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    pytorch教程实现mnist手写数字识别代码示例

    1.构建网络

    nn.Moudle是pytorch官方指定的编写Net模块,在init函数中添加需要使用的层,在foeword中定义网络流向。

    下面详细解释各层:

    conv1层:输入channel = 1 ,输出chanael = 10,滤波器5*5

    maxpooling = 2*2

    conv2层:输入channel = 10 ,输出chanael = 20,滤波器5*5,

    dropout

    maxpooling = 2*2

    fc1层:输入320 个神经单元,输出50个神经单元
    fc1层:输入50个神经单元 ,输出10个神经单元
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)         
            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
            self.conv2_drop = nn.Dropout2d()
            self.fc1 = nn.Linear(320, 50)
            self.fc2 = nn.Linear(50, 10) 
        def forward(self, x):                                 #x.size() = 28*28*1
            x = F.relu(F.max_pool2d(self.conv1(x), 2))
            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))  #x.size() =12*12*10    
            x = x.view(-1, 320)                                          #x.size() =1*320 
            x = F.relu(self.fc1(x))
            x = F.dropout(x, training=self.training)
            x = self.fc2(x)
            return F.log_softmax(x, dim=1)

    2.编写训练代码

    model = Net()                                 #调用写好的网络
    if args.cuda:                                 #如果有GPU使用CPU
        model.cuda()
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)   #设置SGD随机梯度下降算法
    def train(epoch):                                                 
        model.train()                             
        for batch_idx, (data, target) in enumerate(train_loader):
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()                                  #梯度初始化为O
            output = model(data) 
            loss = F.nll_loss(output, target)                      #简历loss function
            loss.backward()                                        #反向传播,计算梯度
            optimizer.step()                                       #更新权重
            if batch_idx % args.log_interval == 0:                 #输出信息
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.data[0]))

    3.编写测试代码

    def test():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data, volatile=True), Variable(target)
            output = model(data)
            test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
            pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
            correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
         test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))

    4.指导程序train和test

    for epoch in range(1, args.epochs + 1):
        train(epoch)                              #训练N个epoch
        test()                                    #检验在测试集上的表现

    5.完整代码

    # -*- coding: utf-8 -*-
    from __future__ import print_function
    import argparse
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.optim as optim
    from torchvision import datasets, transforms
    from torch.autograd import Variable
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    args = parser.parse_args()
    args.cuda = not args.no_cuda and torch.cuda.is_available()
     
    torch.manual_seed(args.seed)
    if args.cuda:
        torch.cuda.manual_seed(args.seed) 
    kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.batch_size, shuffle=True, **kwargs)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.test_batch_size, shuffle=True, **kwargs)
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
            self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
            self.conv2_drop = nn.Dropout2d()
            self.fc1 = nn.Linear(320, 50)
            self.fc2 = nn.Linear(50, 10) 
        def forward(self, x):
            print (x.size())
            x = F.relu(F.max_pool2d(self.conv1(x), 2))
            print(x.size())
            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
            print(x.size())
            x = x.view(-1, 320)
            x = F.relu(self.fc1(x))
            x = F.dropout(x, training=self.training)
            x = self.fc2(x)
            return F.log_softmax(x, dim=1)
     model = Net()                                 #调用写好的网络
    if args.cuda:                                 #如果有GPU使用CPU
        model.cuda()
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)   #设置SGD随机梯度下降算法
    def train(epoch):
        model.train()
        for batch_idx, (data, target) in enumerate(train_loader):
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data), Variable(target)
            optimizer.zero_grad()                                  #梯度初始化为O
            output = model(data)
            loss = F.nll_loss(output, target)                      #简历loss function
            loss.backward()                                        #反向传播,计算梯度
            optimizer.step()                                       #更新权重
            if batch_idx % args.log_interval == 0:                 #输出信息
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.data[0]))
    def test():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data, volatile=True), Variable(target)
            output = model(data)
            test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
            pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
            correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
     
        test_loss /= len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset))) 
    for epoch in range(1, args.epochs + 1):
        train(epoch)
        test()

    以上就是pytorch教程实现mnist手写数字识别代码示例的详细内容,更多关于pytorch实现mnist手写数字识别的资料请关注脚本之家其它相关文章!

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