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NLP之基于Transformer的句子翻译
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Transformer
目录
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Transformer
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1.理论
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1.1 Model Structure
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1.2 Multi-Head Attention & Scaled Dot-Product Attention
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2.实验
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2.1 束搜索
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2.2 Issue
1.理论
1.1 Model Structure
目录
-
Transformer
-
1.理论
- 1.1 Model Structure
- 1.2 Multi-Head Attention & Scaled Dot-Product Attention
-
-
2.实验
- 2.1 束搜索
- 2.2 Issue
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1.理论
1.1 Model Structure
1.2 Multi-Head Attention & Scaled Dot-Product Attention
2.实验
2.1 束搜索
2.1 束搜索
束搜索过程示意图:
2.2 Issue
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贪婪搜索和束搜索
贪婪搜索和束搜索都是针对多个时间步,每一轮都要比较概率大小的,因此所有预测生成1个单词或者进行单词翻译的
都谈不上贪婪搜索和束搜索(没有多个时间步),直接用predict=model(inputs)的也谈不上贪婪搜索和束搜索(没有每一轮比较概率大小).
对于Seq2Seq和采用了序列模型的transformer来说,贪婪搜索和束搜索都应该用预测的单词覆盖填充的'SPPPP'中的'P'-
对于翻译多个单词的任务,应该对于每个生成的单词设置循环
- 贪婪搜索:每个时间步生成一个单词的概率分布,取最大值,然后把这个值传给进行下一时间步,最后生成所有单词
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束搜索(k=3):在每个时间步上预测k个max单词然后把这两个单词分别作为值传给进行下一时间步,
当然,这样会进行\(k^T\)次预测,存储\(k^T\)个输出,最后取总概率最高的.
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对于预测生成多个单词的任务,应该对于每个生成的单词设置循环
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贪婪搜索:在输入时间步之后,每个输出时间步生成一个单词的概率分布,取最大值,
然后把这个值传给进行下一时间步,最后生成所有单词 -
束搜索(k=3):在输入时间步之后,每个输出时间步上预测k个max单词,然后把这两个单词分别作为值
传给进行下一时间步,当然,这样会进行\(k^T\)次预测,存储\(k^T\)个输出,最后取总概率最高的.
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贪婪搜索:在输入时间步之后,每个输出时间步生成一个单词的概率分布,取最大值,
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对于翻译多个单词的任务,应该对于每个生成的单词设置循环
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为什么Seq2Seq和基于序列模型的transformer直接用predict=model(enc_inputs, dec_inputs='SPPPPP')的效果不好,其中transformer效果尤其差,而其他模型还不错?
直接用predict=model(enc_inputs, dec_inputs='SPPPPP')既不算贪婪搜索也不算束搜索,因为这样只在最后才比较概率大小,而贪婪搜索和束搜索每轮都要计算-
RNN/LSTM等模型不需要'SPPPP'填充,因此不会受到空白信息影响,可以直接生成,
而Seq2Seq/transformer会受到'SPPPP'影响,所以效果不好. -
其中,序列模型和RNN/LSTM类似,一个时间步对应一个单词,一个decoder时间步对应初始输入为'P',
上一次的时间步输出可以影响下一时间步生成,所以效果不好不坏 - 而transformer每次输入输出都是以整个句子为单位,所以不存在上一次的时间步输出可以影响下一时间步生成,所以效果尤其差,必须用循环依次生成
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RNN/LSTM等模型不需要'SPPPP'填充,因此不会受到空白信息影响,可以直接生成,
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为什么束搜索中有时候其他句子总体评价更高?
模型过于复杂,训练样本太少,导致过拟合.
"""
Task: 基于Transformer的句子翻译
Author: ChengJunkai @github.com/Cheng0829
Email: chengjunkai829@gmail.com
Date: 2022/09/17
Reference: Tae Hwan Jung(Jeff Jung) @graykode
"""
