{"id":29857,"date":"2024-10-28T03:00:35","date_gmt":"2024-10-28T03:00:35","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29857"},"modified":"2024-11-26T06:51:05","modified_gmt":"2024-11-26T06:51:05","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-%ec%9d%b8%ec%bd%94%eb%8d%94-%eb%94%94%ec%bd%94%eb%8d%94-%eb%aa%a8%eb%8d%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29857\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378"},"content":{"rendered":"

<\/p>\n

\uc624\ub298\uc740 Generative Adversarial Networks(GAN)\uc640 \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc758 \uac1c\ub150\uc744 \uae4a\uc774 \uc788\uac8c \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uac83\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub525\ub7ec\ub2dd \uae30\uc220\uc774\uba70, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc740 \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

1. GAN(Generative Adversarial Networks)<\/h2>\n

GAN\uc740 \uc774\uc548 \uad7f\ud3a0\ub85c(Ian Goodfellow)\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \uc0dd\uc131\uc801 \ubaa8\ub378\ub85c, \uc8fc\ub85c \uc0dd\uc131 \uad00\ub828 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\uc778 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\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 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0\ub97c \ud310\ubcc4\ud569\ub2c8\ub2e4.<\/p>\n

1.1 GAN\uc758 \uc791\ub3d9 \uc6d0\ub9ac<\/h3>\n

GAN\uc758 \uc791\ub3d9 \uc6d0\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc694\uc57d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

    \n
  1. \uc0dd\uc131\uc790\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
  2. \ud310\ubcc4\uc790\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub97c \ube44\uad50\ud558\uc5ec \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ub2e8\ud569\ub2c8\ub2e4.<\/li>\n
  3. \uc0dd\uc131\uc790\ub294 \ud310\ubcc4\uc790\ub97c \uc18d\uc774\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\uc120\ub429\ub2c8\ub2e4.<\/li>\n
  4. \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\uc790\uc758 \uac1c\uc120\uc5d0 \ub300\uc751\ud558\uc5ec \ud310\ubcc4 \ub2a5\ub825\uc744 \ud5a5\uc0c1\uc785\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    1.2 GAN\uc758 \uc218\ud559\uc801 \uc815\uc758<\/h3>\n

    GAN\uc758 \ubaa9\ud45c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac00\uc9c0 \uc2e0\uacbd\ub9dd\uc744 \ucd5c\uc801\ud654\ud558\ub294 \uac83\uc785\ub2c8\ub2e4:<\/p>\n

