add model file

.gitignore

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cifar_net.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf87afeab91ee70d644d0f22733711e058dfa3d20edb740b01cb1abe0ddd2b4e
+size 251167

pytorch_classifier_gen.py

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+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
+
+# 데이터셋 불러오기
+transform = transforms.Compose(
+    [transforms.ToTensor(),
+     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+
+batch_size = 4
+
+trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+                                        download=True, transform=transform)
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
+                                          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=batch_size,
+                                         shuffle=False, num_workers=2)
+
+classes = ('plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+
+# 합성곱 신경망 만들기
+class Net(nn.Module):
+    def __init__(self):
+        super().__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 = torch.flatten(x, 1) # 배치를 제외한 모든 차원을 평탄화(flatten)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+net = Net()
+
+# 손실 함수와 오티마이져
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+# 학습하기
+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
+
+        # 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(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
+            running_loss = 0.0
+
+print('Finished Training')
+
+# 모델 저장하기
+PATH = './cifar_net.pth'
+torch.save(net.state_dict(), PATH)