RNN
RNN 循环神经网络
-处理不定长输入的模型
-常用于NLP及时间序列任务(输入数据具有前后关系)
RNN网络结构
xt:时刻t的输入,shape = (1,57)
st:时刻t的状态值,shape = (1,128)
ot:时刻t的输出值,shape = (1,18)
U:linear层的权重参数,shape = (128,57)
W:linear层的权重参数,shape = (128,128)
V:linear层的权重参数,shape = (57,128)
hidden state : 隐藏层状态信息，记录过往时刻的信息
RNN实现人名分类
输入任意长度姓名(字符串)，输出姓名来自哪个国家(分类任务)
# -*- coding: utf-8 -*-
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 tools.common_tools import set_seed
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(BASE_DIR, "..", "..", "data", "data", "names", "*.txt")
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters) # 52 + 5 字符总数 采用one-hot编码
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 = []
# 遍历每一个类别
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
# 查看一共有多少个类别 总共有18个类别
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')
