
    return Vocab(tokens)
 

import torch
from torch import nn
#词表大小为8，Embedding向量维度3
embedding=nn.Embedding(8,3)
#输入形状(2,4)的整数张量，相当于2个长度为4的整数序列，每个整数范围是0-7
input=torch.tensor([[0,1,2,1],[4,6,6,7]],dtype=torch.long)
#调用embedding
output=embedding(input)
print(output)
print(output.shape)

tensor([[[-0.9536,  0.3367, -1.1639],
         [-0.4816, -0.8973, -0.7700],
         [ 1.9007,  1.3130, -1.2717],
         [-0.4816, -0.8973, -0.7700]],
 
        [[ 0.5755,  0.8908, -1.0384],
         [ 0.3724,  0.1216,  2.4466],
         [ 0.3724,  0.1216,  2.4466],
         [ 1.4089,  0.2458,  0.2263]]], grad_fn=)
torch.Size([2, 4, 3])

import torch
from torch import nn
from torch.nn import functional as F
 
class MLP(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):
        super(MLP, self).__init__()
        # 词嵌入层
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        # 线性变换：词嵌入层->隐含层
        self.linear1 = nn.Linear(embedding_dim, hidden_dim)
        # 使用ReLU激活函数
        self.activate = F.relu
        # 线性变换：激活层->输出层
        self.linear2 = nn.Linear(hidden_dim, num_class)
 
    def forward(self, inputs):
        embeddings = self.embedding(inputs)
        # 将序列中多个embedding进行聚合（此处是求平均值）
        embedding = embeddings.mean(dim=1)
        hidden = self.activate(self.linear1(embedding))
        outputs = self.linear2(hidden)
        # 获得每个序列属于某一类别概率的对数值
        probs = F.log_softmax(outputs, dim=1)
        return probs
 
mlp = MLP(vocab_size=8, embedding_dim=3, hidden_dim=5, num_class=2)
# 输入为两个长度为4的整数序列
inputs = torch.tensor([[0, 1, 2, 1], [4, 6, 6, 7]], dtype=torch.long)
outputs = mlp(inputs)
print(outputs)

tensor([[-1.1612, -0.3756],
        [-0.8089, -0.5894]], grad_fn=)

import torch
from vocab import Vocab
 
def load_sentence_polarity():
    from nltk.corpus import sentence_polarity
 
    #使用全部橘子集合（已经过标记解析）创建词表
    vocab = Vocab.build(sentence_polarity.sents())
 
    #褒贬各4000句子作为训练数据，使用创建的词表将标记映射为相应的索引
    #褒义标签为0，贬义标签为1
    #每个样例是一个有索引值列表和标签组成的元祖
    train_data = [(vocab.convert_tokens_to_ids(sentence), 0)
                  for sentence in sentence_polarity.sents(categories='pos')[:4000]] \
        + [(vocab.convert_tokens_to_ids(sentence), 1)
            for sentence in sentence_polarity.sents(categories='neg')[:4000]]
 
    #其余的作为测试数据
    test_data = [(vocab.convert_tokens_to_ids(sentence), 0)
                 for sentence in sentence_polarity.sents(categories='pos')[4000:]] \
        + [(vocab.convert_tokens_to_ids(sentence), 1)
            for sentence in sentence_polarity.sents(categories='neg')[4000:]]
 
    return train_data, test_data, vocab
 
def length_to_mask(lengths):
    max_len = torch.max(lengths)
    mask = torch.arange(max_len).expand(lengths.shape[0], max_len) < lengths.unsqueeze(1)
    return mask
 
def load_treebank():
    from nltk.corpus import treebank
    sents, postags = zip(*(zip(*sent) for sent in treebank.tagged_sents()))
 
    vocab = Vocab.build(sents, reserved_tokens=[""])
 
    tag_vocab = Vocab.build(postags)
 