import numpy as np
import torch, time, itertools, os, sys
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
# S: 表示开始进行解码输入的符号.
# E: 表示结束进行解码输出的符号.
# P: 当前批次数据大小小于时间步长时将填充空白序列的符号
'''1.数据预处理'''
def pre_process(sentences):
# P在第一个,方便处理
src_sequence = ['P']
src_sequence.extend(sentences[0].split())
src_list = []
'''
如果用list(set(word_sequence))来去重,得到的将是一个随机顺序的列表(因为set无序),
这样得到的字典不同,保存的上一次训练的模型很有可能在这一次不能用
(比如上一次的模型预测碰见i:0,love:1,就输出you:2,但这次模型you在字典3号位置,也就无法输出正确结果)
'''
for word in src_sequence:
if word not in src_list:
src_list.append(word)
src_dict = {w:i for i,w in enumerate(src_list)}
src_dict_size = len(src_dict)
src_len = len(sentences[0].split()) # length of source
# P在第一个,方便处理
tgt_sequence = ['P']
tgt_sequence.extend(sentences[1].split()+sentences[2].split())
tgt_list = []
'''
如果用list(set(word_sequence))来去重,得到的将是一个随机顺序的列表(因为set无序),
这样得到的字典不同,保存的上一次训练的模型很有可能在这一次不能用
(比如上一次的模型预测碰见i:0,love:1,就输出you:2,但这次模型you在字典3号位置,也就无法输出正确结果)
'''
for word in tgt_sequence:
if word not in tgt_list:
tgt_list.append(word)
tgt_dict = {w:i for i,w in enumerate(tgt_list)}
number_dict = {i:w for i,w in enumerate(tgt_dict)}
tgt_dict_size = len(tgt_dict)
tgt_len = len(sentences[1].split()) # length of target
return src_dict,src_dict_size,tgt_dict,number_dict,tgt_dict_size,src_len,tgt_len
'''根据句子数据,构建词元的输入向量'''
def make_batch(sentences):
input_batch = [[src_dict[n] for n in sentences[0].split()]]
output_batch = [[tgt_dict[n] for n in sentences[1].split()]]
target_batch = [[tgt_dict[n] for n in sentences[2].split()]]
input_batch = torch.LongTensor(np.array(input_batch)).to(device)
output_batch = torch.LongTensor(np.array(output_batch)).to(device)
target_batch = torch.LongTensor(np.array(target_batch)).to(device)
# print(input_batch, output_batch,target_batch) # tensor([[0, 1, 2, 3, 4, 5]]) tensor([[3, 1, 0, 2, 4]]) tensor([[1, 0, 2, 4, 5]])
return input_batch, output_batch,target_batch
def get_position_encoding_table(n_position, d_model):
# inputs: (src_len+1, d_model) or (tgt_len+1, d_model)
pos_table = np.zeros((n_position, d_model))
for pos in range(n_position):
for i in range(d_model):
tmp = pos / np.power(10000, 2*(i//2) / d_model)
if i % 2 == 0:
# 偶数为正弦
pos_table[pos][i] = np.sin(tmp) # (7 or 6, 512)
else:
# 奇数为余弦
pos_table[pos][i] = np.cos(tmp) # (7 or 6, 512)
return torch.FloatTensor(pos_table).to(device)
def get_attn_pad_mask(seq_q, seq_k, dict):
'''mask大小和(len_q,len_k)一致,
是为了在点积注意力中,与torch.matmul(Q,K)的大小一致'''
# (seq_q, seq_k): (dec_inputs, enc_inputs)
# dec_inputs:[batch_size, tgt_len] # [1,5]
# enc_inputs:[batch_size, src_len] # [1,6]
batch_size, len_q = seq_q.size() # 1,5
batch_size, len_k = seq_k.size() # 1,6
"""Tensor.data.eq(element)
eq即equal,对Tensor中所有元素进行判断,和element相等即为True,否则为False,返回二值矩阵
Examples:
>>> tensor = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])
>>> tensor.data.eq(1)
tensor([[ True, False, False],
[False, False, False]])
"""
# eq(zero) is PAD token
pad_attn_mask = seq_k.data.eq(dict['P']).unsqueeze(1) # 升维 enc: [1,6] -> [1,1,6]
# 矩阵扩充: enc: pad_attn_mask: [1,1,6] -> [1,5,6]
return pad_attn_mask.expand(batch_size, len_q, len_k) # batch_size, len_q, len_k
'''Attention = Softmax(Q * K^T) * V '''
def Scaled_Dot_Product_Attention(Q, K, V, attn_mask):
# Q_s: [batch_size, n_heads, len_q, d_k] # [1,8,5,64]
# K_s: [batch_size, n_heads, len_k, d_k] # [1,8,6,64]
# attn_mask: [batch_size, n_heads, len_q, len_k] # [1,8,5,6]
"""torch.matmul(Q, K)
torch.matmul是tensor的乘法,输入可以是高维的.
当输入是都是二维时,就是普通的矩阵乘法.
