{"id":30058,"date":"2024-10-28T03:19:23","date_gmt":"2024-10-28T03:19:23","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30058"},"modified":"2024-11-26T06:50:13","modified_gmt":"2024-11-26T06:50:13","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%ec%84%a4%eb%aa%85-%ea%b0%80%eb%8a%a5%ed%95%9c-cnn","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30058\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc124\uba85 \uac00\ub2a5\ud55c CNN"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860: \ub525\ub7ec\ub2dd\uacfc CNN\uc758 \ubc1c\uc804<\/h2>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ub2a5\ub825\uc744 \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional Neural Networks, CNN)\uc740 \uc774\ubbf8\uc9c0 \ucc98\ub9ac\ub97c \uc704\ud55c \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. CNN\uc740 \uc800\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ucd94\ucd9c\ud558\uace0, \uace0\ucc28\uc6d0 \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uad6c\uc870\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc CNN\uc758 \ub0b4\ubd80 \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc774\ud574\ud558\uae30 \uc5b4\ub824\uc6b4 \uc810\uc774 \uc788\uc5b4, \uc124\uba85 \uac00\ub2a5\uc131(Explainability)\uc740 \ud604\uc7ac \ub9ce\uc740 \uc5f0\uad6c\uc790\ub4e4\uc774 \uc8fc\ubaa9\ud558\ub294 \uc8fc\uc81c\uc785\ub2c8\ub2e4.<\/p>\n

2. \uc124\uba85 \uac00\ub2a5\ud55c \ub525\ub7ec\ub2dd\uc758 \ud544\uc694\uc131<\/h2>\n

\ub525\ub7ec\ub2dd \ubaa8\ub378, \ud2b9\ud788 CNN\uacfc \uac19\uc740 \ubcf5\uc7a1\ud55c \uad6c\uc870\uc758 \ubaa8\ub378\uc740 ‘\ube14\ub799\ubc15\uc2a4’\ub85c \uc778\uc2dd\ub418\uace4 \ud569\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc774 \uc5b4\ub5bb\uac8c \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294\uc9c0 \uc774\ud574\ud558\uae30 \uc5b4\ub835\ub2e4\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, \uc124\uba85 \uac00\ub2a5\ud55c CNN \ubaa8\ub378\uc758 \uac1c\ubc1c\uc774 \ub354\uc6b1 \uc911\uc694\ud574\uc84c\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc0ac\uc6a9\uc790\uac00 \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\uba70, \ubaa8\ub378\uc758 \uc2e0\ub8b0\uc131\uc744 \ub192\uc774\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4.<\/p>\n

3. PyTorch\ub85c CNN \uad6c\ud604\ud558\uae30<\/h2>\n

\uba3c\uc800 CNN\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uae30\ubcf8 \uc124\uc815\uc744 \ud558\ub098\uc529 \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \uac15\ub825\ud55c \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc6b0\ub9ac\uc758 CNN\uc744 \uc27d\uac8c \uad6c\ucd95\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \uac00\uc7a5 \uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0, \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