    train_data = [(vocab.convert_tokens_to_ids(sentence), tag_vocab.convert_tokens_to_ids(tags)) for sentence, tags in zip(sents[:3000], postags[:3000])]
    test_data = [(vocab.convert_tokens_to_ids(sentence), tag_vocab.convert_tokens_to_ids(tags)) for sentence, tags in zip(sents[3000:], postags[3000:])]
 
    return train_data, test_data, vocab, tag_vocab
 

import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from collections import defaultdict
from vocab import Vocab
from utils import load_sentence_polarity
 
class BowDataset(Dataset):
    def __init__(self, data):
        #data为原始数据
        self.data = data
    def __len__(self):
        #数据集中样例的数目
        return len(self.data)
    def __getitem__(self, i):
        #返回下标i的数据
        return self.data[i]
 
#用于对样本进行整理
def collate_fn(examples):
    #从独立样本集合中构建各批次的输入输出 
    #其中，BowDataset类定义了一个样本的数据结构，即输入标签和轮出标悠的元组
    # 因此，将输入inputs定义为一个张量的列表，其中每个张量为原始句子中标记序列 
    inputs = [torch.tensor(ex[0]) for ex in examples]
    #输出的目标targets 为该批次中全部样例输出结果 （0或1） 构成的张量
    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)
    # 获取一个批次中每个样例的序列长度 
    offsets = [0] + [i.shape[0] for i in inputs]
    #根据序列的长度，转换为每个序列起始位置的偏移量 
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    #将inputs列表中的张量拼接成一个大的张量 
    inputs = torch.cat(inputs)
    return inputs, offsets, targets

import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from collections import defaultdict
from vocab import Vocab
from utils import load_sentence_polarity
 
 
# tqdm是一个Python模块，能以进度条的方式显示迭代的进度
from tqdm.auto import tqdm
 
# 超参数设置
embedding_dim = 128
hidden_dim = 256
num_class = 2
batch_size = 32
num_epoch = 5
 
# 加载数据
train_data, test_data, vocab = load_sentence_polarity()
train_dataset = BowDataset(train_data)
test_dataset = BowDataset(test_data)
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)
 
# 加载模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = MLP(len(vocab), embedding_dim, hidden_dim, num_class)
model.to(device) # 将模型加载到CPU或GPU设备
 
#训练过程
nll_loss = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) # 使用Adam优化器
 
model.train()
for epoch in range(num_epoch):
    total_loss = 0
    for batch in tqdm(train_data_loader, desc=f"Training Epoch {epoch}"):
        inputs, offsets, targets = [x.to(device) for x in batch]
        log_probs = model(inputs, offsets)
        loss = nll_loss(log_probs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Loss: {total_loss:.2f}")
 
# 测试过程
acc = 0
for batch in tqdm(test_data_loader, desc=f"Testing"):
    inputs, offsets, targets = [x.to(device) for x in batch]
    with torch.no_grad():
        output = model(inputs, offsets)
        acc += (output.argmax(dim=1) == targets).sum().item()
 
# 输出在测试集上的准确率
print(f"Acc: {acc / len(test_data_loader):.2f}")

import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
from collections import defaultdict
from vocab import Vocab
from utils import load_sentence_polarity
 
class CnnDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]
 
#另外，数据整理函数也需要进行一些修改。  
# pad_seguence 两数实现补齐 （Padding）功能，使得一个批次中 全部序列长度相同（同最大长度序列），不足的默认使用0补齐。 
def collate_fn(examples):
    inputs = [torch.tensor(ex[0]) for ex in examples]
    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)
    # 对batch内的样本进行padding，使其具有相同长度
    inputs = pad_sequence(inputs, batch_first=True)
    return inputs, targets
 
#模型不同，需要从nn.Module类派生一个 CNN 子类
class CNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, filter_size, num_filter, num_class):
        super(CNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        #padding=1表示在卷积操作之前，将序列的前后各补充1个输入
        self.conv1d = nn.Conv1d(embedding_dim, num_filter, filter_size, padding=1)
        self.activate = F.relu
        self.linear = nn.Linear(num_filter, num_class)
    def forward(self, inputs):
        embedding = self.embedding(inputs)
        convolution = self.activate(self.conv1d(embedding.permute(0, 2, 1)))
        pooling = F.max_pool1d(convolution, kernel_size=convolution.shape[2])
        outputs = self.linear(pooling.squeeze(dim=2))
        log_probs = F.log_softmax(outputs, dim=1)
        return log_probs
 
