[人工智能]PyTorch深度学习实践概论笔记10练习-Pytorch实现手写数字（MNIST）识别

简单回顾一下PyTorch深度学习实践概论笔记10-卷积神经网络基础篇的练习题。如下图所示：

? Try a more complex CNN:（尝试更复杂的CNN）

????????? Conv2d Layer *3

????????? ReLU Layer * 3

????????? MaxPooling Layer * 3

????????? Linear Layer * 3

? Try different configuration of this CNN:（尝试不同的CNN配置）

????????? Compare their performance.

老师给的课后练习中建议的CNN包含3个卷积层、3个ReLU激活层、3个池化层和3个线性层，我自己尝试去构造了一下，没有成功。然后我就去掉了一个池化层，构造了一个包含3个卷积层（3*3卷积层）、3个ReLU激活层、2个池化层（最大池化层）和3个线性层的CNN。

【小建议】在自己搭建模型时，最好在forward函数中打印x.shape，有助于之后解决bug！?

具体设计模型的代码如下：

#2.设计模型
class ExNet(torch.nn.Module):

    def __init__(self):
        super(ExNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=3)
        self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=3)
        self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=3)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc1 = torch.nn.Linear(1600, 512)  
        self.fc2 = torch.nn.Linear(512, 64)  
        self.fc3 = torch.nn.Linear(64, 10)

    def forward(self, x):
        batch_size = x.size(0)
#         x = x.view(-1,1*28*28)
        x = F.relu(self.conv1(x)) 
        print(x.shape)
        x = self.pooling(F.relu(self.conv2(x)))
        print(x.shape)
        x = self.pooling(F.relu(self.conv3(x)))
        print(x.shape)
        #方法一：
#         x = x.view(-1,64*4*4) # flatten
        #方法二：
        x = x.view(batch_size,-1)
        print(x.shape)
        x = F.relu(self.fc1(x))
        print(x.shape)
        x = F.relu(self.fc2(x))
        print(x.shape)
        x = self.fc3(x)
        print(x.shape)
        return x
     
modele = ExNet()
print(modele)

输出模型结果如下：

ExNet(
  (conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1))
  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1))
  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))
  (pooling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (fc1): Linear(in_features=1600, out_features=512, bias=True)
  (fc2): Linear(in_features=512, out_features=64, bias=True)
  (fc3): Linear(in_features=64, out_features=10, bias=True)
)

其他部分的代码和本节内容类似，最后测试集的准确率为99%，和之前9%相比，准确率大大提高。

完整的代码如下（可跑通）：

#0.导库
import torch
#构建DataLoader的库
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
#使用函数relu()的库
import torch.nn.functional as F
#构建优化器的库
import torch.optim as optim#1.准备数据集
# batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(), #将PIL图像转化成Tensor
    transforms.Normalize((0.1307, ), (0.3081, ))   #正则化，归一化，0.1307是均值，0.3081是标准差，这两个值是根据所有数据集算出来的

])

train_dataset = datasets.MNIST(root='./data/',
    		train=True,
    		download=False,
    		transform=transform)

train_loader = DataLoader(train_dataset,
    		shuffle=True,
    		batch_size=64)
      

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

test_loader = DataLoader(test_dataset,
    		shuffle=False,
    		batch_size=64)

#2.设计模型
class ExNet(torch.nn.Module):

    def __init__(self):
        super(ExNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=3)
        self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=3)
        self.conv3 = torch.nn.Conv2d(32, 64, kernel_size=3)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc1 = torch.nn.Linear(1600, 512)  
        self.fc2 = torch.nn.Linear(512, 64)  
        self.fc3 = torch.nn.Linear(64, 10)

    def forward(self, x):
        batch_size = x.size(0)
        x = F.relu(self.conv1(x)) 
        x = self.pooling(F.relu(self.conv2(x)))
        x = self.pooling(F.relu(self.conv3(x)))
        x = x.view(batch_size,-1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
     
modele = ExNet()
# print(modele)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#把整个模型的参数，缓存，所有的模块都放到cuda里面，转成cuda tensor
modele.to(device)

#3.构造损失和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(modele.parameters(), lr=0.01, momentum=0.5)

#4.训练代码与测试代码
def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        #print("inputs.shape",inputs.shape)#torch.Size([64])
        #print("target.shape",target.shape)#torch.Size([64, 1, 28, 28])
        #加入下面这行，把每一步的inputs和targets迁移到GPU
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()

        # forward + backward + update
        outputs = modele(inputs)
        #print("inputs.shape",inputs.shape)#torch.Size([64, 1, 28, 28])
        #print("outputs.shape",outputs.shape)#torch.Size([100, 10])
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 2000))
            running_loss = 0.0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            inputs, target = data
            #加入下面这行，把每一步的inputs和targets迁移到GPU
            inputs, target = inputs.to(device), target.to(device)
            outputs = modele(inputs)
            _, predicted = torch.max(outputs.data, dim=1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
        print('Accuracy on test set: %d %% [%d/%d]' % (100 * correct / total, correct, total))
    
if __name__ == '__main__':
    for epoch in range(10):
    	train(epoch)
    	test()

运行结果如下：

[1,   300] loss: 0.207
[1,   600] loss: 0.044
[1,   900] loss: 0.026
Accuracy on test set: 96 % [9655/10000]
[2,   300] loss: 0.017
[2,   600] loss: 0.015
[2,   900] loss: 0.013
Accuracy on test set: 97 % [9786/10000]
[3,   300] loss: 0.010
[3,   600] loss: 0.011
[3,   900] loss: 0.009
Accuracy on test set: 98 % [9850/10000]
[4,   300] loss: 0.008
[4,   600] loss: 0.008
[4,   900] loss: 0.008
Accuracy on test set: 98 % [9840/10000]
[5,   300] loss: 0.006
[5,   600] loss: 0.006
[5,   900] loss: 0.006
Accuracy on test set: 98 % [9866/10000]
[6,   300] loss: 0.005
[6,   600] loss: 0.005
[6,   900] loss: 0.005
Accuracy on test set: 98 % [9883/10000]
[7,   300] loss: 0.005
[7,   600] loss: 0.004
[7,   900] loss: 0.004
Accuracy on test set: 98 % [9870/10000]
[8,   300] loss: 0.003
[8,   600] loss: 0.004
[8,   900] loss: 0.004
Accuracy on test set: 99 % [9905/10000]
[9,   300] loss: 0.003
[9,   600] loss: 0.003
[9,   900] loss: 0.003
Accuracy on test set: 98 % [9894/10000]
[10,   300] loss: 0.003
[10,   600] loss: 0.002
[10,   900] loss: 0.002
Accuracy on test set: 98 % [9874/10000]

?可以看到输出结果最后的一次test准确率有99%，效果不错。

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