当输入有多维时,把多出的一维作为batch提出来,其他部分做矩阵乘法.
Exeamples:
>>> a = torch.ones(3,4)
>>> b = torch.ones(4,2)
>>> torch.matmul(a,b).shape
torch.Size([3,2])
>>> a = torch.ones(5,3,4)
>>> b = torch.ones(4,2)
>>> torch.matmul(a,b).shape
torch.Size([5,3,2])
>>> a = torch.ones(2,5,3)
>>> b = torch.ones(1,3,4)
>>> torch.matmul(a,b).shape
torch.Size([2,5,4])
"""
# [1,8,5,64] * [1,8,64,6] -> [1,8,5,6]
# scores : [batch_size, n_heads, len_q, len_k]
scores = torch.matmul(Q, K.transpose(2,3)) / np.sqrt(d_k) # divided by scale
"""scores.masked_fill_(attn_mask, -1e9)
由于scores和attn_mask维度相同,根据attn_mask中的元素值,把和attn_mask中值为True的元素的
位置相同的scores元素的值赋为-1e9
"""
scores.masked_fill_(attn_mask, -1e9)
# 'P'的scores元素值为-1e9, softmax值即为0
softmax = nn.Softmax(dim=-1) # 求行的softmax
attn = softmax(scores) # [1,8,6,6]
# [1,8,6,6] * [1,8,6,64] -> [1,8,6,64]
context = torch.matmul(attn, V) # [1,8,6,64]
return context, attn
class MultiHeadAttention(nn.Module):
# dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)
def __init__(self):
super().__init__()
self.W_Q = nn.Linear(d_model, d_k*n_heads) # (512, 64*8) # d_q必等于d_k
self.W_K = nn.Linear(d_model, d_k*n_heads) # (512, 64*8) # 保持维度不变
self.W_V = nn.Linear(d_model, d_v*n_heads) # (512, 64*8)
self.linear = nn.Linear(n_heads*d_v, d_model)
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, Q, K, V, attn_mask):
# dec_outputs: [batch_size, tgt_len, d_model] # [1,5,512]
# enc_outputs: [batch_size, src_len, d_model] # [1,6,512]
# dec_enc_attn_mask: [batch_size, tgt_len, src_len] # [1,5,6]
# q/k/v: [batch_size, len_q/k/v, d_model]
residual, batch_size = Q, len(Q)
'''用n_heads=8把512拆成64*8,在不改变计算成本的前提下,让各注意力头相互独立,更有利于学习到不同的特征'''
# Q_s: [batch_size, len_q, n_heads, d_q] # [1,5,8,64]
# new_Q_s: [batch_size, n_heads, len_q, d_q] # [1,8,5,64]
Q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2)
# K_s: [batch_size, n_heads, len_k, d_k] # [1,8,6,64]
K_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2)
# V_s: [batch_size, n_heads, len_k, d_v] # [1,8,6,64]
V_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2)
# attn_mask : [1,5,6] -> [1,1,5,6] -> [1,8,5,6]
# attn_mask : [batch_size, n_heads, len_q, len_k]
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)
# context: [batch_size, n_heads, len_q, d_v]
# attn: [batch_size, n_heads, len_q(=len_k), len_k(=len_q)]
# context: [1,8,5,64] attn: [1,8,5,6]
context, attn = Scaled_Dot_Product_Attention(Q_s, K_s, V_s, attn_mask)
"""contiguous() 连续的
contiguous: view只能用在连续(contiguous)的变量上.
