循环神经网络是什么
Recurrent Neural Networks
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
参考资料
Recurrent Neural Networks Tutorial, Part 1 -- Introduction to RNNs
Understanding LSTM Networks
RNN实现人名分类
问题定义：输入任意长度姓名（字符串），输出姓名来自哪一个国家（18类分类任务）
数据： https://download.pytorch.org/tutorial/data.zip
Jackie Chan ------ 成龙
Jay Chou ------ 周杰伦
Tingsong Yue ------ 余霆嵩
RNN如何处理不定长输入
思考：计算机如何实现不定长字符串 到分类向量 的映射？
Chou（字符串）→ RNN →Chinese（分类类别）
-
单词字符 → 数字
-
数字 → model
-
下一个字符 → 数字 → model
-
最后一个字符 → 数字 → model → 分类向量
伪代码
Chou（字符串）→ RNN →Chinese（分类类别）
for string in [C, h, o, u]:
1. one-hot：string → [0,0, ...., 1, ..., 0] # 首先把每个字母转换成编码
2. y, h = model([0,0, ...., 1, ..., 0], h) # h就是隐藏层的状态信息
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 = (18, 128)
代码如下：
python
# -*- coding: utf-8 -*-
"""
# @file name : rnn_demo.py
# @author : TingsongYu https://github.com/TingsongYu
# @date : 2019-12-09
# @brief : rnn人名分类
"""
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
import sys
# 获取路径
hello_pytorch_DIR = os.path.abspath(os.path.dirname(__file__)+os.path.sep+".."+os.path.sep+"..")
sys.path.append(hello_pytorch_DIR)
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__))
# 选择运行设备
# 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
# 定义网络结构
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) # 该方法已经被弃用
p.data.add_(p.grad.data, alpha=-learning_rate)
return output, loss.item()
if __name__ == "__main__":
print(device)
# config
data_dir = os.path.abspath(os.path.join(BASE_DIR, "..", "..", "data", "rnn_data", "names"))
if not os.path.exists(data_dir):
raise Exception("\n{} 不存在，请下载 08-05-数据-20200724.zip 放到\n{} 下，并解压即可".format(
data_dir, os.path.dirname(data_dir)))
path_txt = os.path.join(data_dir, "*.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.abspath(os.path.join(BASE_DIR, "..", "..", "data", "rnn_state_dict.pkl"))
if not os.path.exists(path_model):
raise Exception("\n{} 不存在，请下载 08-05-数据-20200724.zip 放到\n{} 下，并解压即可".format(
path_model, os.path.dirname(path_model)))
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')