#tqdm是一个Pyth模块，能以进度条的方式显示迭代的进度
from tqdm.auto import tqdm
 
#超参数设置
embedding_dim = 128
hidden_dim = 256
num_class = 2
batch_size = 32
num_epoch = 5
#在调用卷积神经网络时，还需要设置两个额外的超参数，分别为filter_size =3（卷积核的大小）和num_tilter =100（卷积核的个数）。
filter_size = 3
num_filter = 100
 
#加载数据
train_data, test_data, vocab = load_sentence_polarity()
train_dataset = CnnDataset(train_data)
test_dataset = CnnDataset(test_data)
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)
 
#加载模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = CNN(len(vocab), embedding_dim, filter_size, num_filter, num_class)
model.to(device) #将模型加载到CPU或GPU设备
 
#训练过程
nll_loss = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器
 
model.train()
for epoch in range(num_epoch):
    total_loss = 0
    for batch in tqdm(train_data_loader, desc=f"Training Epoch {epoch}"):
        inputs, targets = [x.to(device) for x in batch]
        log_probs = model(inputs)
        loss = nll_loss(log_probs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Loss: {total_loss:.2f}")
 
#测试过程
acc = 0
for batch in tqdm(test_data_loader, desc=f"Testing"):
    inputs, targets = [x.to(device) for x in batch]
    with torch.no_grad():
        output = model(inputs)
        acc += (output.argmax(dim=1) == targets).sum().item()
 
#输出在测试集上的准确率
print(f"Acc: {acc / len(test_data_loader):.2f}")

import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
from collections import defaultdict
from vocab import Vocab
from utils import load_sentence_polarity
 
#tqdm是一个Python模块，能以进度条的方式显式迭代的进度
from tqdm.auto import tqdm
 
class LstmDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]
 
#整理函数
def collate_fn(examples):
    #获得每个序列的长度
    lengths = torch.tensor([len(ex[0]) for ex in examples])
    inputs = [torch.tensor(ex[0]) for ex in examples]
    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)
    # 对batch内的样本进行padding，使其具有相同长度
    inputs = pad_sequence(inputs, batch_first=True)
    return inputs, lengths, targets
 
#需要从nn.Module派生一个LSTM子类
class LSTM(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):
        super(LSTM, self).__init__()
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.output = nn.Linear(hidden_dim, num_class)
 
    def forward(self, inputs, lengths):
        embeddings = self.embeddings(inputs)
        #使用 pack_padded_sequence将变长序列打包
        x_pack = pack_padded_sequence(embeddings, lengths, batch_first=True, enforce_sorted=False)
        hidden, (hn, cn) = self.lstm(x_pack)
        outputs = self.output(hn[-1])
        log_probs = F.log_softmax(outputs, dim=-1)
        return log_probs
 
embedding_dim = 128
hidden_dim = 256
num_class = 2
batch_size = 32
num_epoch = 5
 
#加载数据
train_data, test_data, vocab = load_sentence_polarity()
train_dataset = LstmDataset(train_data)
test_dataset = LstmDataset(test_data)
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)
 
#加载模型
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTM(len(vocab), embedding_dim, hidden_dim, num_class)
model.to(device) #将模型加载到GPU中（如果已经正确安装）
 
#训练过程
nll_loss = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器
 
model.train()
for epoch in range(num_epoch):
    total_loss = 0
    for batch in tqdm(train_data_loader, desc=f"Training Epoch {epoch}"):
        inputs, lengths, targets = [x.to(device) for x in batch]
        log_probs = model(inputs, lengths)
        loss = nll_loss(log_probs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Loss: {total_loss:.2f}")
 
