PyTorch NLP From Scratch: 使用char-RNN对姓氏进行分类
2025-06-18 17:15 更新
在自然语言处理(NLP)领域,字符级循环神经网络(char-RNN)是一种强大的工具,可以用于对文本数据进行建模和分类。本教程教你如何从头开始构建和训练一个字符级 RNN 模型,用于对姓氏进行分类。
一、准备数据
我们将使用包含来自 18 种不同语言的姓氏的数据集。这些数据存储在多个文本文件中,每个文件对应一种语言。我们需要将这些数据加载到内存中,并进行预处理。
1. 加载数据文件
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
print(findFiles('data/names/*.txt')) # 查找所有数据文件
2. 将 Unicode 转换为 ASCII
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
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')) # 测试转换功能
3. 构建类别行字典
category_lines = {}
all_categories = []
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)
print(category_lines['Italian'][:5]) # 查看意大利姓氏的前 5 个示例
二、将名称转换为张量
为了将名称输入到神经网络中,我们需要将字符转换为张量。我们使用 one-hot 编码来表示每个字符。
import torch
def letterToIndex(letter):
return all_letters.find(letter)
def letterToTensor(letter):
tensor = torch.zeros(1, n_letters)
tensor[0][letterToIndex(letter)] = 1
return tensor
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')) # 测试单个字符转换
print(lineToTensor('Jones').size()) # 测试整个名称转换
三、构建字符级 RNN 模型
我们将构建一个字符级 RNN 模型,用于根据姓氏的拼写预测其来源。
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)
四、训练模型
1. 准备训练数据
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)
2. 定义训练函数
criterion = nn.NLLLoss()
learning_rate = 0.005
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()
for p in rnn.parameters():
p.data.add_(-learning_rate, p.grad.data)
return output, loss.item()
3. 进行训练
n_iters = 100000
print_every = 5000
plot_every = 1000
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
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))
if iter % plot_every == 0:
all_losses.append(current_loss / plot_every)
current_loss = 0
五、评估模型
1. 绘制训练损失曲线
import matplotlib.pyplot as plt
plt.figure()
plt.plot(all_losses)
plt.title("Training Loss Curve")
plt.xlabel("Iteration")
plt.ylabel("Loss")
plt.show()
2. 构建混淆矩阵
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 10000
def evaluate(line_tensor):
hidden = rnn.initHidden()
for i in range(line_tensor.size()[0]):
output, hidden = rnn(line_tensor[i], hidden)
return output
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
for i in range(n_categories):
confusion[i] = confusion[i] / confusion[i].sum()
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(confusion.numpy())
fig.colorbar(cax)
ax.set_xticklabels([''] + all_categories, rotation=90)
ax.set_yticklabels([''] + all_categories)
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
六、实际应用
现在,我们可以使用训练好的模型对新的姓氏进行分类。
def predict(input_line, n_predictions=3):
print('\n> %s' % input_line)
with torch.no_grad():
output = evaluate(lineToTensor(input_line))
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')
通过本教程,你学会了如何使用 PyTorch 构建和训练字符级 RNN 模型,用于对姓氏进行分类。
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