pytorch 利用lstm做mnist手写数字识别分类的实例

# -*- 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)