pytorch 利用lstm做mnist手写数字识别分类的实例

Python  /  管理员 发布于 5年前   565

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 syssys.path.append('..') import torchimport datetimefrom torch.autograd import Variablefrom torch import nnfrom torch.utils.data import DataLoader from torchvision import transforms as tfsfrom 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)    

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