NLP From Scratch: 使用char-RNN对姓氏进行分类

原文: https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html

注意

单击此处的下载完整的示例代码

作者Sean Robertson

我们将构建和训练基本的字符级 RNN 对单词进行分类。 本教程与以下两个教程一起,展示了如何“从头开始”进行 NLP 建模的预处理数据,特别是不使用 <cite>torchtext</cite> 的许多便利功能,因此您可以了解如何进行 NLP 建模的预处理 在低水平上工作。

字符级 RNN 将单词作为一系列字符读取-在每个步骤输出预测和“隐藏状态”,将其先前的隐藏状态输入到每个下一步。 我们将最终的预测作为输出,即单词属于哪个类别。

具体来说,我们将训练来自 18 种起源语言的数千种姓氏,并根据拼写方式预测名称的来源:

$ python predict.py Hinton
(-0.47) Scottish
(-1.52) English
(-3.57) Irish

$ python predict.py Schmidhuber
(-0.19) German
(-2.48) Czech
(-2.68) Dutch

推荐读物:

我假设您至少已经安装了 PyTorch,了解 Python 和了解 Tensors:

了解 RNN 及其工作方式也将很有用:

准备数据

Note

从的下载数据,并将其提取到当前目录。

data/names目录中包含 18 个文本文件,名为“ [Language] .txt”。 每个文件包含一堆名称,每行一个名称,大多数都是罗马化的(但我们仍然需要从 Unicode 转换为 ASCII)。

我们将得到一个字典,列出每种语言的名称列表{language: [names ...]}。 通用变量“类别”和“行”(在本例中为语言和名称)用于以后的扩展。

from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os

def findFiles(path): return glob.glob(path)

print(findFiles('data/names/*.txt'))

import unicodedata
import string

all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)

# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
        and c in all_letters
    )

print(unicodeToAscii('Ślusàrski'))

# Build the category_lines dictionary, a list of names per language
category_lines = {}
all_categories = []

# Read a file and split into lines
def readLines(filename):
    lines = open(filename, encoding='utf-8').read().strip().split('\n')
    return [unicodeToAscii(line) for line in lines]

for filename in findFiles('data/names/*.txt'):
    category = os.path.splitext(os.path.basename(filename))[0]
    all_categories.append(category)
    lines = readLines(filename)
    category_lines[category] = lines

n_categories = len(all_categories)

出:

['data/names/French.txt', 'data/names/Czech.txt', 'data/names/Dutch.txt', 'data/names/Polish.txt', 'data/names/Scottish.txt', 'data/names/Chinese.txt', 'data/names/English.txt', 'data/names/Italian.txt', 'data/names/Portuguese.txt', 'data/names/Japanese.txt', 'data/names/German.txt', 'data/names/Russian.txt', 'data/names/Korean.txt', 'data/names/Arabic.txt', 'data/names/Greek.txt', 'data/names/Vietnamese.txt', 'data/names/Spanish.txt', 'data/names/Irish.txt']
Slusarski

现在我们有了category_lines,这是一个字典,将每个类别(语言)映射到行(名称)列表。 我们还跟踪了all_categories(只是语言列表)和n_categories,以供以后参考。

print(category_lines['Italian'][:5])

Out:

['Abandonato', 'Abatangelo', 'Abatantuono', 'Abate', 'Abategiovanni']

将名称转换为张量

现在我们已经组织了所有名称,我们需要将它们转换为张量以使用它们。

为了表示单个字母,我们使用大小为&lt;1 x n_letters&gt;的“ one-hot vector”。 一个热门向量用 0 填充,但当前字母的索引处的数字为 1,例如 "b" = &lt;0 1 0 0 0 ...&gt;

为了制造一个单词,我们将其中的一些连接成 2D 矩阵&lt;line_length x 1 x n_letters&gt;

额外的 1 维是因为 PyTorch 假设所有内容都是批量的-我们在这里只使用 1 的批量大小。

import torch

# Find letter index from all_letters, e.g. "a" = 0
def letterToIndex(letter):
    return all_letters.find(letter)

# Just for demonstration, turn a letter into a <1 x n_letters> Tensor
def letterToTensor(letter):
    tensor = torch.zeros(1, n_letters)
    tensor[0][letterToIndex(letter)] = 1
    return tensor

# Turn a line into a <line_length x 1 x n_letters>,
# or an array of one-hot letter vectors
def lineToTensor(line):
    tensor = torch.zeros(len(line), 1, n_letters)
    for li, letter in enumerate(line):
        tensor[li][0][letterToIndex(letter)] = 1
    return tensor

print(letterToTensor('J'))

print(lineToTensor('Jones').size())

Out:

tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0.]])
torch.Size([5, 1, 57])

建立网络

在进行自动分级之前,在 Torch 中创建一个递归神经网络需要在多个时间步上克隆图层的参数。 图层保留了隐藏状态和渐变,这些图层现在完全由图形本身处理。 这意味着您可以以非常“纯粹”的方式实现 RNN,作为常规的前馈层。

