CNN예제

박영욱·2023년 6월 30일

CNN 딥러닝 인공지능

CNN 예제를 살펴보면,,

#라이브러리 호출

import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F

import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader

#CPU 혹은 GPU 확인

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

#fashion_mnist 데이터셋 불러오기
train_dataset = torchvision.datasets.FashionMNIST("FashionMNIST/", download=True, transform=
transforms.Compose([transforms.ToTensor()]))
test_dataset = torchvision.datasets.FashionMNIST("FashionMNIST/", download=True, train=False, transform=
transforms.Compose([transforms.ToTensor()]))

#fashion_mnist 데이터를 데이터로더에 전달

train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=100)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=100)

#분류에 사용될 클래스 정의

labels_map = {0 : 'T-Shirt', 1 : 'Trouser', 2 : 'Pullover', 3 : 'Dress', 4 : 'Coat', 5 : 'Sandal', 6 : 'Shirt',
7 : 'Sneaker', 8 : 'Bag', 9 : 'Ankle Boot'}

fig = plt.figure(figsize=(8,8));
columns = 4;
rows = 5;
for i in range(1, columns*rows +1):
img_xy = np.random.randint(len(train_dataset));
img = train_dataset[img_xy][0][0,:,:]
fig.add_subplot(rows, columns, i)
plt.title(labels_map[train_dataset[img_xy][1]])
plt.axis('off')
plt.imshow(img, cmap='gray')
plt.show()

class FashionDNN(nn.Module):
def init(self):
super(FashionDNN,self).init()
self.fc1 = nn.Linear(in_features=784,out_features=256)
self.drop = nn.Dropout2d(0.25)
self.fc2 = nn.Linear(in_features=256,out_features=128)
self.fc3 = nn.Linear(in_features=128,out_features=10)

def forward(self,input_data):
    out = input_data.view(-1, 784)
    out = F.relu(self.fc1(out))
    out = self.drop(out)
    out = F.relu(self.fc2(out))
    out = self.fc3(out)
    return out

#학습률

learning_rate = 0.001;
model = FashionDNN();
model.to(device)

criterion = nn.CrossEntropyLoss();
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate);
print(model)

#에폭

num_epochs = 5
count = 0
loss_list = []
iteration_list = []
accuracy_list = []

predictions_list = []
labels_list = []

for epoch in range(num_epochs):
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)

train = Variable(images.view(100, 1, 28, 28))
labels = Variable(labels)

outputs = model(train)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
count += 1

if not (count % 50):
total = 0
correct = 0
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
labels_list.append(labels)
test = Variable(images.view(100, 1, 28, 28))
outputs = model(test)
predictions = torch.max(outputs, 1)[1].to(device)
predictions_list.append(predictions)
correct += (predictions == labels).sum()
total += len(labels)

accuracy = correct * 100 / total
loss_list.append(loss.data)
iteration_list.append(count)
accuracy_list.append(accuracy)

if not (count % 500):
print("Iteration: {}, Loss: {}, Accuracy: {}%".format(count, loss.data, accuracy))

class FashionCNN(nn.Module):
def init(self):
super(FashionCNN, self).init()
self.layer1 = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layer2 = nn.Sequential(
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2)
)
self.fc1 = nn.Linear(in_features=6466, out_features=600)
self.drop = nn.Dropout2d(0.25)
self.fc2 = nn.Linear(in_features=600, out_features=120)
self.fc3 = nn.Linear(in_features=120, out_features=10)

def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1)
out = self.fc1(out)
out = self.drop(out)
out = self.fc2(out)
out = self.fc3(out)
return out

#파라미터 정의

learning_rate = 0.001;
model = FashionCNN();
model.to(device)