pytorch记录：seq2seq例子看看这torch怎么玩的

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

先看看简单例子：

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让list成为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)

自动求导设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()方法获取z的值，但是不能对获取后的值求导了。

new_z = z.detach()

print(new_z.grad_fn)

None

好了，重点来了

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")

准备数据：

SOS_token = 0

EOS_token = 1

class 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字符转为ASCII,用小写字母表示一切，去掉标点符号：

# 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)  匹配括号内的表达式，也表示一个组

    # [...] 用来表示一组字符,单独列出：[amk] 匹配 'a'，'m'或'k'

    # \1...\9   匹配第n个分组的内容。

    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

继续：

# 文件用的英语到其他语言，用reverse标志置换一对这样的数据。

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

过滤出部分样本：



MAX_LENGTH = 10

eng_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

允许句子到句子有不同长度和顺序。

The Encoder ：

#编码器

class  EncoderRNN(nn.Module):

    def __init__(self, input_size, hidden_size):

        super(EncoderRNN, self).__init__()

        self.hidden_size = hidden_size

        # 指定embedding矩阵W的大小维度

        self.embedding = nn.Embedding(input_size, hidden_size)

        # 指定gru单元的大小

        self.gru = nn.GRU(hidden_size, hidden_size)

    def forward(self, input, hidden):

        # 扁平化嵌入矩阵

        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初始化隐层

    def initHidden(self):

        # 这个初始化维度可以

        return torch.zeros(1, 1, self.hidden_size, device=device)

这里的self.gru = nn.GRU(hidden_size, hidden_size)中，hidden_size在后面设置为256

print("embedded shape:",embedded.shape)的结果是：
embedded shape: torch.Size([1, 1, 256])

所以self.gru(output, hidden)中传递的第一个维度是[1,1,256],被压缩为这样的。

nn.GRU源码：

The Decoder:

seq2seq解码器的简化版：指利用encoder的最后输出,称为context vector，
context vector 作为decoder的初始化隐层状态值

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]应该是shape为（*，*）的矩阵

        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：

简单的解码器的缺点：把整个句子做编码成一个向量，信息容易丢失，翻译一个词的时候需要追溯之前很长的距离，一般翻译的对应性也没有利用，如翻译第一个词，对应大概率在原句子的第一个位置的信息。
encoder的输出向量会乘以一个attention weights,这个权值用NN来计算完成attn，使用解码器的输入和隐藏状态作为输入。。
因为在训练数据中有各种大小的句子，为了实际创建和训练这一层，我们必须选择一个最大的句子长度（输入长度，对于编码器输出）因为在训练数据中有各种大小的句子，为了实际创建和训练这一层，我们必须选择一个最大的句子长度（输入长度，对于编码器输出）

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_size

        self.output_size = output_size

        self.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)

        #输入向量的维度是10,隐层的长度是10,默认是一层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:在指定的轴上多增加一个维度

        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)

继续准备数据：

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)

训练模型

解码器的第一个输入是SOS符，并且把编码器最后的隐层状态作为解码器的第一隐层状态。
“Teacher forcing”指用真实样本数据作为下一步的输入，而不是解码器猜测的数据作为下一步输入。

teacher_forcing_ratio = 0.5

def train(input_tensor, output_tensor, encoder, decoder, encoder_optimizer,

          decoder_optimizer, criterion, max_length=MAX_LENGTH):

    # 这的隐层大小封装在encoder中，然后拿过来在train的时候初始化隐层的大小

    encoder_hidden = encoder.initHidden()

    encoder_optimizer.zero_grad()

    decoder_optimizer.zero_grad()

    # 第一维度的大小即输入长度

    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]选取最大数组的第一个元素组里的第一个

        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，模型准备结束：

import time

import math

def 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))

训练过程：

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)

画图的这段：

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)

验证的代码：

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)