题目：Transformer-LSTM-SVM回归

前言一：Transformer

1. Transformer的原理

Transformer是一种基于注意力机制的深度学习模型，最早由Vaswani等人在2017年的论文《Attention Is All You Need》中提出。它主要用于序列建模任务，如:自然语言处理和时间序列预测。

1.1 Transformer的核心结构

Transformer主要由两部分组成：

1. 编码器：处理输入数据，将其转化为高维向量表示。
2. 解码器：根据编码的输出和目标序列生成结果。

解码器
包含$N$层，每一层都由两个子层组成：

1. 多头注意力机制。
2. 前向全连接网络。

使用残差连接和层归一化以提高训练效率。

解码器

包含$N$层，每一层都由两个子层组成：

1. 多头注意力机制。
2. 编码器-解码器注意力机制。
3. 前向全连接网络。

使用残差连接和层归一化以提高训练效率。

1.2 注意力机制

注意力得分计算
$$\text{Attention}\left(Q,K,V\right)=\text{softmax}\left(\frac{Q{K}^T}{\sqrt{d_k}}V\right)$$

• $Q$是查询向量。
• $K$键向量。
• $V$值向量。
• $d_k$:向量维度

多头注意力
多头注意力是将输入分成多组，独立计算每组的注意力，然后拼接在一起：
$$\text{MultiHead}\left(Q,K,V\right)=\text{Concat}\left(hea{d}_1,hea{d}_2,\dots ,hea{d}_n\right)W^O$$
每个头的计算如下:
$$hea{d}_i=\text{Attention}\left(Q{W}_i^Q,K{W}_i^K,V{W}_i^V\right)$$

• $W_i^Q,W_i^K,W_i^V$:学习参数矩阵

1.4 位置编码

由于Transformer没有循环结构，如RNN，无法显试捕获序列顺序信息，因此引入位置编码：
$$PE\left(pos,2i\right)=\text{sin}\left(\frac{pos}{10000^{\frac{2i}{d}}}\right)$$
$$PE\left(pos,2i+1\right)=\text{cos}\left(\frac{pos}{10000^{\frac{2i}{d}}}\right)$$

• $pos$:序列中的位置。
• $d$:嵌入向量的维度。

1.5 损失函数

Transformer常常使用交叉熵损失：
$$\mathcal{L}=-\frac{1}{N}\sum _{i=1}^N\sum _{j=1}^C{y}_{ij}\text{ln}\left(\widehat{y_{ij}}\right)$$

• $y_{ij}$：真实标签。
• $\widehat{y_{ij}}$:模型预测概率。

2. 完整案例

导入包

import torch
import torch.nn as nn
import torch.utils.data as Data
import numpy as np
from torch import optim
import random
from tqdm import *
import matplotlib.pyplot as plt

数据集生成

# 数据集生成
soundmark = ['ei',  'bi:',  'si:',  'di:',  'i:',  'ef',  'dʒi:',  'eit∫',  'ai', 'dʒei', 'kei', 'el', 'em', 'en', 'əu', 'pi:', 'kju:','ɑ:', 'es', 'ti:', 'ju:', 'vi:', 'd∧blju:', 'eks', 'wai', 'zi:']alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']t = 1000 #总条数
r = 0.9   #扰动项
seq_len = 6
src_tokens, tgt_tokens = [],[] #原始序列、目标序列列表for i in range(t):src, tgt = [],[]for j in range(seq_len):ind = random.randint(0,25)src.append(soundmark[ind])if random.random() < r:tgt.append(alphabet[ind])else:tgt.append(alphabet[random.randint(0,25)])src_tokens.append(src)tgt_tokens.append(tgt)
src_tokens[:2], tgt_tokens[:2]

from collections import Counter  # 计数类flatten = lambda l: [item for sublist in l for item in sublist]  # 展平数组
# 构建词表
class Vocab:def __init__(self, tokens):self.tokens = tokens  # 传入的tokens是二维列表self.token2index = {'<pad>': 0, '<bos>': 1, '<eos>': 2, '<unk>': 3}  # 先存好特殊词元# 将词元按词频排序后生成列表self.token2index.update({token: index + 4for index, (token, freq) in enumerate(sorted(Counter(flatten(self.tokens)).items(), key=lambda x: x[1], reverse=True))})# 构建id到词元字典self.index2token = {index: token for token, index in self.token2index.items()}def __getitem__(self, query):# 单一索引if isinstance(query, (str, int)):if isinstance(query, str):return self.token2index.get(query, 3)elif isinstance(query, (int)):return self.index2token.get(query, '<unk>')# 数组索引elif isinstance(query, (list, tuple)):return [self.__getitem__(item) for item in query]def __len__(self):return len(self.index2token)