3.1 PyTorch \uc124\uce58<\/h3>\n
pip install torch torchvision<\/code><\/pre>\n
3.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\uc5ec\uae30\uc11c\ub294 CIFAR-10 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. CIFAR-10\uc740 10\uac1c\uc758 \ud074\ub798\uc2a4\uac00 \ud3ec\ud568\ub41c 60,000\uac1c\uc758 32×32 \ud53d\uc140 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. PyTorch\uc758 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud574\uc11c \uc27d\uac8c \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658\ntransform = transforms.Compose(\n    [transforms.ToTensor(),\n     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\n# CIFAR-10 \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\ntrainset = torchvision.datasets.CIFAR10(root='.\/data', train=True,\n                                        download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n                                          shuffle=True, num_workers=2)\n\ntestset = torchvision.datasets.CIFAR10(root='.\/data', train=False,\n                                       download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=4,\n                                         shuffle=False, num_workers=2)\n    <\/code><\/pre>\n
3.3 CNN \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n
\uc774\uc81c CNN \ubaa8\ub378\uc744 \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c CNN \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc11c\ub85c \ub2e4\ub978 \uce35\uc744 \uc313\uc544\uc62c\ub9ac\uaca0\uc2b5\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \ud569\uc131\uacf1(Convolutional) \uce35\uacfc \ud480\ub9c1(Pooling) \uce35\uc744 \uacb0\ud569\ud558\uc5ec \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleCNN(nn.Module):\n    def __init__(self):\n        super(SimpleCNN, self).__init__()\n        self.conv1 = nn.Conv2d(3, 6, 5)  # 3\ucc44\ub110 \uc785\ub825, 6\ucc44\ub110 \ucd9c\ub825, \ucee4\ub110 \ud06c\uae30 5\n        self.pool = nn.MaxPool2d(2, 2)   # 2x2 \ucd5c\ub300 \ud480\ub9c1\n        self.conv2 = nn.Conv2d(6, 16, 5) # 6\ucc44\ub110 \uc785\ub825, 16\ucc44\ub110 \ucd9c\ub825, \ucee4\ub110 \ud06c\uae30 5\n        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # Fully connected layer\n        self.fc2 = nn.Linear(120, 84)\n        self.fc3 = nn.Linear(84, 10)\n\n    def forward(self, x):\n        x = self.pool(F.relu(self.conv1(x)))\n        x = self.pool(F.relu(self.conv2(x)))\n        x = x.view(-1, 16 * 5 * 5)  # Flattening the output\n        x = F.relu(self.fc1(x))\n        x = F.relu(self.fc2(x))\n        x = self.fc3(x)\n        return x\n    <\/code><\/pre>\n
3.4 \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h3>\n
\ubaa8\ub378\uc744 \uc815\uc758\ud588\uc73c\uba74, \uc774\uc81c \ud559\uc2b5 \uacfc\uc815\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\uc744 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud558\uace0, \uc5d0\ud3ec\ud06c \ub2e8\uc704\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4 \uc0dd\uc131\nnet = SimpleCNN()\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(2):  # \ubc18\ubcf5\ud69f\uc218 \uc124\uc815\n    running_loss = 0.0\n    for i, data in enumerate(trainloader, 0):\n        inputs, labels = data\n        optimizer.zero_grad()  # \uae30\uc874 \uadf8\ub798\ub514\uc5b8\ud2b8 \ucd08\uae30\ud654\n        outputs = net(inputs)  # \ubaa8\ub378 \uc608\uce21\n        loss = criterion(outputs, labels)  # \uc190\uc2e4 \uacc4\uc0b0\n        loss.backward()  # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0\n        optimizer.step()  # \ud30c\ub77c\ubbf8\ud130 \uc5c5\ub370\uc774\ud2b8\n        running_loss += loss.item()\n        if i % 2000 == 1999:  # \ub9e4 2000\ubc88\uc9f8 \ubc30\uce58\ub9c8\ub2e4 \ucd9c\ub825\n            print(f\"[{epoch + 1}, {i + 1}] \uc190\uc2e4: {running_loss \/ 2000:.3f}\")\n            running_loss = 0.0\n    print(\"\ud559\uc2b5 \uc644\ub8cc!\")\n    <\/code><\/pre>\n
3.5 \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud3c9\uac00\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc815\ud655\ub3c4\ub97c \uce21\uc815\ud558\ub294 \uac83\uc73c\ub85c, \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \uc798 \ud559\uc2b5\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n    for data in testloader:\n        images, labels = data\n        outputs = net(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint(f'\uc815\ud655\ub3c4: {100 * correct \/ total:.2f}%')\n    <\/code><\/pre>\n
