pytorch 利用lstm做mnist手寫數(shù)字識別分類的實(shí)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
 
#定義數(shù)據(jù)
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)
 
#定義模型
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)#使用兩層lstm
     self.classifier = nn.Linear(hidden_feature, num_class)#將最后一個(gè)的rnn使用全連接的到最后的輸出結(jié)果
     
   def forward(self, x):
     #x的大小為（batch，1，28,28），所以我們需要將其轉(zhuǎn)化為rnn的輸入格式（28，batch，28）
     x = x.squeeze() #去掉（batch，1,28,28）中的1，變成（batch， 28,28）
     x = x.permute(2, 0, 1)#將最后一維放到第一維，變成（batch，28,28）
     out, _ = self.rnn(x) #使用默認(rèn)的隱藏狀態(tài)，得到的out是（28， batch， hidden_feature）
     out = out[-1,:,:]#取序列中的最后一個(gè)，大小是（batch， hidden_feature)
     out = self.classifier(out) #得到分類結(jié)果
     return out
     
net = rnn_classify()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adadelta(net.parameters(), 1e-1)
 
#定義訓(xùn)練過程
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)