这个 RNN 模块(主要从 PyTorch for Torch 用户教程的复制)仅是 2 个线性层,它们在输入和隐藏状态下运行,输出之后是 LogSoftmax 层。

import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()

        self.hidden_size = hidden_size

        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)

n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)

要运行此网络的步骤,我们需要传递输入(在本例中为当前字母的张量)和先前的隐藏状态(首先将其初始化为零)。 我们将返回输出(每种语言的概率)和下一个隐藏状态(我们将其保留用于下一步)。

input = letterToTensor('A')
hidden =torch.zeros(1, n_hidden)

output, next_hidden = rnn(input, hidden)

为了提高效率,我们不想为每个步骤创建一个新的 Tensor,因此我们将使用lineToTensor而不是letterToTensor并使用切片。 这可以通过预先计算一批张量来进一步优化。

input = lineToTensor('Albert')
hidden = torch.zeros(1, n_hidden)

output, next_hidden = rnn(input[0], hidden)
print(output)

Out:

tensor([[-2.9504, -2.8402, -2.9195, -2.9136, -2.9799, -2.8207, -2.8258, -2.8399,
         -2.9098, -2.8815, -2.8313, -2.8628, -3.0440, -2.8689, -2.9391, -2.8381,
         -2.9202, -2.8717]], grad_fn=<LogSoftmaxBackward>)

如您所见,输出为&lt;1 x n_categories&gt;张量,其中每个项目都是该类别的可能性(更高的可能性更大)。

训练

准备训练

在接受训练之前,我们应该做一些辅助功能。 首先是解释网络的输出,我们知道这是每个类别的可能性。 我们可以使用Tensor.topk来获得最大值的索引:

def categoryFromOutput(output):
    top_n, top_i = output.topk(1)
    category_i = top_i[0].item()
    return all_categories[category_i], category_i

print(categoryFromOutput(output))

Out:

('Chinese', 5)

我们还将需要一种快速的方法来获取训练示例(名称及其语言):

import random

def randomChoice(l):
    return l[random.randint(0, len(l) - 1)]

def randomTrainingExample():
    category = randomChoice(all_categories)
    line = randomChoice(category_lines[category])
    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)
    line_tensor = lineToTensor(line)
    return category, line, category_tensor, line_tensor

for i in range(10):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    print('category =', category, '/ line =', line)

Out:

category = Italian / line = Pastore
category = Arabic / line = Toma
category = Irish / line = Tracey
category = Portuguese / line = Lobo
category = Arabic / line = Sleiman
category = Polish / line = Sokolsky
category = English / line = Farr
category = Polish / line = Winogrodzki
category = Russian / line = Adoratsky
category = Dutch / line = Robert

训练网络

现在,训练该网络所需要做的就是向它展示大量示例,进行猜测,并告诉它是否错误。

对于损失函数,nn.NLLLoss是适当的,因为 RNN 的最后一层是nn.LogSoftmax

criterion = nn.NLLLoss()

每个训练循环将:

  • 创建输入和目标张量
  • 创建归零的初始隐藏状态
  • 阅读和中的每个字母
    • 保持下一个字母的隐藏状态
  • 比较最终输出与目标
  • 反向传播
  • 返回输出和损失
learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn

def train(category_tensor, line_tensor):
    hidden = rnn.initHidden()

    rnn.zero_grad()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    loss = criterion(output, category_tensor)
    loss.backward()

    # Add parameters' gradients to their values, multiplied by learning rate
    for p in rnn.parameters():
        p.data.add_(-learning_rate, p.grad.data)

    return output, loss.item()

现在,我们只需要运行大量示例。 由于train函数同时返回输出和损失,因此我们可以打印其猜测并跟踪绘制损失。 因为有 1000 个示例,所以我们仅打印每个print_every示例,并对损失进行平均。

import time
import math

n_iters = 100000
print_every = 5000
plot_every = 1000

# Keep track of losses for plotting
current_loss = 0
all_losses = []

def timeSince(since):
    now = time.time()
    s = now - since
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)

start = time.time()

for iter in range(1, n_iters + 1):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output, loss = train(category_tensor, line_tensor)
    current_loss += loss

    # Print iter number, loss, name and guess
    if iter % print_every == 0:
        guess, guess_i = categoryFromOutput(output)
        correct = '✓' if guess == category else '✗ (%s)' % category
        print('%d %d%% (%s) %.4f %s / %s %s' % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct))

    # Add current loss avg to list of losses
    if iter % plot_every == 0:
        all_losses.append(current_loss / plot_every)
        current_loss = 0

Out:

5000 5% (0m 12s) 3.1806 Olguin / Irish ✗ (Spanish)
10000 10% (0m 21s) 2.1254 Dubnov / Russian ✓
15000 15% (0m 29s) 3.1001 Quirke / Polish ✗ (Irish)
20000 20% (0m 38s) 0.9191 Jiang / Chinese ✓
25000 25% (0m 46s) 2.3233 Marti / Italian ✗ (Spanish)
30000 30% (0m 54s) nan Amari / Russian ✗ (Arabic)
35000 35% (1m 3s) nan Gudojnik / Russian ✓
40000 40% (1m 11s) nan Finn / Russian ✗ (Irish)
45000 45% (1m 20s) nan Napoliello / Russian ✗ (Italian)
50000 50% (1m 28s) nan Clark / Russian ✗ (Irish)
55000 55% (1m 37s) nan Roijakker / Russian ✗ (Dutch)
60000 60% (1m 46s) nan Kalb / Russian ✗ (Arabic)
65000 65% (1m 54s) nan Hanania / Russian ✗ (Arabic)
70000 70% (2m 3s) nan Theofilopoulos / Russian ✗ (Greek)
75000 75% (2m 11s) nan Pakulski / Russian ✗ (Polish)
80000 80% (2m 20s) nan Thistlethwaite / Russian ✗ (English)
85000 85% (2m 29s) nan Shadid / Russian ✗ (Arabic)
90000 90% (2m 37s) nan Finnegan / Russian ✗ (Irish)
95000 95% (2m 46s) nan Brannon / Russian ✗ (Irish)
100000 100% (2m 54s) nan Gomulka / Russian ✗ (Polish)

绘制结果

all_losses绘制历史损失可显示网络学习情况:

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

plt.figure()
plt.plot(all_losses)

../_images/sphx_glr_char_rnn_classification_tutorial_001.png

评估结果

为了查看网络在不同类别上的表现如何,我们将创建一个混淆矩阵,为每种实际语言(行)指示网络猜测(列)哪种语言。 为了计算混淆矩阵,使用evaluate()通过网络运行一堆样本,该样本等于train()减去反向传播器。

# Keep track of correct guesses in a confusion matrix
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 10000

# Just return an output given a line
def evaluate(line_tensor):
    hidden = rnn.initHidden()

    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)

    return output

# Go through a bunch of examples and record which are correctly guessed
for i in range(n_confusion):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output = evaluate(line_tensor)
    guess, guess_i = categoryFromOutput(output)
    category_i = all_categories.index(category)
    confusion[category_i][guess_i] += 1

# Normalize by dividing every row by its sum
for i in range(n_categories):
    confusion[i] = confusion[i] / confusion[i].sum()

# Set up plot
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(confusion.numpy())
fig.colorbar(cax)

# Set up axes
ax.set_xticklabels([''] + all_categories, rotation=90)
ax.set_yticklabels([''] + all_categories)

# Force label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

# sphinx_gallery_thumbnail_number = 2
plt.show()

../_images/sphx_glr_char_rnn_classification_tutorial_002.png

您可以从主轴上挑出一些亮点,以显示它猜错了哪些语言,例如 中文(朝鲜语)和西班牙语(意大利语)。 它似乎与希腊语搭配得很好,与英语搭配得很差(可能是因为与其他语言重叠)。

在用户输入上运行

def predict(input_line, n_predictions=3):
    print('\n> %s' % input_line)
    with torch.no_grad():
        output = evaluate(lineToTensor(input_line))

        # Get top N categories
        topv, topi = output.topk(n_predictions, 1, True)
        predictions = []

        for i in range(n_predictions):
            value = topv[0][i].item()
            category_index = topi[0][i].item()
            print('(%.2f) %s' % (value, all_categories[category_index]))
            predictions.append([value, all_categories[category_index]])

predict('Dovesky')
predict('Jackson')
predict('Satoshi')

Out:

> Dovesky
(nan) Russian
(nan) Arabic
(nan) Korean

> Jackson
(nan) Russian
(nan) Arabic
(nan) Korean

> Satoshi
(nan) Russian
(nan) Arabic
(nan) Korean

实际 PyTorch 存储库中的脚本的最终版本将上述代码分成几个文件:

  • data.py(加载文件)
  • model.py(定义 RNN)
  • train.py(进行训练)
  • predict.py(使用命令行参数运行predict()
  • server.py(通过 bottle.py 将预测用作 JSON API)

运行train.py训练并保存网络。

使用名称运行predict.py以查看预测:

$ python predict.py Hazaki
(-0.42) Japanese
(-1.39) Polish
(-3.51) Czech

运行server.py并访问 http:// localhost:5533 /您的名字以获取预测的 JSON 输出。

练习题

  • 尝试使用其他行->类别的数据集,例如:
    • 任何字词->语言
    • 名->性别
    • 角色名称->作家
    • 页面标题->博客或 subreddit
  • 通过更大和/或形状更好的网络获得更好的结果
    • 添加更多线性层
    • 尝试nn.LSTMnn.GRU
    • 将多个这些 RNN 合并为更高级别的网络

脚本的总运行时间:(3 分 4.326 秒)

Download Python source code: char_rnn_classification_tutorial.py Download Jupyter notebook: char_rnn_classification_tutorial.ipynb

由狮身人面像画廊生成的画廊


Copyright © ibooker.org.cn 2019 all right reserved,由 ApacheCN 团队提供支持该文件修订时间: 2020-07-19 13:48:36

results matching ""

    No results matching ""

    results matching ""

      No results matching ""