NLP From Scratch:使用char-RNN对姓氏进行分类

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作者: Sean Robertson

校验: 松鼠

我们将构建和训练基本的char-RNN来对单词进行分类。本教程以及以下两个教程展示了如何“从头开始”为NLP建模进行预处理数据,尤其是不使用Torchtext的许多便利功能,因此您可以了解NLP建模的预处理是如何从低层次进行的。

char-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,我们将训练起源于18种语言的数千种姓氏,并根据拼写来预测姓氏来自哪种语言:

$ 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

建议:

假设你已经至少安装PyTorch,知道Python和理解张量:

下面这些是了解RNNs以及它们如何工作的相关联接:

准备数据

  • Note 从此处下载数据,并将其解压到当前目录。

包含了在data/names目录被命名为[Language] .txt 的18个文本文件。每个文件都包含了一堆姓氏,每行一个名字,大多都已经罗马字母化了(但我们仍然需要从Unicode转换到到ASCII)。

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

    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]
        a\ll_categories.append(category)
        lines = readLines(filename)
        category_lines[category] = lines

    n_categories = len(all_categories)

输出:

    ['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字典,将每个类别(语言)映射到行(姓氏)列表。我们还保持all_categories(只是一种语言列表)和n_categories为可追加状态,供后续的调用。

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

输出:

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

将姓氏转化为张量

我们已经处理好了所有的姓氏,现在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<1 x n letters>的“独热向量” 。一个独热向量就是在字母索引处填充1,其他都填充为0,例,"b" = <0 1 0 0 0 ...>

为了表达一个单词,我们将一堆字母合并成2D矩阵,其中举证的大小为<line_length x 1 x n_letters>

额外的1维是因为PyTorch假设所有东西都是成批的-我们在这里只使用1的批处理大小。

    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())

输出:

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])

创建网络

在进行自动求导之前,在Torch中创建一个递归神经网络需要在多个时间状态上克隆图的参数。图保留了隐藏状态和梯度,这些状态和梯度现在完全由图本身处理。这意味着您可以以非常“单纯”的方式将RNN作为常规的前馈网络来实现。

这个RNN模块(大部分是从PyTorch for Torch用户教程中复制的)只有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)

运行网络的步骤是,首先我们需要输入(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都创建一个新的Tensor,因此我们将使用和lineToTensor代替letterToTensorslice。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)

输出:

    tensor([[-2.8636, -2.8199, -2.8899, -2.9073, -2.9117, -2.8644, -2.9027, -2.9334,
             -2.8705, -2.8383, -2.8892, -2.9161, -2.8215, -2.9996, -2.9423, -2.9116,
             -2.8750, -2.8862]], grad_fn=<LogSoftmaxBackward>)

正如你看到的输出为<1 × n_categories>的张量,其中每一个值都是该类别的可能性(数值越大可能性越高)。

训练

准备训练

在训练之前,我们需要做一些辅助函数。首先是解释网络的输出,我们知道这是每个类别的可能性。我们可以用Tensor.topk来获取最大值对应的索引:

    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))

输出:

    ('Czech', 1)

我们也将需要一个快速的方法来获得一个训练例子(姓氏和其所属语言):


    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, '\t // \t line =', line)

输出:

category =  Dutch      //      line =  Ryskamp
category =  Spanish      //      line =  Iniguez
category =  Vietnamese      //      line =  Thuy
category =  Italian      //      line =  Nacar
category =  Vietnamese      //      line =  Le
category =  French      //      line =  Tremblay
category =  Russian      //      line =  Bakhchivandzhi
category =  Irish      //      line =  Kavanagh
category =  Irish      //      line =  O'Shea
category =  Spanish      //      line =  Losa

网络训练

现在,训练该网络所需要做的就是向它喂入大量训练样例,进行预测,并告诉它预测的是否正确。

最后因为RNN的最后一层是nn.LogSoftmax,所以我们选择损失函数nn.NLLLoss比较合适。

    criterion = nn.NLLLoss()

每个循环的训练将:

  • 创建输入和目标张量
  • 创建一个零初始隐藏状态
  • 读取每个字母
    • 保持隐藏状态到下一个字母
  • 比较最后输出和目标
  • 进行反向传播
  • 返回输出值和损失函数的值
    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样例。由于train函数同时返回outputloss,因此我们可以打印其猜测并跟踪绘制损失。由于有1000个样例,因此我们仅打印每个print_every样例,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

输出:

    5000 5% (0m 7s) 2.7482 Silje / French ✗ (Dutch)
    10000 10% (0m 15s) 1.5569 Lillis / Greek ✓
    15000 15% (0m 22s) 2.7729 Burt / Korean ✗ (English)
    20000 20% (0m 30s) 1.1036 Zhong / Chinese ✓
    25000 25% (0m 38s) 1.7088 Sarraf / Portuguese ✗ (Arabic)
    30000 30% (0m 45s) 0.7595 Benivieni / Italian ✓
    35000 35% (0m 53s) 1.2900 Arreola / Italian ✗ (Spanish)
    40000 40% (1m 0s) 2.3171 Gass / Arabic ✗ (German)
    45000 45% (1m 8s) 3.1630 Stoppelbein / Dutch ✗ (German)
    50000 50% (1m 15s) 1.7478 Berger / German ✗ (French)
    55000 55% (1m 23s) 1.3516 Almeida / Spanish ✗ (Portuguese)
    60000 60% (1m 31s) 1.8843 Hellewege / Dutch ✗ (German)
    65000 65% (1m 38s) 1.7374 Moreau / French ✓
    70000 70% (1m 46s) 0.5718 Naifeh / Arabic ✓
    75000 75% (1m 53s) 0.6268 Zhui / Chinese ✓
    80000 80% (2m 1s) 2.2226 Dasios / Portuguese ✗ (Greek)
    85000 85% (2m 9s) 1.3690 Walter / Scottish ✗ (German)
    90000 90% (2m 16s) 0.5329 Zhang / Chinese ✓
    95000 95% (2m 24s) 3.4474 Skala / Czech ✗ (Polish)
    100000 100% (2m 31s) 1.4720 Chi / Korean ✗ (Chinese)

绘制结果

从绘制all_losses的历史损失图可以看出网络的学习:


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

    plt.figure()
    plt.plot(all_losses)

img/sphx_glr_char_rnn_classification_tutorial_001.png

评价结果

为了了解网络在不同类别上的表现如何,我们将创建一个混淆矩阵,包含姓氏属于的实际语言(行)和网络猜测的是哪种语言(列)。要计算混淆矩阵,将使用evaluate()通过网络来评测一些样本。

    # 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()

img/sphx_glr_char_rnn_classification_tutorial_002.png

您可以从主轴上挑出一些亮点,以显示错误猜测的语言,例如,中文(朝鲜语)和西班牙语(意大利语)。它似乎与希腊语搭预测得很好,而英语预测的很差(可能是因为与其他语言重叠)。

运行用户输入

    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
    (-0.47) Russian
    (-1.30) Czech
    (-2.90) Polish

    > Jackson
    (-1.04) Scottish
    (-1.72) English
    (-1.74) Russian

    > Satoshi
    (-0.32) Japanese
    (-2.63) Polish
    (-2.71) Italian

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(训练)
  • predict.pypredict()与命令行参数一起运行)
  • server.py(通过bottle.py将预测用作JSON API)

运行train.py训练并保存网络。

predict.py脚本并加上姓氏运行以查看预测:

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

运行server.py,查看http://localhost:5533/Yourname 获得预测的JSON输出。

练习

  • 尝试使用line-> category的其他数据集,例如:
    • 任何单词->语言
    • 名->性别
    • 角色名称->作家
    • 页面标题-> Blog或subreddit
  • 通过更大和/或结构更好的网络获得更好的结果
    • 添加更多线性层
    • 尝试nn.LSTM和nn.GRU图层
    • 将多个这些RNN合并为更高级别的网络

脚本的总运行时间: (2分钟42.458秒)


Copyright © ibooker.org.cn 2019 all right reserved,由 ApacheCN 团队提供支持该文件修订时间: 2019-10-09 15:33:42

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