构造数据集

from torch.utils.data import DataLoader, TensorDataset#实例化source和target词表
src_vocab, tgt_vocab = Vocab(src_tokens), Vocab(tgt_tokens)
src_vocab_size = len(src_vocab)  # 源语言词表大小
tgt_vocab_size = len(tgt_vocab)  # 目标语言词表大小#增加开始标识<bos>和结尾标识<eos>
encoder_input = torch.tensor([src_vocab[line + ['<pad>']] for line in src_tokens])
decoder_input = torch.tensor([tgt_vocab[['<bos>'] + line] for line in tgt_tokens])
decoder_output = torch.tensor([tgt_vocab[line + ['<eos>']] for line in tgt_tokens])# 训练集和测试集比例8比2，batch_size = 16
train_size = int(len(encoder_input) * 0.8)
test_size = len(encoder_input) - train_size
batch_size = 16# 自定义数据集函数
class MyDataSet(Data.Dataset):def __init__(self, enc_inputs, dec_inputs, dec_outputs):super(MyDataSet, self).__init__()self.enc_inputs = enc_inputsself.dec_inputs = dec_inputsself.dec_outputs = dec_outputsdef __len__(self):return self.enc_inputs.shape[0]def __getitem__(self, idx):return self.enc_inputs[idx], self.dec_inputs[idx], self.dec_outputs[idx]train_loader = DataLoader(MyDataSet(encoder_input[:train_size], decoder_input[:train_size], decoder_output[:train_size]), batch_size=batch_size)
test_loader = DataLoader(MyDataSet(encoder_input[-test_size:], decoder_input[-test_size:], decoder_output[-test_size:]), batch_size=1)

位置编码

def get_sinusoid_encoding_table(n_position, d_model):def cal_angle(position, hid_idx):return position / np.power(10000, 2 * (hid_idx // 2) / d_model)def get_posi_angle_vec(position):return [cal_angle(position, hid_j) for hid_j in range(d_model)]sinusoid_table = np.array([get_posi_angle_vec(pos_i) for pos_i in range(n_position)])sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # 偶数位用正弦函数sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # 奇数位用余弦函数return torch.FloatTensor(sinusoid_table)
print(get_sinusoid_encoding_table(30, 512))

掩码操作

# mask掉没有意义的占位符
def get_attn_pad_mask(seq_q, seq_k):                       # seq_q: [batch_size, seq_len] ,seq_k: [batch_size, seq_len]batch_size, len_q = seq_q.size()batch_size, len_k = seq_k.size()pad_attn_mask = seq_k.data.eq(0).unsqueeze(1)          # 判断 输入那些含有P(=0),用1标记 ,[batch_size, 1, len_k]return pad_attn_mask.expand(batch_size, len_q, len_k)# mask掉未来信息
def get_attn_subsequence_mask(seq):                               # seq: [batch_size, tgt_len]attn_shape = [seq.size(0), seq.size(1), seq.size(1)]subsequence_mask = np.triu(np.ones(attn_shape), k=1)          # 生成上三角矩阵,[batch_size, tgt_len, tgt_len]subsequence_mask = torch.from_numpy(subsequence_mask).byte()  #  [batch_size, tgt_len, tgt_len]return subsequence_mask 

