手把手教你实现PyTorch的MNIST数据集

概述

mnist 包含 0~9 的手写数字, 共有 60000 个训练集和 10000 个测试集. 数据的格式为单通道 28*28 的灰度图.

获取数据

def get_data():
    """获取数据"""

    # 获取测试集
    train = torchvision.datasets.mnist(root="./data", train=true, download=true,
                                       transform=torchvision.transforms.compose([
                                           torchvision.transforms.totensor(),  # 转换成张量
                                           torchvision.transforms.normalize((0.1307,), (0.3081,))  # 标准化
                                       ]))
    train_loader = dataloader(train, batch_size=batch_size)  # 分割测试集

    # 获取测试集
    test = torchvision.datasets.mnist(root="./data", train=false, download=true,
                                      transform=torchvision.transforms.compose([
                                          torchvision.transforms.totensor(),  # 转换成张量
                                          torchvision.transforms.normalize((0.1307,), (0.3081,))  # 标准化
                                      ]))
    test_loader = dataloader(test, batch_size=batch_size)  # 分割训练

    # 返回分割好的训练集和测试集
    return train_loader, test_loader

网络模型

class model(torch.nn.module):
    def __init__(self):
        super(model, self).__init__()

        # 卷积层
        self.conv1 = torch.nn.conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))
        self.conv2 = torch.nn.conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))

        # dropout层
        self.dropout1 = torch.nn.dropout(0.25)
        self.dropout2 = torch.nn.dropout(0.5)

        # 全连接层
        self.fc1 = torch.nn.linear(9216, 128)
        self.fc2 = torch.nn.linear(128, 10)

    def forward(self, x):
        """前向传播"""
        
        # [b, 1, 28, 28] => [b, 32, 26, 26]
        out = self.conv1(x)
        out = f.relu(out)
        
        # [b, 32, 26, 26] => [b, 64, 24, 24]
        out = self.conv2(out)
        out = f.relu(out)

        # [b, 64, 24, 24] => [b, 64, 12, 12]
        out = f.max_pool2d(out, 2)
        out = self.dropout1(out)
        
        # [b, 64, 12, 12] => [b, 64 * 12 * 12] => [b, 9216]
        out = torch.flatten(out, 1)
        
        # [b, 9216] => [b, 128]
        out = self.fc1(out)
        out = f.relu(out)

        # [b, 128] => [b, 10]
        out = self.dropout2(out)
        out = self.fc2(out)

        output = f.log_softmax(out, dim=1)

        return output

train 函数

def train(model, epoch, train_loader):
    """训练"""

    # 训练模式
    model.train()

    # 迭代
    for step, (x, y) in enumerate(train_loader):
        # 加速
        if use_cuda:
            model = model.cuda()
            x, y = x.cuda(), y.cuda()

        # 梯度清零
        optimizer.zero_grad()

        output = model(x)

        # 计算损失
        loss = f.nll_loss(output, y)

        # 反向传播
        loss.backward()

        # 更新梯度
        optimizer.step()

        # 打印损失
        if step % 50 == 0:
            print('epoch: {}, step {}, loss: {}'.format(epoch, step, loss))

test 函数

def test(model, test_loader):
    """测试"""
    
    # 测试模式
    model.eval()

    # 存放正确个数
    correct = 0

    with torch.no_grad():
        for x, y in test_loader:

            # 加速
            if use_cuda:
                model = model.cuda()
                x, y = x.cuda(), y.cuda()

            # 获取结果
            output = model(x)

            # 预测结果
            pred = output.argmax(dim=1, keepdim=true)

            # 计算准确个数
            correct += pred.eq(y.view_as(pred)).sum().item()

    # 计算准确率
    accuracy = correct / len(test_loader.dataset) * 100

    # 输出准确
    print("test accuracy: {}%".format(accuracy))

main 函数

def main():
    # 获取数据
    train_loader, test_loader = get_data()
    
    # 迭代
    for epoch in range(iteration_num):
        print("\n================ epoch: {} ================".format(epoch))
        train(network, epoch, train_loader)
        test(network, test_loader)

完整代码:

import torch
import torchvision
import torch.nn.functional as f
from torch.utils.data import dataloader
class model(torch.nn.module):
    def __init__(self):
        super(model, self).__init__()

        # 卷积层
        self.conv1 = torch.nn.conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))
        self.conv2 = torch.nn.conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))

        # dropout层
        self.dropout1 = torch.nn.dropout(0.25)
        self.dropout2 = torch.nn.dropout(0.5)

        # 全连接层
        self.fc1 = torch.nn.linear(9216, 128)
        self.fc2 = torch.nn.linear(128, 10)

    def forward(self, x):
        """前向传播"""
        