如果在view之前用了transpose, permute等,
需要用contiguous()来返回一个contiguous copy
"""
# context: [1,8,5,64] -> [1,5,512]
context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v)
# context: [1,5,512] -> [1,5,512]
output = self.linear(context)
"""nn.LayerNorm(output) 样本归一化
和对所有样本的某一特征进行归一化的BatchNorm不同,
LayerNorm是对每个样本进行归一化,而不是一个特征
Tips:
归一化Normalization和Standardization标准化区别:
Normalization(X[i]) = (X[i] - np.min(X)) / (np.max(X) - np.min(X))
Standardization(X[i]) = (X[i] - np.mean(X)) / np.var(X)
"""
output = self.layer_norm(output + residual)
return output, attn
class Position_wise_Feed_Forward_Networks(nn.Module):
def __init__(self):
super().__init__()
'''输出层相当于1*1卷积层,也就是全连接层'''
"""nn.Conv1d
in_channels应该理解为嵌入向量维度,out_channels才是卷积核的个数(厚度)
"""
# 512 -> 2048
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
# 2048 -> 512
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, inputs):
# enc_outputs: [batch_size, source_len, d_model] # [1,6,512]
residual = inputs
relu = nn.ReLU()
# output: 512 -> 2048 [1,2048,6]
output = relu(self.conv1(inputs.transpose(1, 2)))
# output: 2048 -> 512 [1,6,512]
output = self.conv2(output).transpose(1, 2)
return self.layer_norm(output + residual)
class EncoderLayer(nn.Module):
def __init__(self):
super(EncoderLayer, self).__init__()
self.enc_attn = MultiHeadAttention()
self.pos_ffn = Position_wise_Feed_Forward_Networks()
def forward(self, enc_outputs, enc_attn_mask):
# enc_attn_mask: [1,6,6]
# enc_outputs to same Q,K,V
# enc_outputs: [batch_size, source_len, d_model] # [1, 6, 512]
enc_outputs, attn = self.enc_attn(enc_outputs, \
enc_outputs, enc_outputs, enc_attn_mask)
# enc_outputs: [batch_size , len_q , d_model]
enc_outputs = self.pos_ffn(enc_outputs)
return enc_outputs, attn
class DecoderLayer(nn.Module):
def __init__(self):
super().__init__()
self.dec_attn = MultiHeadAttention()
self.dec_enc_attn = MultiHeadAttention()
self.pos_ffn = Position_wise_Feed_Forward_Networks()
def forward(self, dec_outputs, enc_outputs, dec_attn_mask, dec_enc_attn_mask):
dec_outputs, dec_attn = \
self.dec_attn(dec_outputs, dec_outputs, dec_outputs, dec_attn_mask)
# dec_outputs: [1, 5, 512] dec_enc_attn: [1, 8, 5, 6]
dec_outputs, dec_enc_attn = \
self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)
# 相当于两个全连接层 512 -> 2048 -> 512
dec_outputs = self.pos_ffn(dec_outputs)
return dec_outputs, dec_attn, dec_enc_attn
class Encoder(nn.Module):
def __init__(self):
super().__init__()
# 输入向量的嵌入矩阵
self.src_emb = nn.Embedding(src_dict_size, d_model).to(device)
# 6个编码层
self.layers = nn.ModuleList([EncoderLayer().to(device) for _ in range(n_layers)])
def forward(self, enc_inputs): # enc_inputs: [batch_size, source_len] # [1, 6]
input = [src_dict[i] for i in sentences[0].split()] # [0,1,2,3,4,5]
'''加入pos_emb的意义: 如果不加,所有位置的单词都将有完全相同的影响,体现不出序列的特点'''
# 可学习的输入向量嵌入矩阵 + 不可学习的序列向量矩阵
# enc_outputs: [1, 6, 512]
'''embbeding和linear不同,emb之后会加一个维度'''
enc_outputs = self.src_emb(enc_inputs) + position_encoding[input]
# 屏蔽P,返回一个 [batch_size,src_len,src_len]=[1,6,6]的二值矩阵
enc_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs, src_dict)
enc_attns = []
for layer in self.layers:
# enc_outputs既是输入,也是输出
enc_outputs, enc_attn = layer(enc_outputs, enc_attn_mask)
enc_attns.append(enc_attn)
return enc_outputs, enc_attns
class Decoder(nn.Module):
def __init__(self):
super().__init__()
self.tgt_emb = nn.Embedding(tgt_dict_size, d_model)
