{"id":30076,"date":"2024-10-28T03:19:29","date_gmt":"2024-10-28T03:19:29","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30076"},"modified":"2024-11-26T06:50:06","modified_gmt":"2024-11-26T06:50:06","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%95%99%ec%83%81%eb%b8%94%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%84%b1%eb%8a%a5-%ec%b5%9c%ec%a0%81%ed%99%94","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30076\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc559\uc0c1\ube14\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654"},"content":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \uc885\ub958\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(ANN)\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525\ub7ec\ub2dd\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uac01\uc885 \uc608\uce21 \ubb38\uc81c\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 PyTorch\ub294 \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \uc801\ud569\ud55c \uac15\ub825\ud55c \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ucd95\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\ub294 \uc720\uc5f0\uc131\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \uc774\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc559\uc0c1\ube14\uc740 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uacb0\ud569\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \ubc29\ubc95\uc73c\ub85c, \ud558\ub098\uc758 \ubaa8\ub378\uc774 \uac00\uc9c0\ub294 \ub2e8\uc810\uc744 \ubcf4\uc644\ud558\uace0 \uc77c\ubc18\ud654 \ub2a5\ub825\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uc559\uc0c1\ube14\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, PyTorch\ub97c \ud65c\uc6a9\ud55c \uc2e4\uc81c \uad6c\ud604 \uc608\uc81c\uc640 \ud568\uaed8 \uc131\ub2a5 \ucd5c\uc801\ud654\ub97c \uc704\ud55c \uc804\ub7b5\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uc559\uc0c1\ube14\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>\uc559\uc0c1\ube14 \uae30\ubc95\uc740 \uc5ec\ub7ec \uac1c\uc758 \uae30\ubcf8 \ud559\uc2b5\uae30(\ubaa8\ub378)\ub97c \uacb0\ud569\ud558\uc5ec \ucd5c\uc885\uc801\uc778 \uc608\uce21 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc559\uc0c1\ube14 \uae30\ubc95\uc758 \uc8fc\uc694 \uc7a5\uc810\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uacfc\uc801\ud569(overfitting)\uc744 \uc904\uc774\uace0 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654\ub97c \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\ub2e4.<\/li>\n<li>\uc5ec\ub7ec \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc885\ud569\ud558\uc5ec \ubcf4\ub2e4 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \uc608\uce21\uc744 \ub9cc\ub4e4\uc5b4 \ub0b8\ub2e4.<\/li>\n<li>\ubaa8\ub378\uc774 \uc11c\ub85c \ub2e4\ub978 \uc624\ub958\ub97c \ubc94\ud558\ub294 \uacbd\uc6b0, \uc559\uc0c1\ube14\uc744 \ud1b5\ud574 \uc774\ub7ec\ud55c \uc624\ub958\ub97c \ubcf4\uc644\ud560 \uc218 \uc788\ub2e4.<\/li>\n<\/ul>\n<h2>2. \uc559\uc0c1\ube14 \uae30\ubc95\uc758 \uc885\ub958<\/h2>\n<p>\uc8fc\uc694 \uc559\uc0c1\ube14 \uae30\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ubc30\uae45(Bagging)<\/strong>: \ubd80\ud2b8\uc2a4\ud2b8\ub7a9 \uc0d8\ud50c\ub9c1\uc744 \ud1b5\ud574 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0, \uc774\ub4e4\uc758 \uc608\uce21\uc744 \ud3c9\uade0\ub0b4\uac70\ub098 \ud22c\ud45c\ud558\uc5ec \ucd5c\uc885 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest)\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubd80\uc2a4\ud305(Boosting)<\/strong>: \uc774\uc804 \ubaa8\ub378\uc758 \uc624\ub958\ub97c \ubcf4\uc644\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ucc28\ub840\ub300\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c \ucd5c\uc885\uc801\uc778 \uc608\uce21\uc744 \ube4c\ub4dc\ud569\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 XGBoost, AdaBoost, LightGBM\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uc2a4\ud0dc\ud0b9(Stacking)<\/strong>: \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uc870\ud569\ud558\uc5ec \uba54\ud0c0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc11c\ub85c \ub2e4\ub978 \ubaa8\ub378\uc758 \uc608\uce21\uc744 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc885\uc801\uc73c\ub85c \ub354 \ub098\uc740 \uc608\uce21\uc744 \uc0dd\uc131\ud558\ub294 \uac83\uc774 \ud2b9\uc9d5\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>3. PyTorch\uc5d0\uc11c \uc559\uc0c1\ube14 \uad6c\ud604\ud558\uae30<\/h2>\n<p>\ubcf8 \uc139\uc158\uc5d0\uc11c\ub294 PyTorch\ub97c \uc774\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc73c\ub85c\ub294 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 MNIST \uc190\uae00\uc528 \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<h3>3.1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c import\ud558\uace0 MNIST \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\nimport numpy as np\n<\/code><\/pre>\n<p>MNIST \ub370\uc774\ud130\uc14b\uc744 \uc704\ud55c \ub370\uc774\ud130\ub85c\ub354\ub97c \uc124\uc815\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>transform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntest_dataset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\n\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n<\/code><\/pre>\n<h3>3.2. \uae30\ubcf8 \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758<\/h3>\n<p>\uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 2\uac1c\uc758 \uc644\uc804 \uc5f0\uacb0\uce35\uc744 \uac00\uc9c4 MLP(Multi-layer Perceptron)\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>class SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(28 * 28, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = x.view(-1, 28 * 28)  # flatten\n        x = torch.