Pytorch1.0入门实战一：LeNet神经网络实现 MNIST手写数字识别

记得第一次接触手写数字识别数据集还在学习TensorFlow，各种sess.run()，头都绕晕了。自从接触pytorch以来，一直想写点什么。曾经在2017年5月，Andrej Karpathy发表的一篇Twitter，调侃道：l've been using PyTorch a few months now, l've never felt better, l've more energy.My skin is clearer. My eye sight has improved。确实，使用pytorch以来，确实感觉心情要好多了，不像TensorFlow那样晦涩难懂。迫不及待的用pytorch实战了一把MNIST数据集，构建LeNet神经网络。话不多说，直接上代码！

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
import cv2

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 6, 3, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(6, 16, 5),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )

        self.fc1 = nn.Sequential(
            nn.Linear(16 * 5 * 5, 120),
            nn.BatchNorm1d(120),
            nn.ReLU()
        )

        self.fc2 = nn.Sequential(
            nn.Linear(120, 84),
            nn.BatchNorm1d(84),#加快收敛速度的方法（注：批标准化一般放在全连接层后面，激活函数层的前面）
            nn.ReLU()
        )

        self.fc3 = nn.Linear(84, 10)

    #         self.sfx = nn.Softmax()

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        #         print(x.shape)
        x = x.view(x.size()[0], -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        #         x = self.sfx(x)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 64
LR = 0.001
Momentum = 0.9

# 下载数据集
train_dataset = datasets.MNIST(root = './data/',
                              train=True,
                              transform = transforms.ToTensor(),
                              download=False)
test_dataset =datasets.MNIST(root = './data/',
                            train=False,
                            transform=transforms.ToTensor(),
                            download=False)
#建立一个数据迭代器
train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
                                          batch_size = batch_size,
                                          shuffle = True)
test_loader = torch.utils.data.DataLoader(dataset = test_dataset,
                                         batch_size = batch_size,
                                         shuffle = False)

#实现单张图片可视化
# images,labels = next(iter(train_loader))
# img  = torchvision.utils.make_grid(images)
# img = img.numpy().transpose(1,2,0)
# # img.shape
# std = [0.5,0.5,0.5]
# mean = [0.5,0.5,0.5]
# img = img*std +mean
# cv2.imshow('win',img)
# key_pressed = cv2.waitKey(0)

net = LeNet().to(device)
criterion = nn.CrossEntropyLoss()#定义损失函数
optimizer = optim.SGD(net.parameters(),lr=LR,momentum=Momentum)

epoch = 1
if __name__ == '__main__':
    for epoch in range(epoch):
        sum_loss = 0.0
        for i, data in enumerate(train_loader):
            inputs, labels = data
            inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
            optimizer.zero_grad()#将梯度归零
            outputs = net(inputs)#将数据传入网络进行前向运算
            loss = criterion(outputs, labels)#得到损失函数
            loss.backward()#反向传播
            optimizer.step()#通过梯度做一步参数更新

            # print(loss)
            sum_loss += loss.item()
            if i % 100 == 99:
                print('[%d,%d] loss:%.03f' % (epoch + 1, i + 1, sum_loss / 100))
                sum_loss = 0.0

    #验证测试集
    net.eval()#将模型变换为测试模式
    correct = 0
    total = 0
    for data_test in test_loader:
        images, labels = data_test
        images, labels = Variable(images).cuda(), Variable(labels).cuda()
        output_test = net(images)
        # print("output_test:",output_test.shape)

        _, predicted = torch.max(output_test, 1)#此处的predicted获取的是最大值的下标
        # print("predicted:",predicted.shape)
        total += labels.size(0)
        correct += (predicted == labels).sum()
    print("correct1: ",correct)
    print("Test acc: {0}".format(correct.item() / len(test_dataset)))#.cpu().numpy()

本次识别手写数字，只做了1个epoch，train_loss：0.250，测试集上的准确率：0.9685，相当不错的结果。