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pytorch记录：seq2seq例子看看这torch怎么玩的

發(fā)布時(shí)間：2023/11/28 生活经验 27 豆豆

生活随笔收集整理的這篇文章主要介紹了 pytorch记录：seq2seq例子看看这torch怎么玩的小編覺得挺不錯(cuò)的,現(xiàn)在分享給大家,幫大家做個(gè)參考.

https://blog.csdn.net/nockinonheavensdoor/article/details/82320580

先看看簡單例子：

import torch
import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim torch.manual_seed(1)

用torch.tensor讓list成為tensor：

# Create a 3D tensor of size 2x2x2.
T_data = [[[1., 2.], [3., 4.]],[[5., 6.], [7., 8.]]]
T = torch.tensor(T_data)
print(T)

自動求導(dǎo)設(shè)requires_grad=True:

# Computation Graphs and Automatic Differentiation
x = torch.tensor([1., 2., 3], requires_grad=True) y = torch.tensor([4., 5., 6], requires_grad=True) z = x + y print(z) print(z.grad_fn) tensor([ 5., 7., 9.]) <AddBackward1 object at 0x00000247781E0BE0>

detach()方法獲取z的值，但是不能對獲取后的值求導(dǎo)了。

new_z = z.detach()
print(new_z.grad_fn)None

好了，重點(diǎn)來了

Translation with a Sequence to Sequence Network and Attention

from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata import string import re import random import torch import torch.nn as nn from torch import optim import torch.nn.functional as F device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

準(zhǔn)備數(shù)據(jù)：

SOS_token = 0
EOS_token = 1class lang: def __init__(self, name): self.name = name self.word2index = {} self.word2count = {} self.index2word = {0:'SOS', 1:'EOS'} self.n_words = 2 # Count SOS and EOS def addSentence(self, sentence): for word in sentence.split(): self.addWord(word) def addWord(self, word): if word not in self.word2index: self.word2index[word] = self.n_words self.word2count[word] = 1 self.index2word[self.n_words] = word self.n_words += 1 else: self.word2count[word] += 1

Unicode字符轉(zhuǎn)為ASCII,用小寫字母表示一切，去掉標(biāo)點(diǎn)符號：

# Turn a Unicode string to plain ASCII, thanks to
# http://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' ) # Lowercase,trim,remove non-letter characters #re.sub(pattern, repl, string, count=0, flags=0) def normalizeString(s): s = unicodeToAscii(s.lower().strip()) # (re) 匹配括號內(nèi)的表達(dá)式，也表示一個(gè)組 # [...] 用來表示一組字符,單獨(dú)列出：[amk] 匹配 'a'，'m'或'k' # \1...\9 匹配第n個(gè)分組的內(nèi)容。 s = re.sub(r"([.!?])", r"\1", s) # [^...] 不在[]中的字符：[^abc] 匹配除了a,b,c之外的字符。 s = re.sub(r"[^a-zA-Z.!?]+",r" ", s) return s

繼續(xù)：

# 文件用的英語到其他語言，用reverse標(biāo)志置換一對這樣的數(shù)據(jù)。
def readlangs(lang1, lang2, reverse= False): print("Reading lines...") #Read the file and split into lines lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\ read().strip().split('\n') # Split every line into pairs and normalize pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines] # Reverse pairs, make lang instances if reverse: pairs = [list(reversed(p)) for p in pairs] input_lang = lang(lang2) output_lang = lang(lang1) else: input_lang = lang(lang1) output_lang = lang(lang2) return input_lang, output_lang, pairs

過濾出部分樣本：


MAX_LENGTH = 10eng_prefixes = ("i am ", "i m ","he is", "he s ", "she is", "she s", "you are", "you re ", "we are", "we re ", "they are", "they re " ) def filterPair(p): return len(p[0].split(' ')) < MAX_LENGTH and \ len(p[1].split(' ')) < MAX_LENGTH and \ p[1].startswith(eng_prefixes) def filterPairs(pairs): return [ pair for pair in pairs if filterPair(pair)]

