pytorch 利用lstm做mnist手写数字识别分类的实例

yipeiwu_com5年前Python基础

代码如下,U我认为对于新手来说最重要的是学会rnn读取数据的格式。

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 9 08:53:25 2018
@author: www
"""
 
import sys
sys.path.append('..')
 
import torch
import datetime
from torch.autograd import Variable
from torch import nn
from torch.utils.data import DataLoader
 
from torchvision import transforms as tfs
from torchvision.datasets import MNIST
 
#定义数据
data_tf = tfs.Compose([
   tfs.ToTensor(),
   tfs.Normalize([0.5], [0.5])
])
train_set = MNIST('E:/data', train=True, transform=data_tf, download=True)
test_set = MNIST('E:/data', train=False, transform=data_tf, download=True)
 
train_data = DataLoader(train_set, 64, True, num_workers=4)
test_data = DataLoader(test_set, 128, False, num_workers=4)
 
#定义模型
class rnn_classify(nn.Module):
   def __init__(self, in_feature=28, hidden_feature=100, num_class=10, num_layers=2):
     super(rnn_classify, self).__init__()
     self.rnn = nn.LSTM(in_feature, hidden_feature, num_layers)#使用两层lstm
     self.classifier = nn.Linear(hidden_feature, num_class)#将最后一个的rnn使用全连接的到最后的输出结果
     
   def forward(self, x):
     #x的大小为(batch,1,28,28),所以我们需要将其转化为rnn的输入格式(28,batch,28)
     x = x.squeeze() #去掉(batch,1,28,28)中的1,变成(batch, 28,28)
     x = x.permute(2, 0, 1)#将最后一维放到第一维,变成(batch,28,28)
     out, _ = self.rnn(x) #使用默认的隐藏状态,得到的out是(28, batch, hidden_feature)
     out = out[-1,:,:]#取序列中的最后一个,大小是(batch, hidden_feature)
     out = self.classifier(out) #得到分类结果
     return out
     
net = rnn_classify()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adadelta(net.parameters(), 1e-1)
 
#定义训练过程
def get_acc(output, label):
  total = output.shape[0]
  _, pred_label = output.max(1)
  num_correct = (pred_label == label).sum().item()
  return num_correct / total
  
  
def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
  if torch.cuda.is_available():
    net = net.cuda()
  prev_time = datetime.datetime.now()
  for epoch in range(num_epochs):
    train_loss = 0
    train_acc = 0
    net = net.train()
    for im, label in train_data:
      if torch.cuda.is_available():
        im = Variable(im.cuda()) # (bs, 3, h, w)
        label = Variable(label.cuda()) # (bs, h, w)
      else:
        im = Variable(im)
        label = Variable(label)
      # forward
      output = net(im)
      loss = criterion(output, label)
      # backward
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
 
      train_loss += loss.item()
      train_acc += get_acc(output, label)
 
    cur_time = datetime.datetime.now()
    h, remainder = divmod((cur_time - prev_time).seconds, 3600)
    m, s = divmod(remainder, 60)
    time_str = "Time %02d:%02d:%02d" % (h, m, s)
    if valid_data is not None:
      valid_loss = 0
      valid_acc = 0
      net = net.eval()
      for im, label in valid_data:
        if torch.cuda.is_available():
          im = Variable(im.cuda())
          label = Variable(label.cuda())
        else:
          im = Variable(im)
          label = Variable(label)
        output = net(im)
        loss = criterion(output, label)
        valid_loss += loss.item()
        valid_acc += get_acc(output, label)
      epoch_str = (
        "Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, "
        % (epoch, train_loss / len(train_data),
          train_acc / len(train_data), valid_loss / len(valid_data),
          valid_acc / len(valid_data)))
    else:
      epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
             (epoch, train_loss / len(train_data),
             train_acc / len(train_data)))
    prev_time = cur_time
    print(epoch_str + time_str)
    
train(net, train_data, test_data, 10, optimizer, criterion)    

以上这篇pytorch 利用lstm做mnist手写数字识别分类的实例就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持【听图阁-专注于Python设计】。

相关文章

解决pycharm运行程序出现卡住scanning files to index索引的问题

有时候会出现索引问题,显示scanning files to index 解决方法: in pycharm, go to the "File" on the left top, then...

浅析Python中的多重继承

浅析Python中的多重继承

继承是面向对象编程的一个重要的方式,因为通过继承,子类就可以扩展父类的功能。 回忆一下Animal类层次的设计,假设我们要实现以下4种动物:    ...

Python的函数的一些高阶特性

高阶函数英文叫Higher-order function。什么是高阶函数?我们以实际代码为例子,一步一步深入概念。 变量可以指向函数 以Python内置的求绝对值的函数abs()为例,调...

Python3 tkinter 实现文件读取及保存功能

Python3 tkinter 实现文件读取及保存功能

tkinter介绍 tkinter是python自带的GUI库,是对图形库TK的封装 tkinter是一个跨平台的GUI库,开发的程序可以在win,linux或者mac下运行 #...

python print 按逗号或空格分隔的方法

1)按,分隔 a, b = 0, 1 while b < 1000: print(b, end=',') a, b = b, a+b 1,1,2,3,5,8,13,...