{"id":29792,"date":"2024-10-28T03:00:06","date_gmt":"2024-10-28T03:00:06","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29792"},"modified":"2024-11-26T06:51:21","modified_gmt":"2024-11-26T06:51:21","slug":"%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98%eb%a5%bc-%ed%99%9c%ec%9a%a9%ed%95%9c-gan-%eb%94%a5%eb%9f%ac%eb%8b%9d-gan-%ec%86%8c%ea%b0%9c","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29792\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, GAN \uc18c\uac1c"},"content":{"rendered":"

<\/p>\n

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1. GAN(Generative Adversarial Network) \uc18c\uac1c<\/h2>\n

\n GAN(Generative Adversarial Network)\uc740 2014\ub144\uc5d0 Ian Goodfellow\uac00 \ucc98\uc74c \uc81c\uc548\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\ub85c,
\n \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc0dd\uc131\uc790\ub294 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc774 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ub2e8\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.
\n \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.\n <\/p>\n

\n GAN\uc758 \ud575\uc2ec \uc544\uc774\ub514\uc5b4\ub294 “\uc801\ub300\uc801 \ud559\uc2b5”(Adversarial Training)\uc785\ub2c8\ub2e4.
\n \uc0dd\uc131\uc790\ub294 \ud310\ubcc4\uc790\uac00 \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc798 \uad6c\ubd84\ud558\uc9c0 \ubabb\ud558\uac8c \ud558\uae30 \uc704\ud574 \uacc4\uc18d\ud574\uc11c \ub354 \uadf8\ub7f4\ub4ef\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4.
\n \ubc18\uba74, \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e0 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \uc815\ud655\ud558\uac8c \ud310\ub2e8\ud558\uae30 \uc704\ud574 \ub354\uc6b1 \uc815\uad50\ud558\uac8c \ud559\uc2b5\ud569\ub2c8\ub2e4.
\n \uc774\ub7ec\ud55c \uacbd\uc7c1 \uad6c\uc870\ub294 GAN\uc758 \ub3c5\ud2b9\ud55c \ud2b9\uc9d5\uc774\uba70, \ucc3d\uc758\uc801\uc778 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ube44\ub514\uc624 \uc0dd\uc131, \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2. GAN\uc758 \uad6c\uc870\uc640 \ud559\uc2b5 \uacfc\uc815<\/h2>\n

\n GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4:\n <\/p>\n

    \n
  1. \ub370\uc774\ud130 \uc218\uc9d1:<\/strong> GAN\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud544\uc694\ub85c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc2e4\uc81c \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uc0d8\ud50c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n
  2. \uc0dd\uc131\uc790(Generator) \ud6c8\ub828:<\/strong> \uc0dd\uc131\uc790\ub294 \ub178\uc774\uc988(z)\ub97c \uc785\ub825\ubc1b\uc544 \uac00\uc9dc \uc774\ubbf8\uc9c0(\ub610\ub294 \ub370\uc774\ud130)\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ud310\ubcc4\uc790(Discriminator) \ud6c8\ub828:<\/strong> \ud310\ubcc4\uc790\ub294 \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e0 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\ubc1b\uc544 \uc774\ub4e4\uc774 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n
  4. \uc190\uc2e4 \ud568\uc218 \uacc4\uc0b0:<\/strong> \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.
    \n \uc0dd\uc131\uc790\uc758 \ubaa9\ud45c\ub294 \ud310\ubcc4\uc790\ub97c \uc18d\uc774\ub294 \uac83\uc774\uace0, \ud310\ubcc4\uc790\uc758 \ubaa9\ud45c\ub294 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e0 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc815\ud655\ud558\uac8c \ud310\ub2e8\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/li>\n
  5. \ubaa8\ub378 \uc5c5\ub370\uc774\ud2b8:<\/strong> \uc190\uc2e4 \ud568\uc218\uc5d0 \uae30\ubc18\ud558\uc5ec \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ubaa8\ub450 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \ubaa8\ub378 \ud30c\ub77c\ubbf8\ud130\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/li>\n
  6. \ubc18\ubcf5:<\/strong> 2~5 \ub2e8\uacc4\ub97c \ubc18\ubcf5\ud558\uc5ec \ub450 \ub124\ud2b8\uc6cc\ud06c\uac00 \uc0c1\ud638 \uac1c\uc120\ub420 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    \n \uc774\ub7f0 \ubc29\uc2dd\uc73c\ub85c \uc0dd\uc131\uc790\ub294 \uc810\uc810 \ub354 \ub098\uc740 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc774\ub97c \uc798 \uad6c\ubd84\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.
    \n \uc774 \uacfc\uc815\uc774 \ubc18\ubcf5\ub418\uba74\uc11c \uacb0\uad6d \uc0dd\uc131\uc790\ub294 \ub9e4\uc6b0 \ud604\uc2e4\uc801\uc778 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uc218\uc900\uc5d0 \ub3c4\ub2ec\ud558\uac8c \ub429\ub2c8\ub2e4.\n <\/p>\n

