{"id":29851,"date":"2024-10-28T03:00:33","date_gmt":"2024-10-28T03:00:33","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29851"},"modified":"2024-11-26T06:51:07","modified_gmt":"2024-11-26T06:51:07","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%95%a0%eb%8b%88%eb%a9%80%ea%b0%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29851\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc560\ub2c8\uba40\uac04"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n

\n Generative Adversarial Networks (GANs)\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uc11c\ub85c \ub300\ub9bd\ud558\uba74\uc11c \ud559\uc2b5\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ubcc0\ud658 \ubc0f \uc2a4\ud0c0\uc77c \uc804\uc774\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc9c4\ubcf4\ub41c \ub525\ub7ec\ub2dd \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \ud65c\uc6a9\ud55c GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\uc640 \uc774\ub97c \ud1b5\ud574 \ub3d9\ubb3c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uc560\ub2c8\uba40\uac04(AnimalGAN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2. GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h2>\n

\n GAN\uc740 \uc8fc\ub85c \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825 \ubc1b\uc544 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \uc0dd\uc131\ub41c \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uad6c\ubd84\ud569\ub2c8\ub2e4. \ub450 \uc2e0\uacbd\ub9dd\uc740 \uc11c\ub85c\uc758 \ud559\uc2b5\uc744 \ubc29\ud574\ud558\uba74\uc11c \ucd5c\uc801\ud654\ub429\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uac8c\uc784 \uc774\ub860\uc5d0\uc11c\uc758 ‘\uc81c\ub85c\uc12c \uac8c\uc784’\uacfc \ube44\uc2b7\ud569\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ud310\ubcc4\uc790\uac00 \ubcf4\uc9c0 \ubabb\ud558\uac8c \ub9cc\ub4e4\uae30 \uc704\ud574 \uacc4\uc18d\ud574\uc11c \uac1c\uc120\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e0 \uc774\ubbf8\uc9c0\uc758 \uc9c4\uc704\ub97c \ud310\ub2e8\ud558\ub294 \ub370 \ud5a5\uc0c1\ub429\ub2c8\ub2e4.\n <\/p>\n

2.1 GAN\uc758 \ud559\uc2b5 \uacfc\uc815<\/h3>\n

\n \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n

    \n
  1. \uc9c4\uc9dc \ub370\uc774\ud130\ub85c \ud310\ubcc4\uc790\ub97c \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n
  2. \ubb34\uc791\uc704 \ub178\uc774\uc988\ub97c \uc0dd\uc131\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc0dd\uc131\uc790\ub97c \ud1b5\ud574 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/li>\n
  3. \uac00\uc9dc \uc774\ubbf8\uc9c0\ub85c \ub610\ub2e4\uc2dc \ud310\ubcc4\uc790\ub97c \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n
  4. \uc704 \uacfc\uc815\uc744 \ubc18\ubcf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    3. PyTorch\ub97c \uc774\uc6a9\ud55c GAN \uad6c\ud604<\/h2>\n

    \n \uc774\uc81c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c GAN\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc804\uccb4 \ud504\ub85c\uc138\uc2a4\ub294 \uc900\ube44 \ub2e8\uacc4, \ubaa8\ub378 \uad6c\ud604, \ud559\uc2b5, \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \uc2dc\uac01\ud654\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