        # [b, 1, 28, 28] => [b, 32, 26, 26]
        out = self.conv1(x)
        out = f.relu(out)
        
        # [b, 32, 26, 26] => [b, 64, 24, 24]
        out = self.conv2(out)
        out = f.relu(out)

        # [b, 64, 24, 24] => [b, 64, 12, 12]
        out = f.max_pool2d(out, 2)
        out = self.dropout1(out)
        
        # [b, 64, 12, 12] => [b, 64 * 12 * 12] => [b, 9216]
        out = torch.flatten(out, 1)
        
        # [b, 9216] => [b, 128]
        out = self.fc1(out)
        out = f.relu(out)

        # [b, 128] => [b, 10]
        out = self.dropout2(out)
        out = self.fc2(out)

        output = f.log_softmax(out, dim=1)

        return output


# 定义超参数
batch_size = 64  # 一次训练的样本数目
learning_rate = 0.0001  # 学习率
iteration_num = 5  # 迭代次数
network = model()  # 实例化网络
print(network)  # 调试输出网络结构
optimizer = torch.optim.adam(network.parameters(), lr=learning_rate)  # 优化器

# gpu 加速
use_cuda = torch.cuda.is_available()
print("是否使用 gpu 加速:", use_cuda)


def get_data():
    """获取数据"""

    # 获取测试集
    train = torchvision.datasets.mnist(root="./data", train=true, download=true,
                                       transform=torchvision.transforms.compose([
                                           torchvision.transforms.totensor(),  # 转换成张量
                                           torchvision.transforms.normalize((0.1307,), (0.3081,))  # 标准化
                                       ]))
    train_loader = dataloader(train, batch_size=batch_size)  # 分割测试集

    # 获取测试集
    test = torchvision.datasets.mnist(root="./data", train=false, download=true,
                                      transform=torchvision.transforms.compose([
                                          torchvision.transforms.totensor(),  # 转换成张量
                                          torchvision.transforms.normalize((0.1307,), (0.3081,))  # 标准化
                                      ]))
    test_loader = dataloader(test, batch_size=batch_size)  # 分割训练

    # 返回分割好的训练集和测试集
    return train_loader, test_loader


def train(model, epoch, train_loader):
    """训练"""

    # 训练模式
    model.train()

    # 迭代
    for step, (x, y) in enumerate(train_loader):
        # 加速
        if use_cuda:
            model = model.cuda()
            x, y = x.cuda(), y.cuda()

        # 梯度清零
        optimizer.zero_grad()

        output = model(x)

        # 计算损失
        loss = f.nll_loss(output, y)

        # 反向传播
        loss.backward()

        # 更新梯度
        optimizer.step()

        # 打印损失
        if step % 50 == 0:
            print('epoch: {}, step {}, loss: {}'.format(epoch, step, loss))


def test(model, test_loader):
    """测试"""

    # 测试模式
    model.eval()

    # 存放正确个数
    correct = 0

    with torch.no_grad():
        for x, y in test_loader:

            # 加速
            if use_cuda:
                model = model.cuda()
                x, y = x.cuda(), y.cuda()

            # 获取结果
            output = model(x)

            # 预测结果
            pred = output.argmax(dim=1, keepdim=true)

            # 计算准确个数
            correct += pred.eq(y.view_as(pred)).sum().item()

    # 计算准确率
    accuracy = correct / len(test_loader.dataset) * 100

    # 输出准确
    print("test accuracy: {}%".format(accuracy))


def main():
    # 获取数据
    train_loader, test_loader = get_data()

    # 迭代
    for epoch in range(iteration_num):
        print("\n================ epoch: {} ================".format(epoch))
        train(network, epoch, train_loader)
        test(network, test_loader)

if __name__ == "__main__":
    main()

输出结果:

model(
  (conv1): conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))
  (conv2): conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
  (dropout1): dropout(p=0.25, inplace=false)
  (dropout2): dropout(p=0.5, inplace=false)
  (fc1): linear(in_features=9216, out_features=128, bias=true)
  (fc2): linear(in_features=128, out_features=10, bias=true)
)
是否使用 gpu 加速: true

================ epoch: 0 ================
epoch: 0, step 0, loss: 2.3131277561187744
epoch: 0, step 50, loss: 1.0419045686721802
epoch: 0, step 100, loss: 0.6259541511535645
epoch: 0, step 150, loss: 0.7194482684135437
epoch: 0, step 200, loss: 0.4020516574382782
epoch: 0, step 250, loss: 0.6890509128570557
epoch: 0, step 300, loss: 0.28660136461257935
epoch: 0, step 350, loss: 0.3277580738067627
epoch: 0, step 400, loss: 0.2750288248062134
epoch: 0, step 450, loss: 0.28428223729133606
epoch: 0, step 500, loss: 0.3514065444469452
epoch: 0, step 550, loss: 0.23386947810649872
epoch: 0, step 600, loss: 0.25338059663772583
epoch: 0, step 650, loss: 0.1743898093700409
epoch: 0, step 700, loss: 0.35752204060554504
epoch: 0, step 750, loss: 0.17575909197330475
epoch: 0, step 800, loss: 0.20604261755943298
epoch: 0, step 850, loss: 0.17389622330665588
epoch: 0, step 900, loss: 0.3188241124153137
test accuracy: 96.56%