注意力计算函数

# 缩放点积注意力计算
class ScaledDotProductAttention(nn.Module):def __init__(self):super(ScaledDotProductAttention, self).__init__()def forward(self, Q, K, V, attn_mask):'''Q: [batch_size, n_heads, len_q, d_k]K: [batch_size, n_heads, len_k, d_k]V: [batch_size, n_heads, len_v(=len_k), d_v]attn_mask: [batch_size, n_heads, seq_len, seq_len]'''scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size, n_heads, len_q, len_k]scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is True.attn = nn.Softmax(dim=-1)(scores)context = torch.matmul(attn, V) # [batch_size, n_heads, len_q, d_v]return context, attn#多头注意力计算
class MultiHeadAttention(nn.Module):def __init__(self):super(MultiHeadAttention, self).__init__()self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=False)self.W_K = nn.Linear(d_model, d_k * n_heads, bias=False)self.W_V = nn.Linear(d_model, d_v * n_heads, bias=False)self.fc = nn.Linear(n_heads * d_v, d_model, bias=False)def forward(self, input_Q, input_K, input_V, attn_mask):'''input_Q: [batch_size, len_q, d_model]input_K: [batch_size, len_k, d_model]input_V: [batch_size, len_v(=len_k), d_model]attn_mask: [batch_size, seq_len, seq_len]'''residual, batch_size = input_Q, input_Q.size(0)# (B, S, D) -proj-> (B, S, D_new) -split-> (B, S, H, W) -trans-> (B, H, S, W)Q = self.W_Q(input_Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # Q: [batch_size, n_heads, len_q, d_k]K = self.W_K(input_K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # K: [batch_size, n_heads, len_k, d_k]V = self.W_V(input_V).view(batch_size, -1, n_heads, d_v).transpose(1,2) # V: [batch_size, n_heads, len_v(=len_k), d_v]attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size, n_heads, seq_len, seq_len]# context: [batch_size, n_heads, len_q, d_v], attn: [batch_size, n_heads, len_q, len_k]context, attn = ScaledDotProductAttention()(Q, K, V, attn_mask)context = context.transpose(1, 2).reshape(batch_size, -1, n_heads * d_v) # context: [batch_size, len_q, n_heads * d_v]output = self.fc(context) # [batch_size, len_q, d_model]return nn.LayerNorm(d_model)(output + residual), attn

前馈神经网络

class PoswiseFeedForwardNet(nn.Module):def __init__(self):super(PoswiseFeedForwardNet, self).__init__()self.fc = nn.Sequential(nn.Linear(d_model, d_ff, bias=False),nn.ReLU(),nn.Linear(d_ff, d_model, bias=False))def forward(self, inputs):                             # inputs: [batch_size, seq_len, d_model]residual = inputsoutput = self.fc(inputs)return nn.LayerNorm(d_model)(output + residual)   # 残差 + LayerNorm

编码器与解码器

# 编码器层
class EncoderLayer(nn.Module):def __init__(self):super(EncoderLayer, self).__init__()self.enc_self_attn = MultiHeadAttention()  # 多头注意力self.pos_ffn = PoswiseFeedForwardNet()  # 前馈网络def forward(self, enc_inputs, enc_self_attn_mask):'''enc_inputs: [batch_size, src_len, d_model]enc_self_attn_mask: [batch_size, src_len, src_len]'''# enc_outputs: [batch_size, src_len, d_model], attn: [batch_size, n_heads, src_len, src_len]enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,Venc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]return enc_outputs, attn# 编码器模块
class Encoder(nn.Module):def __init__(self):super(Encoder, self).__init__()self.src_emb = nn.Embedding(src_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_vocab_size, d_model), freeze=True)self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])def forward(self, enc_inputs):'''enc_inputs: [batch_size, src_len]'''word_emb = self.src_emb(enc_inputs) # [batch_size, src_len, d_model]pos_emb = self.pos_emb(enc_inputs) # [batch_size, src_len, d_model]enc_outputs = word_emb + pos_embenc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs) # [batch_size, src_len, src_len]enc_self_attns = []for layer in self.layers:# enc_outputs: [batch_size, src_len, d_model], enc_self_attn: [batch_size, n_heads, src_len, src_len]enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)enc_self_attns.append(enc_self_attn)return enc_outputs, enc_self_attns

