PyTorch CNN实战之MNIST手写数字识别示例

简介

卷积神经网络(Convolutional Neural Network, CNN)是深度学习技术中极具代表的网络结构之一，在图像处理领域取得了很大的成功，在国际标准的ImageNet数据集上，许多成功的模型都是基于CNN的。

卷积神经网络CNN的结构一般包含这几个层：

1. 输入层：用于数据的输入

2. 卷积层：使用卷积核进行特征提取和特征映射

3. 激励层：由于卷积也是一种线性运算，因此需要增加非线性映射

4. 池化层：进行下采样，对特征图稀疏处理，减少数据运算量。

5. 全连接层：通常在CNN的尾部进行重新拟合，减少特征信息的损失

6. 输出层：用于输出结果

PyTorch实战

本文选用上篇的数据集MNIST手写数字识别实践CNN。

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
batch_size = 64

# MNIST Dataset
train_dataset = datasets.MNIST(root='./data/',
               train=True,
               transform=transforms.ToTensor(),
               download=True)

test_dataset = datasets.MNIST(root='./data/',
              train=False,
              transform=transforms.ToTensor())

# Data Loader (Input Pipeline)
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)

class Net(nn.Module):
 def __init__(self):
   super(Net, self).__init__()
   # 输入1通道，输出10通道，kernel 5*5
   self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
   self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
   self.mp = nn.MaxPool2d(2)
   # fully connect
   self.fc = nn.Linear(320, 10)

def forward(self, x):
   # in_size = 64
   in_size = x.size(0) # one batch
   # x: 64*10*12*12
   x = F.relu(self.mp(self.conv1(x)))
   # x: 64*20*4*4
   x = F.relu(self.mp(self.conv2(x)))
   # x: 64*320
   x = x.view(in_size, -1) # flatten the tensor
   # x: 64*10
   x = self.fc(x)
   return F.log_softmax(x)

model = Net()

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

def train(epoch):
 for batch_idx, (data, target) in enumerate(train_loader):
   data, target = Variable(data), Variable(target)
   optimizer.zero_grad()
   output = model(data)
   loss = F.nll_loss(output, target)
   loss.backward()
   optimizer.step()
   if batch_idx ％ 200 == 0:
     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():
 test_loss = 0
 correct = 0
 for data, target in test_loader:
   data, target = Variable(data, volatile=True), Variable(target)
   output = model(data)
   # sum up batch loss
   test_loss += F.nll_loss(output, target, size_average=False).data[0]
   # get the index of the max log-probability
   pred = output.data.max(1, keepdim=True)[1]
   correct += pred.eq(target.data.view_as(pred)).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, 10):
 train(epoch)
 test()

输出结果：

Train Epoch: 1 [0/60000 (0％)]   Loss: 2.315724
Train Epoch: 1 [12800/60000 (21％)]  Loss: 1.931551
Train Epoch: 1 [25600/60000 (43％)]  Loss: 0.733935
Train Epoch: 1 [38400/60000 (64％)]  Loss: 0.165043
Train Epoch: 1 [51200/60000 (85％)]  Loss: 0.235188

Test set: Average loss: 0.1935, Accuracy: 9421/10000 (94％)

Train Epoch: 2 [0/60000 (0％)]   Loss: 0.333513
Train Epoch: 2 [12800/60000 (21％)]  Loss: 0.163156
Train Epoch: 2 [25600/60000 (43％)]  Loss: 0.213840
Train Epoch: 2 [38400/60000 (64％)]  Loss: 0.141114
Train Epoch: 2 [51200/60000 (85％)]  Loss: 0.128191

Test set: Average loss: 0.1180, Accuracy: 9645/10000 (96％)

Train Epoch: 3 [0/60000 (0％)]   Loss: 0.206469
Train Epoch: 3 [12800/60000 (21％)]  Loss: 0.234443
Train Epoch: 3 [25600/60000 (43％)]  Loss: 0.061048
Train Epoch: 3 [38400/60000 (64％)]  Loss: 0.192217
Train Epoch: 3 [51200/60000 (85％)]  Loss: 0.089190

Test set: Average loss: 0.0938, Accuracy: 9723/10000 (97％)

Train Epoch: 4 [0/60000 (0％)]   Loss: 0.086325
Train Epoch: 4 [12800/60000 (21％)]  Loss: 0.117741
Train Epoch: 4 [25600/60000 (43％)]  Loss: 0.188178
Train Epoch: 4 [38400/60000 (64％)]  Loss: 0.049807
Train Epoch: 4 [51200/60000 (85％)]  Loss: 0.174097

Test set: Average loss: 0.0743, Accuracy: 9767/10000 (98％)

Train Epoch: 5 [0/60000 (0％)]   Loss: 0.063171
Train Epoch: 5 [12800/60000 (21％)]  Loss: 0.061265
Train Epoch: 5 [25600/60000 (43％)]  Loss: 0.103549
Train Epoch: 5 [38400/60000 (64％)]  Loss: 0.019137
Train Epoch: 5 [51200/60000 (85％)]  Loss: 0.067103

Test set: Average loss: 0.0720, Accuracy: 9781/10000 (98％)

Train Epoch: 6 [0/60000 (0％)]   Loss: 0.069251
Train Epoch: 6 [12800/60000 (21％)]  Loss: 0.075502
Train Epoch: 6 [25600/60000 (43％)]  Loss: 0.052337
Train Epoch: 6 [38400/60000 (64％)]  Loss: 0.015375
Train Epoch: 6 [51200/60000 (85％)]  Loss: 0.028996

Test set: Average loss: 0.0694, Accuracy: 9783/10000 (98％)

Train Epoch: 7 [0/60000 (0％)]   Loss: 0.171613
Train Epoch: 7 [12800/60000 (21％)]  Loss: 0.078520
Train Epoch: 7 [25600/60000 (43％)]  Loss: 0.149186
Train Epoch: 7 [38400/60000 (64％)]  Loss: 0.026692
Train Epoch: 7 [51200/60000 (85％)]  Loss: 0.108824

Test set: Average loss: 0.0672, Accuracy: 9793/10000 (98％)

Train Epoch: 8 [0/60000 (0％)]   Loss: 0.029188
Train Epoch: 8 [12800/60000 (21％)]  Loss: 0.031202
Train Epoch: 8 [25600/60000 (43％)]  Loss: 0.194858
Train Epoch: 8 [38400/60000 (64％)]  Loss: 0.051497
Train Epoch: 8 [51200/60000 (85％)]  Loss: 0.024832

Test set: Average loss: 0.0535, Accuracy: 9837/10000 (98％)

Train Epoch: 9 [0/60000 (0％)]   Loss: 0.026706
Train Epoch: 9 [12800/60000 (21％)]  Loss: 0.057807
Train Epoch: 9 [25600/60000 (43％)]  Loss: 0.065225
Train Epoch: 9 [38400/60000 (64％)]  Loss: 0.037004
Train Epoch: 9 [51200/60000 (85％)]  Loss: 0.057822

Test set: Average loss: 0.0538, Accuracy: 9829/10000 (98％)

Process finished with exit code 0

参考：https://github.com/hunkim/PyTorchZeroToAll

来源：https://blog.csdn.net/m0_37306360/article/details/79311501