self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])
def forward(self, dec_inputs, enc_inputs, enc_outputs):
# dec_inputs : [batch_size, target_len] [1,5]
input = [tgt_dict[i] for i in sentences[1].split()]
dec_outputs = self.tgt_emb(dec_inputs)
# 为输入句子的每一个单词(不去重)加入序列信息
# dec_outputs: [1, 5, 512]
dec_outputs = dec_outputs + position_encoding[input]
# 屏蔽pad字符,返回一个 [batch_size,tgt_len,tgt_len]=[1,5,5]的二值矩阵
dec_attn_mask = get_attn_pad_mask(dec_inputs, dec_inputs, tgt_dict)
# 屏蔽pad字符和之后时刻的信息
for i in range(0, len(dec_inputs[0])):
for j in range(i+1, len(dec_inputs[0])):
dec_attn_mask[0][i][j] = True # 使softmax值为0
# 第二个多注意力机制,输入来自encoder和decoder 屏蔽encoder中的pad字符
dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs, src_dict) # [1,5,6]
dec_attns, dec_enc_attns = [], []
for layer in self.layers:
dec_outputs, dec_attn, dec_enc_attn = \
layer(dec_outputs, enc_outputs, dec_attn_mask, dec_enc_attn_mask)
dec_attns.append(dec_attn)
dec_enc_attns.append(dec_enc_attn)
return dec_outputs, dec_attns, dec_enc_attns
'''2.构建Transformer模型'''
class Transformer(nn.Module):
def __init__(self):
super().__init__()
self.encoder = Encoder()
self.decoder = Decoder()
self.projection = nn.Linear(d_model, tgt_dict_size, bias=False)
def forward(self, dec_inputs, enc_inputs):
enc_outputs, enc_attns = self.encoder(enc_inputs)
dec_outputs, dec_attns, dec_enc_attns \
= self.decoder(dec_inputs, enc_inputs, enc_outputs)
# model_outputs: [batch_size, tgt_len, tgt_dict_size]
model_outputs = self.projection(dec_outputs) # [1,5,7]
model_outputs = model_outputs.squeeze(0)
return model_outputs, enc_attns, dec_attns, dec_enc_attns
'''贪婪搜索'''
def Greedy_Search(model, enc_inputs, start_symbol): # start_symbol = tgt_dict['S']
'''
依次生成tgt_len个目标单词,每生成一个单词都要进行一次完整的transformer计算,
最后依次取第i个位置上概率最大的单词,生成新的dec_inputs
贪婪搜索输出之后,新的dec_inputs重新和其他参数输入模型,最后生成predict
'''
padding_inputs = 'S' + ' P' * (tgt_len-1)
# [1,5]
dec_inputs = torch.Tensor([[tgt_dict[i] for i in padding_inputs.split()]]).to(device).type_as(enc_inputs.data)
next_symbol = start_symbol
i = 0
while True:
'''
由enc_inputs和'S P P P P'生成i,然后i赋值给下一轮的dec_inputs:'S i P P P'
然后生成want->'S i want P P' ······,生成beer->'S i want a beer'
其实就是生成target_batch,然后错位赋值给dec_inputs,这个刚好符合训练模型时
target_batch对应sentence[2],dec_inputs对应的sentence[1],enc_inputs对应的sentence[0]
'''
dec_inputs[0][i] = next_symbol
outputs, _, _, _ = model(dec_inputs, enc_inputs)
predict = outputs.squeeze(0).max(dim=-1, keepdim=False)[1]
next_symbol = predict[i].item()
i = i + 1
# 超过最大长度或者有终止符则退出循环
if next_symbol == tgt_dict['E'] or i > max_len:
break
outputs, _, _, _ = model(dec_inputs, enc_inputs)
predict = outputs.squeeze(0).max(dim=-1, keepdim=False)[1]
return predict
# 为束搜索构造连续重复矩阵
def repeat(inputs,k=2):
"""连续重复
Examples:
>>> a = torch.tensor([[1,2,3],[4,5,6]])
>>> print(repeat(a))
[[1,2,3],
[1,2,3],
[4,5,6],
[4,5,6]]
>>> import torch
>>> print(torch.repeat(a))
[[1,2,3],
[4,5,6],
[1,2,3],
[4,5,6]]
"""
tmp = torch.zeros(inputs.size(0)*k, inputs.size(1), inputs.size(2)).type_as(inputs.data)
pos = 0
while pos < len(inputs):
tmp[pos*k] = inputs[pos]
for i in range(1,k):
tmp[pos*k+i] = inputs[pos]
pos = pos + 1
return tmp
'''束搜索'''
def Beam_Search(model, enc_inputs, start_symbol, k=2):
padding_inputs = 'S' + (tgt_len-1)*' P'
# dec_inputs:[1,5]
dec_inputs = torch.Tensor([[tgt_dict[i] for i in padding_inputs.split()]]).to(device).type_as(enc_inputs.data)