    3.1 \ud658\uacbd \uc124\uc815<\/h3>\n
    python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nfrom torchvision import datasets\nfrom torch.utils.data import DataLoader\nimport matplotlib.pyplot as plt\nimport numpy as np\n    <\/code><\/pre>\n
    3.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
    \n        \uc560\ub2c8\uba40\uac04 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub294 CIFAR-10 \ub610\ub294 \ub3d9\ubb3c \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 CIFAR-10 \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
    python\ntransform = transforms.Compose([\n    transforms.Resize(64),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n])\n\n# CIFAR-10 \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ndataset = datasets.CIFAR10(root='.\/data', train=True, download=True, transform=transform)\ndataloader = DataLoader(dataset, batch_size=128, shuffle=True)\n    <\/code><\/pre>\n
    3.3 GAN \ubaa8\ub378 \uad6c\ud604<\/h3>\n
    \n        GAN \ubaa8\ub378\uc740 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825 \ubc1b\uace0 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc774\ubbf8\uc9c0\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ubcc4\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.\n    <\/p>\n
    python\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(True),\n            nn.Linear(256, 512),\n            nn.ReLU(True),\n            nn.Linear(512, 1024),\n            nn.ReLU(True),\n            nn.Linear(1024, 3 * 64 * 64),  # CIFAR-10 \uc774\ubbf8\uc9c0 \ud06c\uae30\n            nn.Tanh()  # \ucd9c\ub825 \ubc94\uc704\ub97c [-1, 1]\ub85c\n        )\n\n    def forward(self, z):\n        return self.model(z).view(-1, 3, 64, 64)\n\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(3 * 64 * 64, 512),\n            nn.LeakyReLU(0.2),\n            nn.Linear(512, 256),\n            nn.LeakyReLU(0.2),\n            nn.Linear(256, 1),\n            nn.Sigmoid()  # \ucd9c\ub825\uc774 [0, 1] \uc0ac\uc774\uc5d0 \uc788\ub3c4\ub85d\n        )\n\n    def forward(self, img):\n        return self.model(img.view(-1, 3 * 64 * 64))\n    <\/code><\/pre>\n
    3.4 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
    \n        GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ud310\ubcc4\uc790\uc640 \uc0dd\uc131\uc790\ub97c \ubc88\uac08\uc544 \uac00\uba70 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 GAN\uc744 \ud559\uc2b5\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
    python\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758\ngenerator = Generator().cuda()\ndiscriminator = Discriminator().cuda()\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\n# \ud559\uc2b5 \ub8e8\ud504\nnum_epochs = 50\nfor epoch in range(num_epochs):\n    for i, (imgs, _) in enumerate(dataloader):\n        # \uc9c4\uc9dc \uc774\ubbf8\uc9c0 \ub808\uc774\ube14 \ubc0f \uac00\uc9dc \uc774\ubbf8\uc9c0 \ub808\uc774\ube14 \uc124\uc815\n        real_imgs = imgs.cuda()\n        batch_size = real_imgs.size(0)\n        labels_real = torch.ones(batch_size, 1).cuda()\n        labels_fake = torch.zeros(batch_size, 1).cuda()\n\n        # \ud310\ubcc4\uc790 \ud559\uc2b5\n        optimizer_D.zero_grad()\n        outputs_real = discriminator(real_imgs)\n        loss_real = criterion(outputs_real, labels_real)\n\n        z = torch.randn(batch_size, 100).cuda()  # \ub178\uc774\uc988 \uc0dd\uc131\n        fake_imgs = generator(z)\n        outputs_fake = discriminator(fake_imgs.detach())\n        loss_fake = criterion(outputs_fake, labels_fake)\n\n        loss_D = loss_real + loss_fake\n        loss_D.backward()\n        optimizer_D.step()\n\n        # \uc0dd\uc131\uc790 \ud559\uc2b5\n        optimizer_G.zero_grad()\n        outputs_fake = discriminator(fake_imgs)\n        loss_G = criterion(outputs_fake, labels_real)  # \uac00\uc9dc \uc774\ubbf8\uc9c0\uac00 \uc9c4\uc9dc\ub85c \ud310\ub2e8\ub418\ub3c4\ub85d \ud559\uc2b5\n        loss_G.backward()\n        optimizer_G.step()\n\n    print(f'Epoch [{epoch}\/{num_epochs}], Loss D: {loss_D.item():.4f}, Loss G: {loss_G.item():.4f}')\n    <\/code><\/pre>\n
    3.5 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
    \n        \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\uc5ec GAN\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac1c\uc758 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n    <\/p>\n
    python\ndef show_generated_images(model, num_images=25):\n    z = torch.randn(num_images, 100).cuda()\n    with torch.no_grad():\n        generated_imgs = model(z)\n    generated_imgs = generated_imgs.cpu().numpy()\n    generated_imgs = (generated_imgs + 1) \/ 2  # [0, 1] \ubc94\uc704\ub85c \ubcc0\ud658\n\n    fig, axes = plt.subplots(5, 5, figsize=(10, 10))\n    for i, ax in enumerate(axes.flatten()):\n        ax.imshow(generated_imgs[i].transpose(1, 2, 0))  # \ucc44\ub110 \uc21c\uc11c\ub97c \uc774\ubbf8\uc9c0\uc5d0 \ub9de\uac8c \ubcc0\uacbd\n        ax.axis('off')\n    plt.tight_layout()\n    plt.show()\n\nshow_generated_images(generator)\n    <\/code><\/pre>\n
    4. \uacb0\ub860<\/h2>\n
    \n        \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uc744 \ud1b5\ud574 \ub3d9\ubb3c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uc560\ub2c8\uba40\uac04(AnimalGAN)\uc744 \uad6c\ud604\ud558\uc600\uc2b5\ub2c8\ub2e4. GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\ub97c \uc774\ud574\ud558\uace0 \uc2e4\uc81c\ub85c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uacb0\uacfc\ub97c \ud655\uc778\ud568\uc73c\ub85c\uc368, GAN\uc758 \uac1c\ub150\uacfc \uc791\ub3d9 \ubc29\uc2dd\uc744 \uba85\ud655\ud788 \uc774\ud574\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc5ec\uc804\ud788 \uc5f0\uad6c\uac00 \ud65c\ubc1c\ud788 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\ub294 \ubd84\uc57c\uc774\uba70, \ubcf4\ub2e4 \uc9c4\ubcf4\ub41c \ubaa8\ub378\uacfc \uae30\uc220\ub4e4\uc774 \uc9c0\uc18d\uc801\uc73c\ub85c \ub4f1\uc7a5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc2dc\ub3c4\ub97c \ud1b5\ud574 \uc6b0\ub9ac\ub294 \ub354 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \ud0d0\uc0c9\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n    <\/p>\n
    <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
    1. \uc11c\ub860 Generative Adversarial Networks (GANs)\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uc11c\ub85c \ub300\ub9bd\ud558\uba74\uc11c \ud559\uc2b5\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ubcc0\ud658 \ubc0f \uc2a4\ud0c0\uc77c \uc804\uc774\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc9c4\ubcf4\ub41c \ub525\ub7ec\ub2dd \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \ud65c\uc6a9\ud55c GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\uc640 \uc774\ub97c \ud1b5\ud574 \ub3d9\ubb3c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uc560\ub2c8\uba40\uac04(AnimalGAN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 2. GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac GAN\uc740 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc560\ub2c8\uba40\uac04”<\/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-29851","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, \uc560\ub2c8\uba40\uac04 - \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\/29851\/\" \/>\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, \uc560\ub2c8\uba40\uac04 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"1. \uc11c\ub860 Generative Adversarial Networks (GANs)\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uc11c\ub85c \ub300\ub9bd\ud558\uba74\uc11c \ud559\uc2b5\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ubcc0\ud658 \ubc0f \uc2a4\ud0c0\uc77c \uc804\uc774\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc9c4\ubcf4\ub41c \ub525\ub7ec\ub2dd \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \ud65c\uc6a9\ud55c GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\uc640 \uc774\ub97c \ud1b5\ud574 \ub3d9\ubb3c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uc560\ub2c8\uba40\uac04(AnimalGAN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 2. 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