"""prepare the data"""
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset = torchvision.datasets.CIFAR10(root = './data',train = True,download = True,transform = transform)
trainloader = torch.utils.data.DataLoader(trainset,batch_size = 4,shuffle = True,num_workers = 2)
testset = torchvision.datasets.CIFAR10(root = './data',train = False,download = True,transform = transform)
testloader = torch.utils.data.DataLoader(testset,batch_size = 4,shuffle = False,num_workers = 2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
"""prepare the net"""
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.conv1 = nn.Conv2d(3,6,5)
self.pool = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(6,16,5)
self.fc1 = nn.Linear(16*5*5,120)
self.fc2 = nn.Linear(120,84)
self.fc3 = nn.Linear(84,10)
def forward(self,x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1,16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return x
net = Net()
""""define the loss and optimizer"""
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(),lr = 0.001,momentum = 0.9)
"""train the net"""
for epoch in range(1):
running_loss = 0.0
for i,data in enumerate(trainloader,0):
inputs,labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs,labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i %2000 == 1999:
print('[%d,%5d] loss:%.3f'%(epoch+1,i+1,running_loss/2000))
running_loss = 0.0
print ('Finished Training')
"""Testing"""
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
imgs,labels = data
out = net(imgs)
_,predicted = torch.max(out.data,1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print ('Accuracy on 10000 test images: %d %%'
%(100*correct/total))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
imgs,labels = data
out = net(imgs)
_,predicted = torch.max(out,1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print("Accuracy of %5s : %d %%"%(classes[i],100*class_correct[i]/class_total[label]))