    3. GAN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95<\/h2>\n

    \n \uc774\uc81c GAN\uc744 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
    \n \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c GAN\uc744 \ub9cc\ub4e4\uc5b4\uc11c \uc190\uae00\uc528 \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc778 MNIST\ub97c \uc0ac\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
    \n MNIST\ub294 0\uc5d0\uc11c 9\uae4c\uc9c0\uc758 \uc22b\uc790\uac00 \ud3ec\ud568\ub41c 70,000\uac1c\uc758 \ud751\ubc31 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.
    \n \uc6b0\ub9ac\uac00 \uc0dd\uc131\ud558\ub824\ub294 \ubaa9\ud45c\ub294 \uc774 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

    3.1. \ud544\uc218 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n

    \n \uba3c\uc800 \ud30c\uc774\ud1a0\uce58\uc640 \uae30\ud0c0 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.
    \n \uc544\ub798 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

    !pip install torch torchvision matplotlib<\/code><\/pre>\n
    3.2. \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
    \n                \uc774\uc81c MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uace0, Tensor \ud615\ud0dc\ub85c \ubcc0\ud658\ud55c \ud6c4, \ud6c8\ub828\uc744 \uc704\ud574 \uc900\ube44\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n            <\/p>\n
    \nimport torch\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc124\uc815\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ub85c\ub4dc\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n<\/code><\/pre>\n
    3.3. GAN\uc758 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \uc815\uc758\ud558\uae30<\/h3>\n
    \n                GAN\uc758 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.
    \n                \uc0dd\uc131\uc790\ub294 \ub79c\ub364 \ub178\uc774\uc988\ub97c \uc785\ub825\ubc1b\uc544 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc8fc\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ub2e8\ud569\ub2c8\ub2e4.\n            <\/p>\n
    \nimport torch.nn as nn\n\n# \uc0dd\uc131\uc790 \uc815\uc758\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(100, 256),\n            nn.ReLU(),\n            nn.Linear(256, 512),\n            nn.ReLU(),\n            nn.Linear(512, 1024),\n            nn.ReLU(),\n            nn.Linear(1024, 28 * 28),\n            nn.Tanh() # -1 ~ 1\ub85c \ucd9c\ub825\uc744 \uc815\uaddc\ud654\n        )\n\n    def forward(self, z):\n        return self.model(z).view(-1, 1, 28, 28)\n\n# \ud310\ubcc4\uc790 \uc815\uc758\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Flatten(),\n            nn.Linear(28 * 28, 1024),\n            nn.LeakyReLU(0.2),\n            nn.Linear(1024, 512),\n            nn.LeakyReLU(0.2),\n            nn.Linear(512, 256),\n            nn.LeakyReLU(0.2),\n            nn.Linear(256, 1),\n            nn.Sigmoid() # 0 ~ 1\ub85c\uc758 \ucd9c\ub825\uc744 \uc815\uaddc\ud654\n        )\n\n    def forward(self, img):\n        return self.model(img)\n<\/code><\/pre>\n
    3.4. \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815<\/h3>\n
    \n                GAN\uc758 \uc190\uc2e4 \ud568\uc218\ub294 \ub450 \uac1c\uc758 \uc190\uc2e4\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.
    \n                \uc0dd\uc131\uc790\uc758 \uc190\uc2e4\uacfc \ud310\ubcc4\uc790\uc758 \uc190\uc2e4\uc744 \uc124\uc815\ud558\uace0, \ub450 \uc2e0\uacbd\ub9dd\uc758 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n            <\/p>\n
    \nimport torch.optim as optim\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815\ncriterion = nn.BCELoss()\noptimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n<\/code><\/pre>\n
    3.5. GAN \ud6c8\ub828\ud558\uae30<\/h3>\n