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x\n<\/code><\/pre>\n<h3>3.3. \ubaa8\ub378 \ud6c8\ub828 \ud568\uc218<\/h3>\n<p>\ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud55c \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>def train_model(model, train_loader, criterion, optimizer, epochs=5):\n    model.train()\n    for epoch in range(epochs):\n        for data, target in train_loader:\n            optimizer.zero_grad()\n            output = model(data)\n            loss = criterion(output, target)\n            loss.backward()\n            optimizer.step()\n        print(f'Epoch {epoch+1}\/{epochs}, Loss: {loss.item():.4f}')\n<\/code><\/pre>\n<h3>3.4. \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud55c \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>def evaluate_model(model, test_loader):\n    model.eval()\n    correct = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            output = model(data)\n            pred = output.argmax(dim=1, keepdim=True)  # get index of max log-probability\n            correct += pred.eq(target.view_as(pred)).sum().item()\n    accuracy = 100. * correct \/ len(test_loader.dataset)\n    print(f'Accuracy: {accuracy:.2f}%')\n<\/code><\/pre>\n<h3>3.5. \uc559\uc0c1\ube14 \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud6c8\ub828<\/h3>\n<p>\uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uc5ec \uc559\uc0c1\ube14\uc744 \ub9cc\ub4ed\ub2c8\ub2e4:<\/p>\n<pre><code>models = [SimpleNN() for _ in range(5)]\nfor model in models:\n    optimizer = optim.Adam(model.parameters(), lr=0.001)\n    criterion = nn.CrossEntropyLoss()\n    train_model(model, train_loader, criterion, optimizer, epochs=5)\n<\/code><\/pre>\n<h3>3.6. \uc559\uc0c1\ube14 \uc608\uce21<\/h3>\n<p>\ubaa8\ub378\ub4e4\uc774 \uc608\uce21\ud55c \uacb0\uacfc\ub97c \ud3c9\uade0 \ub0b4\uac70\ub098 \ud22c\ud45c\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uac12\uc744 \ub3c4\ucd9c\ud569\ub2c8\ub2e4:<\/p>\n<pre><code>def ensemble_predict(models, data):\n    with torch.no_grad():\n        outputs = [model(data) for model in models]\n        avg_output = sum(outputs) \/ len(models)\n        return avg_output.argmax(dim=1)\n    \ncorrect = 0\nwith torch.no_grad():\n    for data, target in test_loader:\n        output = ensemble_predict(models, data)\n        correct += output.eq(target.view_as(output)).sum().item()\n        \nensemble_accuracy = 100. * correct \/ len(test_loader.dataset)\nprint(f'Ensemble Accuracy: {ensemble_accuracy:.2f}%')\n<\/code><\/pre>\n<h2>4. \uc559\uc0c1\ube14 \uc131\ub2a5 \ucd5c\uc801\ud654 \uc804\ub7b5<\/h2>\n<p>\uc6b0\ub9ac\ub294 \uc774\ub807\uac8c \uc559\uc0c1\ube14\uc744 \uad6c\ucd95\ud558\uc5ec \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc9c0\ub9cc, \ucd94\uac00\uc801\uc778 \ucd5c\uc801\ud654 \uc804\ub7b5\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ubaa8\ub378 \ub2e4\uc591\uc131 \uc99d\uac00<\/strong>: \uc11c\ub85c \ub2e4\ub978 \uad6c\uc870\uc758 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud568\uc73c\ub85c\uc368 \uc608\uce21\uc758 \ub2e4\uc591\uc131\uc744 \uc99d\uac00\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>hyperparameter tuning<\/strong>: \uac01 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucd5c\uc801\ud654\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c GridSearchCV, RandomSearchCV \uac19\uc740 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uba54\ud0c0 \ubaa8\ub378 \ud559\uc2b5<\/strong>: \uc5ec\ub7ec \uae30\ubcf8 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc785\ub825\uc73c\ub85c \ud558\uc5ec \uc0c8\ub85c\uc6b4 \ubaa8\ub378(\uba54\ud0c0 \ubaa8\ub378)\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub97c \uc774\uc6a9\ud558\uc5ec \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \ud1b5\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc559\uc0c1\ube14 \uae30\ubc95\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc774\uba70, \ub2e4\uc591\ud55c \ubc29\ubc95\uc73c\ub85c \uc870\ud569\uacfc \uc2e4\ud5d8\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc2b5\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0 \ud3c9\uac00\ud558\uc5ec \ucd5c\uc801\uc758 \uc559\uc0c1\ube14 \ubaa8\ub378\uc744 \ucc3e\ub294 \uacfc\uc815\uc5d0\uc11c \ub9ce\uc740 \uac83\uc744 \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc758 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc774\ud574\ud558\uace0 \uc801\uc6a9\ud558\ub294 \ub370 \uc788\uc5b4, \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc774 \ub354 \ub098\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uac00 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \uc885\ub958\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(ANN)\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525\ub7ec\ub2dd\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uac01\uc885 \uc608\uce21 \ubb38\uc81c\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 PyTorch\ub294 \uc5f0\uad6c \ubc0f \uac1c\ubc1c\uc5d0 \uc801\ud569\ud55c \uac15\ub825\ud55c \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ucd95\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\ub294 \uc720\uc5f0\uc131\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uc559\uc0c1\ube14 \uae30\ubc95\uc744 \uc774\uc6a9\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc559\uc0c1\ube14\uc740 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30076\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc559\uc0c1\ube14\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[33],"tags":[],"class_list":["post-30076","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc559\uc0c1\ube14\uc744 \uc774\uc6a9\ud55c \uc131\ub2a5 \ucd5c\uc801\ud654 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/30076\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" 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