# 目录

 单层神经网络

 RNN原理

## y1为第1个输出向量

 经典RNN结构

## 输入为字符，输出为下一个字符的概率。这就是著名的CharRNN问题。

 N VS 1 RNN结构

## 输入一段视频判断他的类别

 1 VS N RNN结构

## 从类别生成语音或音乐等

 Pytorch文本分类实践

## 对应代码

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
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)
category_lines[category] = lines

n_categories = len(all_categories)

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

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())

import torch.nn as nn
import torch.nn.functional as F

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(hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)

def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = F.relu(self.i2h(combined))
output = self.i2o(hidden)
output = self.softmax(output)
return output, hidden

def initHidden(self):

n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)

input = letterToTensor('A')
hidden =torch.zeros(1, n_hidden)

output, next_hidden = rnn(input, hidden)

input = lineToTensor('Albert')
hidden = torch.zeros(1, n_hidden)

output, next_hidden = rnn(input[0], hidden)
print(output)

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))

import random
random.seed(66)
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)

criterion = nn.NLLLoss()
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()

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():

return output, loss.item()

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()
PATH = './char-rnn-classification.pth'
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
torch.save(rnn.state_dict(), PATH)
net = RNN(n_letters, n_hidden, n_categories)

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

plt.figure()
plt.plot(all_losses)

# Keep track of correct guesses in a confusion matrix
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 100000

# 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
n_correct = 0
n_sum = 0
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
if category == guess:
n_correct += 1
n_sum += 1

print("acc",n_correct/n_sum)

# 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()
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()
View Code

 参考资料

## https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial

posted @ 2020-07-04 23:18  黎明程序员  阅读(1663)  评论(0编辑  收藏  举报