    \n                \uc774\uc81c \uc2e4\uc81c\ub85c GAN\uc744 \ud6c8\ub828\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
    \n                \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c\ub294 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uac00 \uad50\ub300\ub85c \ud6c8\ub828\ub429\ub2c8\ub2e4.\n            <\/p>\n
    \nimport matplotlib.pyplot as plt\n\ndef train_gan(num_epochs):\n    for epoch in range(num_epochs):\n        for i, (imgs, _) in enumerate(train_loader):\n            # \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub77c\ubca8\n            real_imgs = imgs\n            real_labels = torch.ones(real_imgs.size(0), 1)\n            fake_labels = torch.zeros(real_imgs.size(0), 1)\n\n            # \ud310\ubcc4\uc790 \ud6c8\ub828\n            optimizer_D.zero_grad()\n            outputs = discriminator(real_imgs)\n            d_loss_real = criterion(outputs, real_labels)\n            d_loss_real.backward()\n\n            z = torch.randn(real_imgs.size(0), 100)\n            fake_imgs = generator(z)\n            outputs = discriminator(fake_imgs.detach())\n            d_loss_fake = criterion(outputs, fake_labels)\n            d_loss_fake.backward()\n            optimizer_D.step()\n\n            # \uc0dd\uc131\uc790 \ud6c8\ub828\n            optimizer_G.zero_grad()\n            outputs = discriminator(fake_imgs)\n            g_loss = criterion(outputs, real_labels)\n            g_loss.backward()\n            optimizer_G.step()\n\n        if epoch % 100 == 0:\n            print(f'Epoch [{epoch}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item():.4f}, g_loss: {g_loss.item():.4f}')\n\n            # \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \ucd9c\ub825\n            with torch.no_grad():\n                generated_images = generator(torch.randn(64, 100)).detach().cpu()\n                plt.figure(figsize=(10, 10))\n                plt.imshow(torchvision.utils.make_grid(generated_images, nrow=8, normalize=True).permute(1, 2, 0))\n                plt.axis('off')\n                plt.show()\n\ntrain_gan(num_epochs=1000)\n<\/code><\/pre>\n
    4. \uacb0\ub860<\/h2>\n
    \n                GAN\uc740 \ub9e4\uc6b0 \uac15\ub825\ud55c \uc0dd\uc131 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.
    \n                \uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud558\uc5ec GAN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
    \n                \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c GAN\uc740 \uace0\ud488\uc9c8\uc758 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.
    \n                \uc2e4\uc81c \uc751\uc6a9\uc744 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uac00\uc9c0 \uae30\ubc95(\uc608: \uc870\uac74\ubd80 GAN, \uc2a4\ud0c0\uc77c GAN \ub4f1)\uc744 \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n
    \n                \uc55e\uc73c\ub85c \ub354 \ubc1c\uc804\ub41c GAN \uc544\ud0a4\ud14d\ucc98\uc640 \uadf8 \ud65c\uc6a9\uc5d0 \ub300\ud574\uc11c\ub3c4 \uc774\uc57c\uae30\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
    \n                GAN\uc740 \ud604\uc7ac\ub3c4 \ud65c\ubc1c\ud788 \uc5f0\uad6c\ub418\uace0 \uc788\uc73c\uba70, \uc0c8\ub85c\uc6b4 \ubc29\uc2dd\uc758 GAN\uc774 \uacc4\uc18d \ubc1c\ud45c\ub418\uace0 \uc788\uc73c\ub2c8 \uc774\uc5d0 \ub300\ud55c \uc5c5\ub370\uc774\ud2b8\ub3c4 \uc8fc\ubaa9\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n<\/article>\n
    <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
    1. GAN(Generative Adversarial Network) \uc18c\uac1c GAN(Generative Adversarial Network)\uc740 2014\ub144\uc5d0 Ian Goodfellow\uac00 \ucc98\uc74c \uc81c\uc548\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc774 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ub2e8\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4. 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