The full process for preparing the data is:
- Read text file and split into lines, split lines into pairs
- Normalize text, filter by length and content
- Make word lists from sentences in pairs

def prepareData(lang1, lang2, reverse= False):input_lang, output_lang, pairs = readlangs(lang1,lang2,reverse)print("Read %s sentence pairs " % len(pairs)) pairs = filterPairs(pairs) print("Trimmed to %s sentence pairs " % len(pairs)) print("Counting words...") for pair in pairs: input_lang.addSentence(pair[0]) output_lang.addSentence(pair[1]) print("Counted word:") print(input_lang.name,input_lang.n_words) print(output_lang.name, output_lang.n_words) return input_lang, output_lang, pairs input_lang, output_lang, pairs = prepareData('eng','fra',True) print(random.choice(pairs)) Reading lines... Read 135842 sentence pairs Trimmed to 11739 sentence pairs Counting words... Counted word: fra 5911 eng 3965 ['elle chante les dernieres chansons populaires.', 'she is singing the latest popular songs.']

The Seq2Seq Model

允許句子到句子有不同長度和順序。

The Encoder ：

#編碼器
class  EncoderRNN(nn.Module): def __init__(self, input_size, hidden_size): super(EncoderRNN, self).__init__() self.hidden_size = hidden_size # 指定embedding矩陣W的大小維度 self.embedding = nn.Embedding(input_size, hidden_size) # 指定gru單元的大小 self.gru = nn.GRU(hidden_size, hidden_size) def forward(self, input, hidden): # 扁平化嵌入矩陣 embedded = self.embedding(input).view(1, 1, -1) print("embedded shape:",embedded.shape) output = embedded output, hidden = self.gru(output, hidden) return output, hidden #全0初始化隱層 def initHidden(self): # 這個(gè)初始化維度可以 return torch.zeros(1, 1, self.hidden_size, device=device)

這里的self.gru = nn.GRU(hidden_size, hidden_size)中，hidden_size在后面設(shè)置為256

print("embedded shape:",embedded.shape)的結(jié)果是：?
embedded shape: torch.Size([1, 1, 256])

所以self.gru(output, hidden)中傳遞的第一個(gè)維度是[1,1,256],被壓縮為這樣的。

nn.GRU源碼：

The Decoder:

seq2seq解碼器的簡化版：指利用encoder的最后輸出,稱為context vector，
context vector 作為decoder的初始化隱層狀態(tài)值?

class DecoderRNN(nn.Module):def self__init__(self, hidden_size, output_size): super(DecoderRNN, self).__init__() self.hidden_size = hidden_size self.embedding = nn.Embedding(output_size,hidden_size) self.gru = nn.GRU(hidden_size, hidden_size) self.out = nn.Linear(hidden_size, output_size) self.softmax = nn.LogSoftmax(dim=1) def forward(self, input, hidden): output = self.embedding(input).view(1, 1, -1) # 1行X列的shape做relu output = F.relu(output) output, hidden = self.gru(output, hidden) #output[0]應(yīng)該是shape為（*，*）的矩陣 output = self.softmax(self.out(output[0])) return output, hidden def initHidden(self): return torch.zeros(1, 1, self.hidden_size, device=device)

Attention Decoder：

簡單的解碼器的缺點(diǎn)：把整個(gè)句子做編碼成一個(gè)向量，信息容易丟失，翻譯一個(gè)詞的時(shí)候需要追溯之前很長的距離，一般翻譯的對應(yīng)性也沒有利用，如翻譯第一個(gè)詞，對應(yīng)大概率在原句子的第一個(gè)位置的信息。
encoder的輸出向量會乘以一個(gè)attention weights,這個(gè)權(quán)值用NN來計(jì)算完成attn，使用解碼器的輸入和隱藏狀態(tài)作為輸入。。
因?yàn)樵谟?xùn)練數(shù)據(jù)中有各種大小的句子，為了實(shí)際創(chuàng)建和訓(xùn)練這一層，我們必須選擇一個(gè)最大的句子長度（輸入長度，對于編碼器輸出）因?yàn)樵谟?xùn)練數(shù)據(jù)中有各種大小的句子，為了實(shí)際創(chuàng)建和訓(xùn)練這一層，我們必須選擇一個(gè)最大的句子長度（輸入長度，對于編碼器輸出）?