# all_dec_inputs用来存储每一种dec_inputs
# all_dec_inputs:[1,1,5]
all_dec_inputs = dec_inputs.unsqueeze(0)
# 由于第一个单词被定为'S',所以其实每轮生成的实际结果数是2,4,8,16,16(最后一轮复制为32,但因为马上就结束循环,不会再分别赋值)
for i in range(tgt_len):
# 每一个单词,all_dec_inputs都要重复k次,存放新生成的k**i个结果
# 注意,repeat函数实现的是连续重复,而不是toch.repeat那样的整体间断重复
all_dec_inputs = repeat(all_dec_inputs,k)
# 对于第i个单词,以步长k遍历all_dec_inputs(即前一轮的所有dec_inputs)
# 分别将其作为模型输入
for j in range(0,len(all_dec_inputs),k):
# print(j)
dec_inputs = all_dec_inputs[j]
outputs, _, _, _ = model(dec_inputs, enc_inputs)
# 排序,得到索引
indices = outputs.sort(descending=True).indices # [5,7]
# 提取第i个单词的第k个可能,赋值给all_dec_inputs的第j+pos个样本
# (因为连续重复,所以all_dec_inputs中第j个dec_inputs生成的
# 输出就分布在j~j+k个dec_inputs)
for pos in range(k):
if i < tgt_len - 1:
# i+1表示留给下一轮用,这和贪婪搜索中的思想一致
all_dec_inputs[j+pos][0][i+1] = indices[i][pos]
print(all_dec_inputs)
'''评判所有dec_inputs,选出输出概率最大的'''
result = []
for dec_inputs in all_dec_inputs:
sum = 0
outputs, _, _, _ = model(dec_inputs, enc_inputs)
indexs = outputs.squeeze(0).max(dim=-1, keepdim=False)[1]
for i in range(tgt_len):
# 计算各个样本输出的单词概率之和
sum = sum + outputs.data[i][indexs[i]]
result.append(sum.item())
'''可能过拟合输出错误解'''
max_index = result.index(max(result))
predict = all_dec_inputs[max_index]
return predict
if __name__ == '__main__':
chars = 30 * '*'
# sentences = ['ich mochte ein bier P', 'S i want a beer', 'i want a beer E']
sentences = ['我 要 喝 啤 酒 P', 'S i want a beer', 'i want a beer E']
device = ['cuda:0' if torch.cuda.is_available() else 'cpu'][0]
d_model = 512 # Embedding Size 嵌入向量维度
d_ff = 2048 # FeedForward dimension
d_k = d_v = 64 # dimension of K(=Q), V
n_layers = 6 # number of Encoder of Decoder Layer 编解码器层数
n_heads = 8 # number of heads in Multi-Head Attention 多注意力机制头数
max_len = 5
'''1.数据预处理'''
src_dict,src_dict_size,tgt_dict,number_dict,tgt_dict_size,src_len,tgt_len = pre_process(sentences)
position_encoding = get_position_encoding_table(max(src_dict_size, tgt_dict_size), d_model)
enc_inputs, dec_inputs, target_batch = make_batch(sentences)
'''2.构建模型'''
model = Transformer()
model.to(device)
criterion = nn.CrossEntropyLoss()
# Adam的效果非常不好
optimizer = optim.SGD(model.parameters(), lr=0.001)
if os.path.exists('model_param.pt') == True:
# 加载模型参数到模型结构
model.load_state_dict(torch.load('model_param.pt', map_location=device))
'''3.训练'''
print('{}\nTrain\n{}'.format('*'*30, '*'*30))
loss_record = []
for epoch in range(1000):
optimizer.zero_grad()
outputs, enc_attns, dec_attns, dec_enc_attns \
= model(dec_inputs, enc_inputs)
outputs = outputs.to(device)
loss = criterion(outputs, target_batch.contiguous().view(-1))
loss.backward()
optimizer.step()
if loss >= 0.01: # 连续30轮loss小于0.01则提前结束训练
loss_record = []
else:
loss_record.append(loss.item())
if len(loss_record) == 30:
torch.save(model.state_dict(), 'model_param.pt')
break
if (epoch + 1) % 100 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'Loss = {:.6f}'.format(loss))
torch.save(model.state_dict(), 'model_param.pt')
'''4.测试'''
print('{}\nTest\n{}'.format('*'*30, '*'*30))
# 贪婪算法
predict = Greedy_Search(model, enc_inputs, start_symbol=tgt_dict["S"])
# 束搜索算法
predict = Beam_Search(model, enc_inputs, start_symbol=tgt_dict["S"])
for word in sentences[0].split():
if word not in ['S', 'P', 'E']:
print(word, end='')
print(' ->',end=' ')
for i in predict.data.squeeze():
if number_dict[int(i)] not in ['S', 'P', 'E'] :
print(number_dict[int(i)], end=' ')
