CIFAR-10 dataset에 대하여 [기본적인 CNN + Data Augmentation] 적용 => 정확도 65%
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import torch
import numpy as np
import os
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms, datasets
import torch.nn.init as init
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
BATCH_SIZE = 64
EPOCHS = 10
if torch.cuda.is_available():
DEVICE = torch.device('cuda')
else:
DEVICE = torch.device('cpu')
print(DEVICE)
# Data Augmentation 기법 사용
trans = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root="./data/CIFAR_10",
train=True,
download=True,
transform=trans)
test_dataset = datasets.CIFAR10(root="./data/CIFAR_10",
train=False,
download=True,
transform=trans)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=BATCH_SIZE,
shuffle=False)
# 다운로드 받은 데이터셋 확인
for (x_train, y_train) in train_loader:
print('x_train: ', x_train.size(), ' data_type: ', x_train.type())
print('y_train: ', y_train.size(), ' data_type: ', y_train.type())
break
fig = plt.figure(figsize=(5, 1))
for i in range(5):
plt.subplot(1, 5, i + 1)
plt.axis('off')
plt.imshow(np.transpose(x_train[i], (1, 2, 0)))
plt.title("class: " + str(y_train[i].item()))
plt.show()
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=3,
out_channels=16,
kernel_size=3,
padding=1
)
self.conv2 = nn.Conv2d(
in_channels=16,
out_channels=32,
kernel_size=3,
padding=1
)
self.pooling = nn.MaxPool2d(
kernel_size=2,
stride=2
)
self.dense1 = nn.Linear(8*8*32, 128)
self.dense2 = nn.Linear(128, 64)
self.dense3 = nn.Linear(64, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pooling(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pooling(x)
x = x.view(-1, 8*8*32)
x = self.dense1(x)
x = F.relu(x)
x = self.dense2(x)
x = F.relu(x)
x = self.dense3(x)
x = F.softmax(x)
return x
def weight_initializer(m):
if isinstance(m, nn.Linear):
init.kaiming_uniform_(m.weight.data)
model = CNN().to(DEVICE)
model.apply(weight_initializer) #가중치 초기화 기법을 사용
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()
print(model)
def train(model, train_loader, optimizer, interval):
model.train()
for idx, (image, label) in enumerate(train_loader):
image = image.to(DEVICE)
label = label.to(DEVICE)
optimizer.zero_grad()
output = model(image)
loss = criterion(output, label)
loss.backward()
optimizer.step()
if idx % interval == 0:
print('train epoch: {}, {}/{} train_loss: {}'
.format(epoch, idx*len(image), len(train_loader.dataset), loss.item()))
def evaluate(model, test_loader):
model.eval()
test_loss = 0
right = 0
with torch.no_grad():
for image, label in test_loader:
image = image.to(DEVICE)
label = label.to(DEVICE)
output = model(image)
test_loss += criterion(output, label).item()
pred = output.max(1, keepdim=True)[1]
right += pred.eq(label.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
test_acc = right/len(test_loader.dataset) * 100
return test_loss, test_acc
for epoch in range(1, EPOCHS+1):
train(model, train_loader, optimizer, 200)
test_loss, test_acc = evaluate(model, test_loader)
print("test_loss: {}, test_acc: {}".format(test_loss, test_acc))
|
cs |
위의 소스코드를 실행시키면, 아래와 같은 결과값을 얻을 수 있다.
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