### import torch and some packages, and downloading CIFAR10 dataset
import torch
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')
Files already downloaded and verified
Files already downloaded and verified
testset = torchvision.datasets.CIFAR10(root="./data", train=False,
download=True, transform=transform)
Files already downloaded and verified
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')
Files already downloaded and verified
Files already downloaded and verified
import matplotlib.pyplot as plt
import numpy as np
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
<Figure size 640x480 with 1 Axes>
horse bird ship cat
### show some images
import matplotlib.pyplot as plt
import numpy as np
def imshow(img):
img = img/2 + 0.5
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1,2,0)))
plt.show()
dataiter = iter(trainloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print(' '.join('%5s' % classes[labels]) for j in range(4))
<generator object <genexpr> at 0x7f6d8dacde08>
### define a Convolutional Neural Network
import torch.nn as nn
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))
x = self.fc3(x)
return x
net = Net()
print(net)
Net(
(conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(fc1): Linear(in_features=400, out_features=120, bias=True)
(fc2): Linear(in_features=120, out_features=84, bias=True)
(fc3): Linear(in_features=84, out_features=10, bias=True)
)
### define loss function as optimizer
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
### Train the network
for epoch in range(2):
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))
print('Finished Training')
[1, 2000] loss : 2.212
[1, 4000] loss : 4.095
[1, 6000] loss : 5.784
[1, 8000] loss : 7.361
[1, 10000] loss : 8.859
[1, 12000] loss : 10.343
[2, 2000] loss : 1.413
[2, 4000] loss : 2.789
[2, 6000] loss : 4.143
[2, 8000] loss : 5.459
[2, 10000] loss : 6.777
[2, 12000] loss : 8.078
Finished Training
### test the network on the test data
dataiter = iter(testloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print('GroundTruth : ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
GroundTruth : cat ship ship plane
outputs = net(images)
_, predicted = torch.max(outputs, 1)
print('Predicted : ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))
Predicted : cat car car plane
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print("Accuracy of the network on the 10000 test images : %d %%" % (100 * correct/total))
Accuracy of the network on the 10000 test images : 53 %
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:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 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 : %2d %%" % (classes[i], 100 * class_correct[i] / class_total[i]))
Accuracy of plane : 67 %
Accuracy of car : 54 %
Accuracy of bird : 43 %
Accuracy of cat : 21 %
Accuracy of deer : 36 %
Accuracy of dog : 58 %
Accuracy of frog : 83 %
Accuracy of horse : 47 %
Accuracy of ship : 56 %
Accuracy of truck : 70 %
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cuda:0
net.to(device)
inputs, labels = inputs.to(device), labels.to(device)
