RNN和LSTM

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一、RNN

全称为Recurrent Neural Network,意为循环神经网络，用于处理序列数据。

序列数据是指在不同时间点上收集到的数据，反映了某一事物、现象等随时间的变化状态或程度。即数据之间有联系。

RNN的特点：1，,层间神经元也有连接（主要为隐层）；2，共享参数

其结构如上图所示，数据为顺序处理，在处理长序列数据时，极易导致梯度消失问题。

二、LSTM

LSTM为长短期记忆，是一种变种的RNN，在RNN的基础上引入了细胞状态，根据细胞状态可决定哪些状态应该保留下来，哪些状态应该被遗忘。

LSTM可一定程度上解决梯度消失问题。

由上图可知，在RNN的基础上，增加了一路输入和输出，增加的这一路就是细胞状态。

由上一时刻的输出和当前时刻的输入，经过sigmod函数之后，趋近于0被遗忘的多，趋近于1被遗忘的少。

由上一时刻的输出和当前时刻的输入，经过sigmod函数之后，决定哪些内容应该被记住，被记住的内容并不是上一时刻的输出和当前时刻的输入，而是需要经过tanh函数。

程序：应用LSTM训练mnist数据集

import os
import torch
import torch.nn as nn
import torch.utils.data as Data
from torch.autograd import Variable
import torchvision.datasets as dsets
import matplotlib.pyplot as plt
import torchvision.transforms as transforms# torch.manual_seed(1) # reproducible# Hyper Parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64
LR = 0.01 # learning rate
DOWNLOAD_MNIST = False #已下载好数据集，就设置为False，否则为TRUE
TIME_STEP=28 #可理解为输入图像维度
INPUT_SIZE=28# Mnist digits dataset
if not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):
# not mnist dir or mnist is empyt dir
DOWNLOAD_MNIST = Truetrain_data = dsets.MNIST(
root='./mnist/',
train=True, # this is training data
transform=transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to
# torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]
download=DOWNLOAD_MNIST,
)# plot one example
# print(train_data.train_data.size()) # (60000, 28, 28)
# print(train_data.train_labels.size()) # (60000)
# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_labels[0])
# plt.show()# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)# pick 2000 samples to speed up testing
test_data = dsets.MNIST(root='./mnist/', train=False,transform=transforms.ToTensor())
test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels.numpy()[:2000]class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM(
input_size=INPUT_SIZE,
hidden_size=64,
num_layers=1,
batch_first=True
) self.out=nn.Linear(64,10)
def forward(self,x):
r_out,(h_n,h_c)=self.rnn(x,None)
out=self.out(r_out[:,-1,:]) #数据格式为[batch,time_step,input],因此输出参考的是最后时刻的数据
return outrnn=RNN()
print(rnn) # net architectureoptimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hottedfor epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader): # gives batch data, normalize x when iterate train_loader
b_x=Variable(x.view(-1,28,28))
b_y=Variable(y)
output = rnn(b_x) # cnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients if step % 50 == 0:
test_output = rnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
accuracy =float((pred_y==test_y).astype(int).sum())/float(test_y.size)
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)# print 10 predictions from test data
test_output = rnn(test_x[:10].view(-1,28,28))
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')