Pytorch 深度学习实践 第13讲

第13讲 循环神经网络（高级篇）

数据集链接：https://pan.baidu.com/s/1bIKasCIDAaT-_EwB6hcAMQ
提取码：4fij

任务：使用RNN通过训练name数据集来预测name属于哪个country.

RNN，LSTM，GRU都是循环神经网络。

网络模型：

最后只需要一个Linear Layer来得出整个name序列的预测结果。

数据准备

Name序列处理步骤：

1.Name转成序列List，即Maclean→[‘M’, ‘a’, ‘c’, ‘l’, ‘e’, ‘a’, ‘n’]

2.用ASCⅡ码表示List中的元素，即[77 97 99 108 101 97 110]，里面不是代表数值，而是one-hot向量，比如77是指共128维的向量，只有Idex77是1，其他都是0

3.Padding将一个batch_size的序列填充成长度相同，即[77 97 99 108 101 97 110 0 0 0]

4.将Padding后的矩阵转置

5.按照序列长度降序排列

同时要获得对应顺序的序列长度列表[10, 8, 7, 7, 7, 7,6, 6, 5]

Country的处理步骤：

给每个Country一个索引，构成字典{‘country’: idx}，在name排序之后，也要按照相同的排序将name对应的Country标签排序。

就是说，在拿到一个name后，就能根据name在batch_size中的位置找到country，再进一步找到分类标签idx。

双向RNN

从算一遍，再从算一遍，将相应的和 拼接起来，最终得到的hidden=[, ]。

是否双向在代码中通过bidirectional = 1或2 体现。

torch.nn.GRU()

1.维度

input:(seqlen, batch_size, hidden_size)

output:(seqlen, batch_size, hidden_size * nDirections)

hidden:(nLayers * nDirections, batch_size, hidden_size)

2.参数设置

torch.nn.GRU(hidden_size, hidden_size, num_layers, bidirectional=_)

torch.nn.utils.rnn.pack_padded_sequence()

1.作用：提升GRU的运行效率

2.用法：经过embadding之后的维度（seqlen, baych_size, hidden_size）

随后通过rnn.pack_padded_sequence(embadding, seq_lengths)对非零的值进行打包

示例代码：

import torch
import gzip
import csv
from torch.utils.data import DataLoader
import numpy as np
from torch.nn.utils import rnn
from torch.utils.data import Dataset
import time
import matplotlib.pyplot as plt
import math
 
#参数初始化
Hidden_size = 100
Batch_size = 256
Num_layers = 2
Num_epoch = 10
Num_chars = 128
use_gpu = False
 
class NameDataset(Dataset):
    '构造数据集'
    def __init__(self, is_train_set=True):
        super(NameDataset, self).__init__()
        filename = 'E:/image-dataset/names_sets/names_train.csv.gz' if is_train_set else 'E:/image-dataset/names_sets/names_test.csv.gz'
        with gzip.open(filename, 'rt') as f:
            reader = csv.reader(f)
            rows = list(reader) #rows=[(name,country),(name,country),...]
        self.names = [row[0] for row in rows]
        self.len = len(self.names)
        self.countries = [row[1] for row in rows]
        self.country_list = list(sorted(set(self.countries))) #经过去重、排序后得到country列表
        self.country_dict = self.getCountryDict() #用此函数将list转成对应的dict，['country1':idx分类号,'country2':idx分类号,...]
        self.country_num = len(self.country_list)
    def __getitem__(self, index):
        '为了数据集能提供索引访问，返回：索引对应的name字符串,索引对应的country的分类号'
        return self.names[index], self.country_dict[self.countries[index]]
    def __len__(self):
        return self.len
    def getCountryDict(self):
        country_dict = dict()
        for idx, country_name in enumerate(self.country_list, 0):
            country_dict[country_name] = idx
        return country_dict
    def idx2country(self,index):
        return self.country_list[index]
    def getCountryNum(self):
        return self.country_num
 
#数据准备
train_sets = NameDataset(is_train_set=True)
train_loader = DataLoader(train_sets, batch_size = Batch_size, shuffle=True)
 
test_sets = NameDataset(is_train_set=False)
test_loader = DataLoader(test_sets, batch_size=Batch_size, shuffle=False)
Num_country = train_sets.getCountryNum() #模型最终的分类类别数
 
def create_tensor(tensor):
    if use_gpu:
        device = torch.device("cuda:0")
        tensor = tensor.to(device)
    return tensor
 
def name2list(name):
    "读取名字的每个字符 对应的 的ASC码值，将名字list变成由ASC表示的列表.输出元组：名字的ASC表示 和 名字长度"
    arr = [ord(c) for c in name]
    return arr, len(arr)
 
def make_tensors(names, countries):
    sequences_and_lengths = [name2list(name) for name in names]
    name_sequences = [sl[0] for sl in sequences_and_lengths]
    seq_lengths = torch.LongTensor([sl[1] for sl in sequences_and_lengths])
    countries = countries.long()
 