================ epoch: 1 ================
epoch: 1, step 0, loss: 0.23558208346366882
epoch: 1, step 50, loss: 0.13511177897453308
epoch: 1, step 100, loss: 0.18823786079883575
epoch: 1, step 150, loss: 0.2644936144351959
epoch: 1, step 200, loss: 0.145077645778656
epoch: 1, step 250, loss: 0.30574971437454224
epoch: 1, step 300, loss: 0.2386859953403473
epoch: 1, step 350, loss: 0.08346735686063766
epoch: 1, step 400, loss: 0.10480977594852448
epoch: 1, step 450, loss: 0.07280707359313965
epoch: 1, step 500, loss: 0.20928426086902618
epoch: 1, step 550, loss: 0.20455852150917053
epoch: 1, step 600, loss: 0.10085935145616531
epoch: 1, step 650, loss: 0.13476189970970154
epoch: 1, step 700, loss: 0.19087043404579163
epoch: 1, step 750, loss: 0.0981522724032402
epoch: 1, step 800, loss: 0.1961515098810196
epoch: 1, step 850, loss: 0.041140712797641754
epoch: 1, step 900, loss: 0.250461220741272
test accuracy: 98.03%

================ epoch: 2 ================
epoch: 2, step 0, loss: 0.09572553634643555
epoch: 2, step 50, loss: 0.10370486229658127
epoch: 2, step 100, loss: 0.17737184464931488
epoch: 2, step 150, loss: 0.1570713371038437
epoch: 2, step 200, loss: 0.07462178170681
epoch: 2, step 250, loss: 0.18744900822639465
epoch: 2, step 300, loss: 0.09910508990287781
epoch: 2, step 350, loss: 0.08929706364870071
epoch: 2, step 400, loss: 0.07703761011362076
epoch: 2, step 450, loss: 0.10133732110261917
epoch: 2, step 500, loss: 0.1314031481742859
epoch: 2, step 550, loss: 0.10394387692213058
epoch: 2, step 600, loss: 0.11612939089536667
epoch: 2, step 650, loss: 0.17494803667068481
epoch: 2, step 700, loss: 0.11065669357776642
epoch: 2, step 750, loss: 0.061209067702293396
epoch: 2, step 800, loss: 0.14715790748596191
epoch: 2, step 850, loss: 0.03930797800421715
epoch: 2, step 900, loss: 0.18030673265457153
test accuracy: 98.46000000000001%

================ epoch: 3 ================
epoch: 3, step 0, loss: 0.09266342222690582
epoch: 3, step 50, loss: 0.0414913073182106
epoch: 3, step 100, loss: 0.2152961939573288
epoch: 3, step 150, loss: 0.12287424504756927
epoch: 3, step 200, loss: 0.13468700647354126
epoch: 3, step 250, loss: 0.11967387050390244
epoch: 3, step 300, loss: 0.11301510035991669
epoch: 3, step 350, loss: 0.037447575479745865
epoch: 3, step 400, loss: 0.04699449613690376
epoch: 3, step 450, loss: 0.05472381412982941
epoch: 3, step 500, loss: 0.09839300811290741
epoch: 3, step 550, loss: 0.07964356243610382
epoch: 3, step 600, loss: 0.08182843774557114
epoch: 3, step 650, loss: 0.05514759197831154
epoch: 3, step 700, loss: 0.13785190880298615
epoch: 3, step 750, loss: 0.062480345368385315
epoch: 3, step 800, loss: 0.120387002825737
epoch: 3, step 850, loss: 0.04458726942539215
epoch: 3, step 900, loss: 0.17119190096855164
test accuracy: 98.55000000000001%

================ epoch: 4 ================
epoch: 4, step 0, loss: 0.08094145357608795
epoch: 4, step 50, loss: 0.05615215748548508
epoch: 4, step 100, loss: 0.07766406238079071
epoch: 4, step 150, loss: 0.07915271818637848
epoch: 4, step 200, loss: 0.1301635503768921
epoch: 4, step 250, loss: 0.12118984013795853
epoch: 4, step 300, loss: 0.073218435049057
epoch: 4, step 350, loss: 0.04517696052789688
epoch: 4, step 400, loss: 0.08493026345968246
epoch: 4, step 450, loss: 0.03904269263148308
epoch: 4, step 500, loss: 0.09386837482452393
epoch: 4, step 550, loss: 0.12583576142787933
epoch: 4, step 600, loss: 0.09053893387317657
epoch: 4, step 650, loss: 0.06912104040384293
epoch: 4, step 700, loss: 0.1502612829208374
epoch: 4, step 750, loss: 0.07162325084209442
epoch: 4, step 800, loss: 0.10512275993824005
epoch: 4, step 850, loss: 0.028180215507745743
epoch: 4, step 900, loss: 0.08492615073919296
test accuracy: 98.69%

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