# 解码器层
class DecoderLayer(nn.Module):def __init__(self):super(DecoderLayer, self).__init__()self.dec_self_attn = MultiHeadAttention()self.dec_enc_attn = MultiHeadAttention()self.pos_ffn = PoswiseFeedForwardNet()def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):'''dec_inputs: [batch_size, tgt_len, d_model]enc_outputs: [batch_size, src_len, d_model]dec_self_attn_mask: [batch_size, tgt_len, tgt_len]dec_enc_attn_mask: [batch_size, tgt_len, src_len]'''# dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len]dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)# dec_outputs: [batch_size, tgt_len, d_model], dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)dec_outputs = self.pos_ffn(dec_outputs) # [batch_size, tgt_len, d_model]return dec_outputs, dec_self_attn, dec_enc_attn# 解码器模块
class Decoder(nn.Module):def __init__(self):super(Decoder, self).__init__()self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_vocab_size, d_model),freeze=True)self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])def forward(self, dec_inputs, enc_inputs, enc_outputs):'''dec_inputs: [batch_size, tgt_len]enc_intpus: [batch_size, src_len]enc_outputs: [batsh_size, src_len, d_model]'''word_emb = self.tgt_emb(dec_inputs) # [batch_size, tgt_len, d_model]pos_emb = self.pos_emb(dec_inputs) # [batch_size, tgt_len, d_model]dec_outputs = word_emb + pos_embdec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs) # [batch_size, tgt_len, tgt_len]dec_self_attn_subsequent_mask = get_attn_subsequence_mask(dec_inputs) # [batch_size, tgt_len]dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0) # [batch_size, tgt_len, tgt_len]dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs) # [batc_size, tgt_len, src_len]dec_self_attns, dec_enc_attns = [], []for layer in self.layers:# dec_outputs: [batch_size, tgt_len, d_model], dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [batch_size, h_heads, tgt_len,src_len]dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)dec_self_attns.append(dec_self_attn)dec_enc_attns.append(dec_enc_attn)return dec_outputs, dec_self_attns, dec_enc_attns

transformer模型

class Transformer(nn.Module):def __init__(self):super(Transformer, self).__init__()self.encoder = Encoder()self.decoder = Decoder()self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)def forward(self, enc_inputs, dec_inputs):'''enc_inputs: [batch_size, src_len]dec_inputs: [batch_size, tgt_len]'''# tensor to store decoder outputs# outputs = torch.zeros(batch_size, tgt_len, tgt_vocab_size).to(self.device)# enc_outputs: [batch_size, src_len, d_model], enc_self_attns: [n_layers, batch_size, n_heads, src_len, src_len]enc_outputs, enc_self_attns = self.encoder(enc_inputs)# dec_outpus: [batch_size, tgt_len, d_model], dec_self_attns: [n_layers, batch_size, n_heads, tgt_len, tgt_len], dec_enc_attn: [n_layers, batch_size, tgt_len, src_len]dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)dec_logits = self.projection(dec_outputs) # dec_logits: [batch_size, tgt_len, tgt_vocab_size]return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns

模型训练

d_model = 512   # 字 Embedding 的维度
d_ff = 2048     # 前向传播隐藏层维度
d_k = d_v = 64  # K(=Q), V的维度 
n_layers = 6    # 有多少个encoder和decoder
n_heads = 8     # Multi-Head Attention设置为8
num_epochs = 50 # 训练50轮
# 记录损失变化
loss_history = []model = Transformer()
criterion = nn.CrossEntropyLoss(ignore_index=0)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.99)for epoch in tqdm(range(num_epochs)):total_loss = 0for enc_inputs, dec_inputs, dec_outputs in train_loader:'''enc_inputs: [batch_size, src_len]dec_inputs: [batch_size, tgt_len]dec_outputs: [batch_size, tgt_len]'''# enc_inputs, dec_inputs, dec_outputs = enc_inputs.to(device), dec_inputs.to(device), dec_outputs.to(device)# outputs: [batch_size * tgt_len, tgt_vocab_size]outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)loss = criterion(outputs, dec_outputs.view(-1))optimizer.zero_grad()loss.backward()optimizer.step()total_loss += loss.item()avg_loss = total_loss/len(train_loader)loss_history.append(avg_loss)print('Epoch:', '%d' % (epoch + 1), 'loss =', '{:.6f}'.format(avg_loss))

LSTM

SVM