# Chou(字符串) -> RNN -> Chinese(分类类别)
for string in [C,h,o,u]:
首先把每个字母转换成 one-hot -> [0,0,...,1,...,0]
y,h=model([0,0,...,1,...,0], h) # h 就是隐藏层的状态信息
这里没有使用 DataLoader 和 Dataset，而是手动构造了数据集的结构，训练数据使用 dict 存储，包括 18 个元素，每个元素是一个 list，存储了 18 个类别的名字列表。label 存放在一个 list 中。在迭代训练过程如下：
from io import open
import glob
import unicodedata
import string
import math
import os
import time
import torch.nn as nn
import torch
import random
import matplotlib.pyplot as plt
import torch.utils.data
from common_tools import set_seed
import enviroments
set_seed(1) # 设置随机种子
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
# 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]
def unicodeToAscii(s):
return ''.join(
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
and c in all_letters)
# 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
def categoryFromOutput(output):
top_n, top_i = output.topk(1)
category_i = top_i[0].item()
return all_categories[category_i], category_i
def randomChoice(l):
return l[random.randint(0, len(l) - 1)]
def randomTrainingExample():
category = randomChoice(all_categories) # 选类别
line = randomChoice(category_lines[category]) # 选一个样本
category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)
line_tensor = lineToTensor(line) # str to one-hot
return category, line, category_tensor, line_tensor
def timeSince(since):
now = time.time()
s = now - since
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
# 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
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)
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]))
def get_lr(iter, learning_rate):
lr_iter = learning_rate if iter < n_iters else learning_rate*0.1
return lr_iter
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.u = nn.Linear(input_size, hidden_size)
self.w = nn.Linear(hidden_size, hidden_size)
self.v = nn.Linear(hidden_size, output_size)
self.tanh = nn.Tanh()
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, inputs, hidden):
u_x = self.u(inputs)
hidden = self.w(hidden)
hidden = self.tanh(hidden + u_x)
output = self.softmax(self.v(hidden))
return output, hidden
def initHidden(self):
return torch.zeros(1, self.hidden_size)
def train(category_tensor, line_tensor):
hidden = rnn.initHidden()
rnn.zero_grad()
line_tensor = line_tensor.to(device)
hidden = hidden.to(device)
category_tensor = category_tensor.to(device)
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()
if __name__ == "__main__":
# config
path_txt = os.path.join(enviroments.names,"*.txt")
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters) # 52 + 5 字符总数
print_every = 5000
plot_every = 5000
learning_rate = 0.005
n_iters = 200000
# step 1 data
# Build the category_lines dictionary, a list of names per language
category_lines = {}
all_categories = []
for filename in glob.glob(path_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)
# step 2 model
n_hidden = 128
# rnn = RNN(n_letters, n_hidden, n_categories)
rnn = RNN(n_letters, n_hidden, n_categories)
rnn.to(device)
# step 3 loss
criterion = nn.NLLLoss()
# step 4 optimize by hand
# step 5 iteration
current_loss = 0
all_losses = []
start = time.time()
for iter in range(1, n_iters + 1):
# sample
category, line, category_tensor, line_tensor = randomTrainingExample()
# training
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('Iter: {:<7} time: {:>8s} loss: {:.4f} name: {:>10s} pred: {:>8s} label: {:>8s}'.format(
iter, 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
path_model = os.path.join(BASE_DIR, "rnn_state_dict.pkl")
torch.save(rnn.state_dict(), path_model)
plt.plot(all_losses)
plt.show()
predict('Yue Tingsong')
predict('Yue tingsong')
predict('yutingsong')
predict('test your name')