class AttnDecoderRNN(nn.Module):def __init__(self, hidden_size, output_size, dropout_p = 0.1, max_length=MAX_LENGTH):super(AttnDecoderRNN,self).__init__()self.hidden_size = hidden_sizeself.output_size = output_sizeself.dropout_p = dropout_p self.max_length = max_length self.embedding = nn.Embedding(self.output_size, self.hidden_size) self.attn = nn.Linear(self.hidden_size * 2, self.max_length) self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size) self.dropout = nn.Dropout(self.dropout_p) #輸入向量的維度是10,隱層的長度是10,默認(rèn)是一層GRU self.gru = nn.GRU(self.hidden_size, self.hidden_size) self.out = nn.Linear(self.hidden_size, self.output_size) def forward(self, input, hidden, encoder_outputs): embedded = self.embedding(input).view(1,1,-1) embedded = self.dropout(embedded) attn_weights = F.softmax( self.attn(torch.cat((embedded[0],hidden[0]),1)),dim=1) # unsqueeze:在指定的軸上多增加一個(gè)維度 attn_applied = torch.bmm(attn_weights.unsqueeze(0), encoder_outputs.unsqueeze(0)) output = torch.cat((embedded[0],attn_applied[0]),1) output = self.attn_combine(output).unsqueeze(0) output = F.relu(output) output, hidden = self.gru(output, hidden) #print("output shape:",output.shape) #print("output[0]:",output[0]) output = F.log_softmax(self.out(output[0]),dim=1) return output , hidden, attn_weights def initHidden(self): return torch.zeros(1, 1, self.hidden_size, device=device)

繼續(xù)準(zhǔn)備數(shù)據(jù)：

def indexesFromSentence(lang, sentence):return [lang.word2index[word] for word in sentence.split(' ')] def tensorFromSentence(lang, sentence): indexes = indexesFromSentence(lang, sentence) indexes.append(EOS_token) return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1) def tensorsFromPair(pair): input_tensor = tensorFromSentence(input_lang, pair[0]) target_tensor = tensorFromSentence(output_lang, pair[1]) return (input_tensor, target_tensor)

訓(xùn)練模型

解碼器的第一個(gè)輸入是SOS符，并且把編碼器最后的隱層狀態(tài)作為解碼器的第一隱層狀態(tài)。
“Teacher forcing”指用真實(shí)樣本數(shù)據(jù)作為下一步的輸入，而不是解碼器猜測的數(shù)據(jù)作為下一步輸入。