    # make tensor of name, Batchsize*seqlen
    seq_tensor = torch.zeros(len(name_sequences), seq_lengths.max()).long()
    for idx, (seq, seq_len) in enumerate(zip(name_sequences, seq_lengths), 0):
        seq_tensor[idx, :seq_len] = torch.LongTensor(seq) #idx对应的位置中，从0到name长度将name填进去
 
    #按照name长度进行排序
    seq_lengths, perm_idx = seq_lengths.sort(dim=0, descending=True)
    seq_tensor = seq_tensor[perm_idx] #将这两个也按照相同的idx排序
    countries = countries[perm_idx]
 
    return create_tensor(seq_tensor),\
        create_tensor(seq_lengths),\
        create_tensor(countries)
class RNNClassifier(torch.nn.Module):
    '构造RNN分类器模型'
    def __init__(self, input_size, hidden_size, output_size, n_layers=1,bidirectional=True):
        super(RNNClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.n_layers = n_layers
        self.directions = 2 if bidirectional else 1
 
        self.embadding = torch.nn.Embedding(input_size, hidden_size)
        self.gru = torch.nn.GRU(hidden_size, hidden_size, n_layers, bidirectional=bidirectional)
        self.fc = torch.nn.Linear(hidden_size * self.directions, output_size)
    def _init_hidden(self,batch_size):
        hidden = torch.zeros(self.n_layers * self.directions, batch_size, self.hidden_size)
        return create_tensor(hidden)
    def forward(self,input, seq_lengths):
        'input:所有序列，seq_lengths:每个序列的长度'
        input = input.t() #将input做转置，由batch×seq_len到seq_len×batch
        batch_size = input.size(1) #用它来构造最初的隐层H0
 
        hidden = self._init_hidden(batch_size)
        embadding = self.embadding(input) #shape:(seq_len,batchsize,hiddensize)
 
        # 将序列按照长度降序打包
        gru_input = rnn.pack_padded_sequence(embadding, seq_lengths)
 
        output, hidden = self.gru(gru_input, hidden)
        if self.directions==2:
            hidden_cat = torch.cat([hidden[-1], hidden[-2]], dim=1)
        else:
            hidden_cat = hidden[-1]
        fc_output = self.fc(hidden_cat)
        return fc_output
 
def time_since(since):
    s = time.time()-since
    m = math.floor(s/60)
    s -= m * 60
    return '%dm %ds' % (m, s)
def trainModel():
    total_loss = 0
    for i, (names,countries) in enumerate(train_loader,1):
        inputs, seq_lengths, targets = make_tensors(names,countries)
        output = classifier(inputs, seq_lengths)
        loss = criterion(output, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
        if i % 10 == 0:
            print(f'[{time_since(start)}] Epoch {epoch}', end='')
            print(f'[{i * len(inputs)}/{len(train_sets)}]',end='')
            print(f'loss={total_loss / (i * len(inputs))}')
    return total_loss
def testModel():
    correct = 0
    total = len(test_sets)
    print('evaluating trained model...')
    with torch.no_grad():
        for i, (names, countries) in enumerate(test_loader, 1):
            inputs, seq_lengths, targets = make_tensors(names, countries)
            output = classifier(inputs, seq_lengths)
            pred = output.max(dim=1, keepdim=True)[1]
            correct += pred.eq(targets.view_as(pred)).sum().item()
        percent = '%.2f' % (100 * correct / total)
        print(f'Test set:Accuracy {correct} / {total}  {percent}%')
    return correct / total
#参数初始化
 
if __name__=="__main__":
    classifier = RNNClassifier(Num_chars, Hidden_size, Num_country, Num_layers)
    if use_gpu:
        device = torch.device('cuda:0')
        classifier.to(device)
 
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)
 
    start = time.time()
    print('Training for %d epochs...' % Num_epoch)
    acc_list = []
    for epoch in range(1,Num_epoch+1):
        trainModel()
        acc = testModel()
        acc_list.append(acc)
 
    epoch = np.arange(1, len(acc_list) + 1, 1)
    acc_list = np.array(acc_list)
    plt.plot(epoch, acc_list)
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.grid()
    plt.show()

说明：m.sort(dim = _, descending = _),dim=0或1，0是按列排序，1是按行排序；descending=True是由大到小，false是由小到大。最后返回：1.排序后的序列；2.排序后对应的原来的idx。