{"id":29832,"date":"2024-10-28T03:00:25","date_gmt":"2024-10-28T03:00:25","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29832"},"modified":"2024-11-26T06:51:12","modified_gmt":"2024-11-26T06:51:12","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-%eb%aa%a8%eb%8d%b8-%ec%84%b1%eb%8a%a5-%ed%96%a5%ec%83%81","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29832\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1"},"content":{"rendered":"<p>Generative Adversarial Networks (GANs)\ub294 2014\ub144 Ian Goodfellow\uc640 \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \ud601\uc2e0\uc801\uc778 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uace0, \ud310\ubcc4\uae30\ub294 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uc0dd\uc131\ub41c \uac83\uc778\uc9c0\ub97c \uad6c\ubcc4\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ubaa8\ub378\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70, \uacb0\uacfc\uc801\uc73c\ub85c \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n<h2>1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n<p>GAN\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \ub450 \uc2e0\uacbd\ub9dd\uc758 \uc801\ub300\uc801 \ud559\uc2b5(adversarial training)\uc785\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ubc18\uba74\uc5d0 \ud310\ubcc4\uae30\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub97c \ubc1b\uc544 \uc774\ub97c \uad6c\ubcc4\ud558\ub294 \ubc29\ubc95\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>\u751f\u6210\u5668(Generator)<\/strong>: \ubb34\uc791\uc704 \ub178\uc774\uc988\ub97c \uc785\ub825\ubc1b\uc544 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\u5224\u5225\u5668(Discriminator)<\/strong>: \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h2>\n<p>\uc6b0\uc120, PyTorch\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. PyTorch\ub294 pip\uc774\ub098 conda\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec PyTorch\ub97c \uc124\uce58\ud558\uc138\uc694.<\/p>\n<pre><code>pip install torch torchvision<\/code><\/pre>\n<h2>3. GAN \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc544\ub798\ub294 \uae30\ubcf8\uc801\uc778 GAN\uc758 \uad6c\uc870\ub97c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4. MNIST \ub370\uc774\ud130\uc138\ud2b8\ub97c \ud65c\uc6a9\ud558\uc5ec \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 GAN\uc744 \ub9cc\ub4e4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130\uc14b \ub85c\ub529<\/h3>\n<pre><code>import torch\nimport torchvision.transforms as transforms\nfrom torchvision import datasets\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)\n<\/code><\/pre>\n<h3>3.2 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30 \ubaa8\ub378 \uc815\uc758<\/h3>\n<pre><code>import torch.nn as nn\n\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.fc = 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()\n        )\n\n    def forward(self, z):\n        return self.fc(z).reshape(-1, 1, 28, 28)\n\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.fc = nn.Sequential(\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()\n        )\n\n    def forward(self, x):\n        return self.fc(x.view(-1, 28 * 28))\n<\/code><\/pre>\n<h3>3.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n<pre><code>import torch.optim as optim\n\ndevice = 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 = 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\nnum_epochs = 50\nfor epoch in range(num_epochs):\n    for i, (images, _) in enumerate(train_loader):\n        images = images.to(device)\n        batch_size = images.size(0)\n\n        # \uc9c4\uc9dc\uc640 \uac00\uc9dc \ub808\uc774\ube14 \uc0dd\uc131\n        real_labels = torch.ones(batch_size, 1).to(device)\n        fake_labels = torch.zeros(batch_size, 1).to(device)\n\n        # \ud310\ubcc4\uae30 \ud559\uc2b5\n        optimizer_d.zero_grad()\n        outputs = discriminator(images)\n        d_loss_real = criterion(outputs, real_labels)\n        d_loss_real.backward()\n\n        z = torch.randn(batch_size, 100).to(device)\n        fake_images = generator(z)\n        outputs = discriminator(fake_images.detach())\n        d_loss_fake = criterion(outputs, fake_labels)\n        d_loss_fake.backward()\n\n        optimizer_d.step()\n\n        # \uc0dd\uc131\uae30 \ud559\uc2b5\n        optimizer_g.zero_grad()\n        outputs = discriminator(fake_images)\n        g_loss = criterion(outputs, real_labels)\n        g_loss.backward()\n\n        optimizer_g.step()\n\n    print(f'Epoch [{epoch+1}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n<\/code><\/pre>\n<h2>4. \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1<\/h2>\n<p>GAN \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \ub370\uc774\ud130 \uc99d\uac00, \ubaa8\ub378 \ubcc0\ud654, \uc815\uaddc\ud654 \uae30\ubc95 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ub370\uc774\ud130 \uc99d\uac00<\/h3>\n<p>\ub370\uc774\ud130\ub97c \ub298\ub9ac\uae30 \uc704\ud574 \ud68c\uc804, \uc774\ub3d9, \ud06c\uae30 \ubcc0\ud658 \ub4f1\uc758 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. PyTorch\uc758 <code>torchvision.transforms<\/code> \ubaa8\ub4c8\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc27d\uac8c \ubcc0\ud615\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>transform = transforms.Compose([\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomVerticalFlip(),\n    transforms.RandomRotation(10),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n<\/code><\/pre>\n<h3>4.2 \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98 \uac1c\uc120<\/h3>\n<p>\uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\uc758 \uc544\ud0a4\ud14d\ucc98\ub97c \uac1c\uc120\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub354 \uae4a\uc740 \ub124\ud2b8\uc6cc\ud06c\ub098 Convolutional Neural Networks (CNN)\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>4.3 \ud559\uc2b5\ub960 \uc870\uc815<\/h3>\n<p>\ud559\uc2b5\ub960\uc740 \ubaa8\ub378 \ud559\uc2b5\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \ud559\uc2b5\ub960 \uc2a4\ucf00\uc904\ub7ec\ub97c \ud1b5\ud574 \ud559\uc2b5\ub960\uc744 \ub3d9\uc801\uc73c\ub85c \uc870\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>scheduler_g = optim.lr_scheduler.StepLR(optimizer_g, step_size=30, gamma=0.1)\nscheduler_d = optim.lr_scheduler.StepLR(optimizer_d, step_size=30, gamma=0.1)\n<\/code><\/pre>\n<h3>4.4 \ub2e4\uc591\ud55c \uc190\uc2e4 \ud568\uc218 \uc0ac\uc6a9\ud558\uae30<\/h3>\n<p>\uae30\ubcf8 BCELoss \ub300\uc2e0 Wasserstein Loss \ub610\ub294 Least Squares Loss \ub4f1\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 GAN\uc758 \uc548\uc815\uc131\uc744 \ub192\uc774\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>5. \uacb0\ub860<\/h2>\n<p>GAN\uc740 \uac15\ub825\ud55c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c GAN \uad6c\ud604\uc740 \ube44\uad50\uc801 \uac04\ub2e8\ud558\uba70, \uc5ec\ub7ec \uac00\uc9c0 \ubc29\ubc95\uc73c\ub85c \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 GAN \uc5f0\uad6c \ubc0f \uae30\ub2a5 \uac1c\uc120\uc5d0 \ub300\ud55c \uad00\uc2ec\uc774 \ub354\uc6b1 \ub192\uc544\uc9c8 \uac83\uc73c\ub85c \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n<h2>6. \ucc38\uace0\ubb38\ud5cc<\/h2>\n<ul>\n<li>Ian Goodfellow et al. (2014). Generative Adversarial Networks.<\/li>\n<li>Pytorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/li>\n<li>Deep Learning for Computer Vision with Python by Adrian Rosebrock.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Generative Adversarial Networks (GANs)\ub294 2014\ub144 Ian Goodfellow\uc640 \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \ud601\uc2e0\uc801\uc778 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uace0, \ud310\ubcc4\uae30\ub294 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uc0dd\uc131\ub41c \uac83\uc778\uc9c0\ub97c \uad6c\ubcc4\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ubaa8\ub378\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70, \uacb0\uacfc\uc801\uc73c\ub85c \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150 GAN\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \ub450 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29832\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1&#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":[32],"tags":[],"class_list":["post-29832","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 - \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\/29832\/\" \/>\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, \ubaa8\ub378 \uc131\ub2a5 \ud5a5\uc0c1 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"Generative Adversarial Networks (GANs)\ub294 2014\ub144 Ian Goodfellow\uc640 \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \ud601\uc2e0\uc801\uc778 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uace0, \ud310\ubcc4\uae30\ub294 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uc0dd\uc131\ub41c \uac83\uc778\uc9c0\ub97c \uad6c\ubcc4\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ubaa8\ub378\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70, \uacb0\uacfc\uc801\uc73c\ub85c \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4. 1. 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