teacher_forcing_ratio = 0.5def train(input_tensor, output_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH): # 這的隱層大小封裝在encoder中，然后拿過來在train的時(shí)候初始化隱層的大小 encoder_hidden = encoder.initHidden() encoder_optimizer.zero_grad() decoder_optimizer.zero_grad() # 第一維度的大小即輸入長度 input_length = input_tensor.size(0) output_length = output_tensor.size(0) encoder_outputs = torch.zeros(max_length, encoder.hidden_size,device=device) loss = 0 for ei in range(input_length): encoder_output, encoder_hidden = encoder(input_tensor[ei],encoder_hidden) # [0,0]選取最大數(shù)組的第一個(gè)元素組里的第一個(gè) encoder_outputs[ei] = encoder_output[0 , 0] if ei == 0 : print("encoder_output[0, 0] shape: ",encoder_outputs[ei].shape) decoder_input = torch.tensor([[SOS_token]], device=device) decoder_hidden = encoder_output # niubi use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False if use_teacher_forcing: # Teacher forcing: Feed the target as the next input for di in range(output_length): decoder_ouput,decoder_hidden,decoder_attention = decoder( decoder_input, decoder_hidden, encoder_outputs) loss = loss + criterion(decoder_ouput, output_tensor[di]) decoder_input = output_tensor[di] # Teacher forcing else: for di in range(output_length): decoder_output,decoder_hidden,decoder_attention=decoder(decoder_input, decoder_hidden, encoder_outputs) topv ,topi = decoder_output.topk(1) decoder_input= topi.squeeze().detach() # # detach from history as input loss = loss + criterion(decoder_output, output_tensor[di]) if decoder_input.item() == EOS_token: break loss.backward() encoder_optimizer.step() decoder_optimizer.step() return loss.item() / target_length

好了，模型準(zhǔn)備結(jié)束：

import time
import mathdef asMinutes(s): m = math.floors(s / 60) s -= m * 60 return "%s(- %s)" % (asMinutes(s), asMinutes(rs)) def timeSince(since, percent): now = time.time() s = now - since es = s / (percent) rs = es - s return '%s (- %s)' % (asMinutes(s), asMinutes(rs))

訓(xùn)練過程：

def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01): start = time.time() plot_losses = [] print_loss_total = 0 # Reset every print_every plot_loss_total = 0 # Reset every plot_every encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate) decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate) training_pairs = [tensorsFromPair(random.choice(pairs)) for i in range(n_iters)] criterion = nn.NLLLoss() for iter in range(1, n_iters + 1): training_pair = training_pairs[iter - 1] input_tensor = training_pair[0] target_tensor = training_pair[1] loss = train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion) print_loss_total = loss + print_loss_total plot_loss_total = loss + plot_loss_total if iter % print_every == 0: print_loss_avg = print_loss_total / print_every print_loss_total = 0 print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters), iter, iter / n_iters * 100, print_loss_avg)) if iter % plot_every == 0: plot_loss_avg = plot_loss_total / plot_every plot_losses.append(plot_loss_avg) plot_loss_total = 0 showPlot(plot_losses)

畫圖的這段：

import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker import numpy as np def showPlot(points): plt.figure() fig, ax = plt.subplots() # this locator puts ticks at regular intervals loc = ticker.MultipleLocator(base=0.2) ax.yaxis.set_major_locator(loc) plt.plot(points)

驗(yàn)證的代碼：

def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):with torch.no_grad(): input_tensor = tensorFromSentence(input_lang, sentence) input_length = input_tensor.size()[0] encoder_hidden = encoder.initHidden() encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device) for ei in range(input_length): encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden) encoder_outputs[ei] += encoder_output[0, 0] decoder_input = torch.tensor([[SOS_token]], device=device) # SOS decoder_hidden = encoder_hidden decoded_words = [] decoder_attentions = torch.zeros(max_length, max_length) for di in range(max_length): decoder_output, decoder_hidden, decoder_attention = decoder( decoder_input, decoder_hidden, encoder_outputs) decoder_attentions[di] = decoder_attention.data topv, topi = decoder_output.data.topk(1) if topi.item() == EOS_token: decoded_words.append('<EOS>') break else: decoded_words.append(output_lang.index2word[topi.item()]) decoder_input = topi.squeeze().detach() return decoded_words, decoder_attentions[:di + 1]

def evaluateRandomly(encoder, decoder, n=10): for i in range(n): pair = random.choice(pairs) print('>', pair[0]) print('=', pair[1]) output_words, attentions = evaluate(encoder, decoder, pair[0]) output_sentence = ' '.join(output_words) print('<', output_sentence) print('')

最后一步：

hidden_size = 256
encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)
attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device) trainIters(encoder1, attn_decoder1, 75000, print_every=5000)