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Created 4 years ago
from __future__ import print_function
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
cuda:0
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')
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))
Extracting ./data/cifar-10-python.tar.gz to ./data
Files already downloaded and verified
# 이미지를 보여주기 위한 함수
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# 학습용 이미지를 무작위로 가져오기
dataiter = iter(trainloader)
images, labels = dataiter.next()
# 이미지 보여주기
imshow(torchvision.utils.make_grid(images))
# 정답(label) 출력
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
truck cat frog deer
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()
net.cuda()
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)
)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001, )
for epoch in range(2): # 데이터셋을 수차례 반복합니다.
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# [inputs, labels]의 목록인 data로부터 입력을 받은 후;
inputs, labels = data
inputs, labels = inputs.cuda(), labels.cuda()
# 변화도(Gradient) 매개변수를 0으로 만들고
optimizer.zero_grad()
# 순전파 + 역전파 + 최적화를 한 후
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 통계를 출력합니다.
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
[1, 2000] loss: 1.907
[1, 4000] loss: 1.638
[1, 6000] loss: 1.527
[1, 8000] loss: 1.485
[1, 10000] loss: 1.420
[1, 12000] loss: 1.398
[2, 2000] loss: 1.312
[2, 4000] loss: 1.311
[2, 6000] loss: 1.279
[2, 8000] loss: 1.272
[2, 10000] loss: 1.251
[2, 12000] loss: 1.246
Finished Training