    min_G max_D V(D, G) = E[log(D(x))] + E[log(1 - D(G(z)))].<\/code><\/pre>\n
    \uc5ec\uae30\uc11c D(x)<\/code>\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ud310\ubcc4\uc790\uc758 \ucd9c\ub825, G(z)<\/code>\ub294 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e4\uc5b4\ub0b8 \uac00\uc9dc \ub370\uc774\ud130\uc785\ub2c8\ub2e4.<\/p>\n
    2. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\uc758 GAN \uad6c\ud604<\/h2>\n
    2.1 \ud658\uacbd \uc124\uc815<\/h3>\n
    !pip install torch torchvision<\/code><\/pre>\n
    2.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
    MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc0dd\uc131\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
    import torch\nfrom torchvision import datasets, transforms\n\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)<\/code><\/pre>\n
    2.3 GAN \ubaa8\ub378 \uc815\uc758<\/h3>\n
    import torch.nn as nn\n\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, 784),\n            nn.Tanh()  # MNIST\uc758 \ud53d\uc140 \uac12\uc740 -1~1 \ubc94\uc704\n        )\n\n    def forward(self, z):\n        return self.model(z)\n\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(784, 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()\n        )\n\n    def forward(self, img):\n        return self.model(img)<\/code><\/pre>\n
    2.4 \ud6c8\ub828 \ub8e8\ud504 \uad6c\ud604<\/h3>\n
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ngenerator = Generator().to(device)\ndiscriminator = Discriminator().to(device)\n\ncriterion = nn.BCELoss()\noptimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizer_D = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n\nfor epoch in range(50):\n    for i, (imgs, _) in enumerate(train_loader):\n        imgs = imgs.view(imgs.size(0), -1).to(device)\n        z = torch.randn(imgs.size(0), 100).to(device)\n\n        real_labels = torch.ones(imgs.size(0), 1).to(device)\n        fake_labels = torch.zeros(imgs.size(0), 1).to(device)\n\n        # \ud310\ubcc4\uc790 \ud6c8\ub828\n        optimizer_D.zero_grad()\n        outputs = discriminator(imgs)\n        d_loss_real = criterion(outputs, real_labels)\n        d_loss_real.backward()\n\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\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    print(f'Epoch [{epoch+1}\/50], d_loss: {d_loss_real.item() + d_loss_fake.item():.4f}, g_loss: {g_loss.item():.4f}')<\/code><\/pre>\n
    3. \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378<\/h2>\n
    \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc740 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc555\ucd95\ud558\uace0, \uc555\ucd95\ub41c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc2dc \uc0dd\uc131\ud558\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd \uad6c\uc870\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ub4f1\uc758 \uc791\uc5c5\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
    3.1 \uc778\ucf54\ub354-\ub514\ucf54\ub354 \uad6c\uc870<\/h3>\n
    \uc778\ucf54\ub354\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc7a0\uc7ac \uacf5\uac04(latent space)\uc73c\ub85c \ubcc0\ud658\ud558\uace0, \ub514\ucf54\ub354\ub294 \uc7a0\uc7ac \uacf5\uac04\uc5d0\uc11c \ub2e4\uc2dc \uc6d0\ub798\uc758 \ub370\uc774\ud130\ub85c \ubcf5\uc6d0\ud569\ub2c8\ub2e4. \uc774 \uad6c\uc870\ub294 \uae30\uacc4 \ubc88\uc5ed, \uc774\ubbf8\uc9c0 \ucea1\uc154\ub2dd \ub4f1\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
    3.2 \ubaa8\ub378 \uad6c\ud604<\/h3>\n
    class Encoder(nn.Module):\n        def __init__(self):\n            super(Encoder, self).__init__()\n            self.model = nn.Sequential(\n                nn.Linear(784, 256),\n                nn.ReLU(),\n                nn.Linear(256, 64)\n            )\n\n        def forward(self, x):\n            return self.model(x)\n\n    class Decoder(nn.Module):\n        def __init__(self):\n            super(Decoder, self).__init__()\n            self.model = nn.Sequential(\n                nn.Linear(64, 256),\n                nn.ReLU(),\n                nn.Linear(256, 784),\n                nn.Sigmoid()\n            )\n\n        def forward(self, z):\n            return self.model(z)<\/code><\/pre>\n
    3.3 \ud6c8\ub828 \ub8e8\ud504<\/h3>\n
    encoder = Encoder().to(device)\ndecoder = Decoder().to(device)\n\noptimizer = torch.optim.Adam(list(encoder.parameters()) + list(decoder.parameters()), lr=0.001)\ncriterion = nn.BCELoss()\n\nfor epoch in range(50):\n    for imgs, _ in train_loader:\n        imgs = imgs.view(imgs.size(0), -1).to(device)\n        z = encoder(imgs)\n\n        optimizer.zero_grad()\n        reconstructed = decoder(z)\n        loss = criterion(reconstructed, imgs)\n        loss.backward()\n        optimizer.step()\n\n    print(f'Epoch [{epoch+1}\/50], Loss: {loss.item():.4f}') <\/code><\/pre>\n
    \uacb0\ub860<\/h2>\n
    \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 GAN \ubc0f \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc5d0 \ub300\ud55c \uc0c1\uc138\ud55c \uc124\uba85\uacfc \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc758 \uad6c\uc870\uc640 \uc791\ub3d9 \uc6d0\ub9ac\ub97c \uc774\ud574\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uc774\ubbf8\uc9c0 \uc0dd\uc131 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc785\ub825 \ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\ub3c4 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \ub525\ub7ec\ub2dd\uc758 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c \ubc1c\uc804 \uac00\ub2a5\uc131\uc774 \ud070 \uae30\uc220\ub4e4\uc785\ub2c8\ub2e4.<\/p>\n
    \uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \ub3c5\uc790 \uc5ec\ub7ec\ubd84\uc774 \ub525\ub7ec\ub2dd\uc758 \uc2ec\ud654 \uacfc\uc815\uc5d0 \ub300\ud574 \ub354 \uae4a\uc774 \uc774\ud574\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
    <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
    \uc624\ub298\uc740 Generative Adversarial Networks(GAN)\uc640 \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc758 \uac1c\ub150\uc744 \uae4a\uc774 \uc788\uac8c \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uac83\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub525\ub7ec\ub2dd \uae30\uc220\uc774\uba70, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc740 \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 1. GAN(Generative Adversarial Networks) GAN\uc740 \uc774\uc548 \uad7f\ud3a0\ub85c(Ian Goodfellow)\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \uc0dd\uc131\uc801 \ubaa8\ub378\ub85c, \uc8fc\ub85c \uc0dd\uc131 \uad00\ub828 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. GAN\uc740 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378”<\/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":[32],"tags":[],"class_list":["post-29857","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"\n\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378 - \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\/29857\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc624\ub298\uc740 Generative Adversarial Networks(GAN)\uc640 \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc758 \uac1c\ub150\uc744 \uae4a\uc774 \uc788\uac8c \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ub450 \uac00\uc9c0 \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uac83\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub525\ub7ec\ub2dd \uae30\uc220\uc774\uba70, \uc778\ucf54\ub354-\ub514\ucf54\ub354 \ubaa8\ub378\uc740 \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 1. GAN(Generative Adversarial Networks) GAN\uc740 \uc774\uc548 \uad7f\ud3a0\ub85c(Ian Goodfellow)\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \uc0dd\uc131\uc801 \ubaa8\ub378\ub85c, \uc8fc\ub85c \uc0dd\uc131 \uad00\ub828 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 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