pytorch-10. Convolutional Neural Networks (homework)

手算推导：
1 × 28 × 28 -> 10 × 24 × 24(conv) -> 10 × 12 × 12(maxpooling) -> 20 × 10 × 10 (conv)  
-> 20 × 5 × 5(maxpooling) -> 30 × 4 × 4(conv) 
-> 30 × 2 × 2(maxpooling) -> 120,64 (Linear)-> 64,32(Linear) -> 32,10(Linear)

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307),(0.3081))
])

train_dataset = datasets.MNIST(root='../dataset/mnist/',train=True,download=True,transform=transform)

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

test_dataset = datasets.MNIST(root='../dataset/mnist/',train=False,download=True,transform=transform)

test_loader = DataLoader(train_dataset,shuffle = False,batch_size=batch_size)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10,20,kernel_size=3)  # input_channel , output_channel
        self.conv3 = torch.nn.Conv2d(20, 30, kernel_size=2)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc1 = torch.nn.Linear(120,64)
        self.fc2 = torch.nn.Linear(64, 32)
        self.fc3 = torch.nn.Linear(32, 10)

    '''
    1 × 28 × 28 -> 10 × 24 × 24(conv) -> 10 × 12 × 12(maxpooling) -> 20 × 10 × 10 (conv)  -> 20 × 5 × 5(maxpooling) -> 30 × 4 × 4(conv) 
    -> 30 × 2 × 2(maxpooling) -> 120,64 (Linear)-> 64,32(Linear) -> 32,10(Linear)
    '''

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

model = Net()
device = torch.device("cuda:0"if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

def train(epoch):
    running_loss = 0.0
    for batch_idx,data in enumerate(train_loader,0):
        inputs, target = data
        inputs, target = inputs.to(device),target.to(device) #送到GPU
        optimizer.zero_grad()

        #forward + backward + update
        outputs = model(inputs)
        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/300))
            running_loss = 0

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images,labels = data
            images, labels = images.to(device), labels.to(device)  # 送到GPU
            outputs = model(images)
            _,predicted = torch.max(outputs.data,dim=1)  #dim=1维度1,行是第0个维度,列是第1个维度
            total +=labels.size(0)
            correct +=(predicted==labels).sum().item()
    print('Accuracy on test set:%d %%'%(100*correct/total) )


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

结果：

[1,  300]loss:2.235
[1,  600]loss:0.707
[1,  900]loss:0.239
Accuracy on test set:94 %
[2,  300]loss:0.164
[2,  600]loss:0.127
[2,  900]loss:0.120
Accuracy on test set:96 %
[3,  300]loss:0.103
[3,  600]loss:0.103
[3,  900]loss:0.095
Accuracy on test set:97 %
[4,  300]loss:0.088
[4,  600]loss:0.080
[4,  900]loss:0.079
Accuracy on test set:97 %
[5,  300]loss:0.072
[5,  600]loss:0.073
[5,  900]loss:0.070
Accuracy on test set:97 %
[6,  300]loss:0.074
[6,  600]loss:0.062
[6,  900]loss:0.060
Accuracy on test set:98 %
[7,  300]loss:0.060
[7,  600]loss:0.057
[7,  900]loss:0.060
Accuracy on test set:98 %
[8,  300]loss:0.050
[8,  600]loss:0.061
[8,  900]loss:0.048
Accuracy on test set:98 %
[9,  300]loss:0.051
[9,  600]loss:0.049
[9,  900]loss:0.048
Accuracy on test set:98 %
[10,  300]loss:0.046
[10,  600]loss:0.045
[10,  900]loss:0.050
Accuracy on test set:98 %

Process finished with exit code 0