4. \uc124\uba85 \uac00\ub2a5\ud55c CNN \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c CNN\uc744 \uc124\uba85 \uac00\ub2a5\ud558\uac8c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud558\ub098\uc758 \uc811\uadfc\ubc95\uc73c\ub85c\ub294 Grad-CAM (Gradient-weighted Class Activation Mapping) \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec, \ubaa8\ub378\uc758 \uc5b4\ub5a4 \ubd80\ubd84\uc774 \uc608\uce21\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\ucce4\ub294\uc9c0\ub97c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 Grad-CAM \uc815\uc758\ud558\uae30<\/h3>\n
Grad-CAM\uc740 CNN\uc758 \uc608\uce21\uc5d0 \ub300\ud55c \uae30\uc5ec\ub3c4\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc0ac\uc6a9\uc790\uc5d0\uac8c \ubaa8\ub378\uc758 \ud574\uc11d \uac00\ub2a5\uc131\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 Grad-CAM\uc744 \uad6c\ud604\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef grad_cam(input_model, image, category_index):\n    # \ubaa8\ub378\uc758 \ub9c8\uc9c0\ub9c9 \ud569\uc131\uacf1 \uce35\uc744 \uac00\uc838\uc635\ub2c8\ub2e4.\n    final_conv_layer = 'conv2'\n    grad_model = nn.Sequential(*list(input_model.children())[:-1])\n    \n    with torch.enable_grad():\n        # \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ud150\uc11c\ub85c \ubcc0\ud658\n        inputs = image.unsqueeze(0)  # \ubc30\uce58 \ucc28\uc6d0 \ucd94\uac00\n        inputs.requires_grad = True  # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0 \uac00\ub2a5\ud558\uac8c \uc124\uc815\n        preds = grad_model(inputs)  # \uc608\uce21\n        class_channel = preds[0][category_index]  # \uad00\uc2ec\uc788\ub294 \ud074\ub798\uc2a4 \ucc44\ub110\n        \n        # \uc608\uce21 \ud074\ub798\uc2a4\uc5d0 \ub300\ud55c \uadf8\ub798\ub514\uc5b8\ud2b8\ub97c \uacc4\uc0b0\n        grad_model.zero_grad()\n        class_channel.backward()\n        \n        # \ub9c8\uc9c0\ub9c9 \ud569\uc131\uacf1 \uce35\uc758 \ucd9c\ub825\uacfc \uadf8\ub798\ub514\uc5b8\ud2b8 \uac00\uc838\uc624\uae30\n        conv_layer_output = grad_model[-1].forward(inputs).cpu().data.numpy()\n        gradients = grad_model[-1].weight.grad.cpu().data.numpy()\n        \n        # Grad-CAM\uc744 \uc0dd\uc131\ud558\uae30 \uc704\ud55c \ube44\uc728 \uacc4\uc0b0\n        alpha = np.mean(gradients, axis=(2, 3))[0, :]\n        cam = np.dot(alpha, conv_layer_output[0])  # \uae30\uc5ec\ub3c4 \uacc4\uc0b0\n        cam = np.maximum(cam, 0)  # ReLU \uc801\uc6a9\n        cam = cam \/ np.max(cam)  # \uc815\uaddc\ud654\n        \n        # \uc6d0\ubcf8 \uc774\ubbf8\uc9c0\uc5d0 \ub367\ubd99\uc785\ub2c8\ub2e4.\n        return cam\n    <\/code><\/pre>\n
4.2 Grad-CAM \uc801\uc6a9\ud558\uae30<\/h3>\n
\uc774\uc81c \ud559\uc2b5\ub41c \ubaa8\ub378\uc5d0 Grad-CAM\uc744 \uc801\uc6a9\ud558\uc5ec \uba87 \uac00\uc9c0 \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n# \uc608\uc2dc \uc774\ubbf8\uc9c0 \ubd88\ub7ec\uc624\uae30\nimage, label = testset[0]\ncategory_index = label  # \uad00\uc2ec \ud074\ub798\uc2a4 \uc778\ub371\uc2a4\ncam = grad_cam(net, image, category_index)\n\n# \uc6d0\ubcf8 \uc774\ubbf8\uc9c0\uc640 Grad-CAM \uc5f4\ub9f5 \uc2dc\uac01\ud654\nplt.subplot(1, 2, 1)\nplt.imshow(image.permute(1, 2, 0))\nplt.title('\uc6d0\ubcf8 \uc774\ubbf8\uc9c0')\n\nplt.subplot(1, 2, 2)\nplt.imshow(cam, cmap='jet', alpha=0.5)  # \uc0c9\uc0c1 \ub9f5 \uc801\uc6a9\nplt.title('Grad-CAM \uc5f4\ub9f5')\nplt.show()\n    <\/code><\/pre>\n
5. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc5d0\uc11c \uc124\uba85 \uac00\ub2a5\uc131\uc740 \uc810\uc810 \ub354 \uc911\uc694\ud55c \uc8fc\uc81c\uac00 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. CNN\uc758 \ub0b4\ubd80 \uc791\uc791\uc744 \uc774\ud574\ud558\uace0 \uadf8 \uacb0\uacfc\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \uc124\uba85\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec CNN\uc744 \uad6c\ud604\ud558\uace0, Grad-CAM\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc608\uce21\uc744 \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774 \uacfc\uc815\uc740 \ub2e8\uc21c\ud55c CNN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac83\uc5d0\uc11c \uc2dc\uc791\ud558\uc5ec, \ucd5c\uc2e0\uc758 \uc124\uba85 \uac00\ub2a5\ud55c \ub525\ub7ec\ub2dd \uae30\ubc95\uc778 Grad-CAM\uc744 \ud65c\uc6a9\ud558\uc5ec CNN\uc758 \uc608\uce21\uc744 \ud574\uc11d\ud558\uace0 \uc2dc\uac01\ud654\ud558\ub294 \ub370\uae4c\uc9c0 \uc774\uc5b4\uc84c\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \uc2dc\ub3c4\ub97c \ud1b5\ud574 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uacfc \ubc29\ubc95\ub860\uc744 \ud0d0\uad6c\ud574\uc57c \ud560 \uac83\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ub3d9\uc2dc\uc5d0 \uc124\uba85 \uac00\ub2a5\ud55c AI \uc2dc\uc2a4\ud15c \uac1c\ubc1c\uc774 \uaf2d \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
6. \ub808\ud37c\ub7f0\uc2a4<\/h2>\n