#测试过程
acc = 0
for batch in tqdm(test_data_loader, desc=f"Testing"):
    inputs, lengths, targets = [x.to(device) for x in batch]
    with torch.no_grad():
        output = model(inputs, lengths)
        acc += (output.argmax(dim=1) == targets).sum().item()
 
#输出在测试集上的准确率
print(f"Acc: {acc / len(test_data_loader):.2f}")

# Defined in Section 4.6.8
 
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence
from collections import defaultdict
from vocab import Vocab
from utils import load_sentence_polarity, length_to_mask
 
# tqdm是一个Pyth模块，能以进度条的方式显式迭代的进度
from tqdm.auto import tqdm
 
class TransformerDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return len(self.data)
    def __getitem__(self, i):
        return self.data[i]
 
def collate_fn(examples):
    lengths = torch.tensor([len(ex[0]) for ex in examples])
    inputs = [torch.tensor(ex[0]) for ex in examples]
    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)
    # 对batch内的样本进行padding，使其具有相同长度
    inputs = pad_sequence(inputs, batch_first=True)
    return inputs, lengths, targets
 
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=512):
        super(PositionalEncoding, self).__init__()
 
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)
 
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x
 
class Transformer(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class,
                 dim_feedforward=512, num_head=2, num_layers=2, dropout=0.1, max_len=128, activation: str = "relu"):
        super(Transformer, self).__init__()
        # 词嵌入层
        self.embedding_dim = embedding_dim
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.position_embedding = PositionalEncoding(embedding_dim, dropout, max_len)
        # 编码层：使用Transformer
        encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_head, dim_feedforward, dropout, activation)
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
        # 输出层
        self.output = nn.Linear(hidden_dim, num_class)
 
 
    def forward(self, inputs, lengths):
        inputs = torch.transpose(inputs, 0, 1)
        #与LSTM处理情况相同，输入的数据的第一维是批次，需要转换为TransformerEncoder
        #所需要的第1维是长度，第二维是批次的形状
        hidden_states = self.embeddings(inputs)
        hidden_states = self.position_embedding(hidden_states)
        #length_to_mask作用是根据批次中每个序列的长度生成mask矩阵，以便处理长度不一致的序列，忽略掉比较短的序列的无效部分
        #src_key_padding_mask的参数正好与length_to_mask的结果相反（无自注意力的部分为true）
        attention_mask = length_to_mask(lengths.to('cpu')) == False
        #根据批次中的每个序列长度生成mask矩阵
        hidden_states = self.transformer(hidden_states, src_key_padding_mask=attention_mask.to('cuda'))
        hidden_states = hidden_states[0, :, :]
        #取第一个标记的输出结果作为分类层的输入
        output = self.output(hidden_states)
        log_probs = F.log_softmax(output, dim=1)
        return log_probs
 
embedding_dim = 128
hidden_dim = 128
num_class = 2
batch_size = 32
num_epoch = 5
 
# 加载数据
train_data, test_data, vocab = load_sentence_polarity()
train_dataset = TransformerDataset(train_data)
test_dataset = TransformerDataset(test_data)
train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)
 
# 加载模型
device = torch.device('cuda')
model = Transformer(len(vocab), embedding_dim, hidden_dim, num_class)
model.to(device) # 将模型加载到GPU中（如果已经正确安装）
 
# 训练过程
nll_loss = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001) # 使用Adam优化器
 
model.train()
for epoch in range(num_epoch):
    total_loss = 0
    for batch in tqdm(train_data_loader, desc=f"Training Epoch {epoch}"):
        inputs, lengths, targets = [x.to(device) for x in batch]
        log_probs = model(inputs, lengths)
        loss = nll_loss(log_probs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print(f"Loss: {total_loss:.2f}")
 
# 测试过程
acc = 0
for batch in tqdm(test_data_loader, desc=f"Testing"):
    inputs, lengths, targets = [x.to(device) for x in batch]
    with torch.no_grad():
        output = model(inputs, lengths)
        acc += (output.argmax(dim=1) == targets).sum().item()
 
# 输出在测试集上的准确率
print(f"Acc: {acc / len(test_data_loader):.2f}")