{"id":29959,"date":"2024-10-28T03:18:53","date_gmt":"2024-10-28T03:18:53","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29959"},"modified":"2024-11-26T06:50:41","modified_gmt":"2024-11-26T06:50:41","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-gan-%ea%b5%ac%ed%98%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29959\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GAN \uad6c\ud604"},"content":{"rendered":"

<\/p>\n

\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 GAN(Generative Adversarial Network)\uc744 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uae4a\uc740 \uc124\uba85\uc744 \ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc88b\uc740 \uc0dd\uc131 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub3c4\uad6c\ub85c\uc11c, \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658, \ub370\uc774\ud130 \ubcf4\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uac15\uc88c\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uac01 \uad6c\uc131 \uc694\uc18c\ub97c \uad6c\ud604\ud558\uace0, \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc2e4\uc81c\uc801\uc778 \uc608\uc81c\ub97c \ud1b5\ud574 GAN\uc774 \uc5b4\ub5bb\uac8c \uc791\ub3d9\ud558\ub294\uc9c0 \uc774\ud574\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n

GAN\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc774 \ub450 \ubaa8\ub378\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba74\uc11c \ud559\uc2b5\ud558\uac8c \ub418\uba70, \uc774 \uacfc\uc815\uc774 GAN\uc758 \ud575\uc2ec\uc785\ub2c8\ub2e4.<\/p>\n

1.1 \uc0dd\uc131\uc790(Generator)<\/h3>\n

\uc0dd\uc131\uc790\uc758 \uc5ed\ud560\uc740 \ub79c\ub364 \ub178\uc774\uc988\ub97c \uc785\ub825 \ubc1b\uc544\uc11c \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc720\uc0ac\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ubaa8\ubc29\ud558\ub294 \ubc29\ubc95\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n

1.2 \ud310\ubcc4\uc790(Discriminator)<\/h3>\n

\ud310\ubcc4\uc790\ub294 \uc785\ub825\ub41c \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc544\ub2c8\uba74 \uc0dd\uc131\uc790\uac00 \ub9cc\ub4e4\uc5b4\ub0b8 \uac00\uc9dc \ub370\uc774\ud130\uc778\uc9c0\ub97c \ud310\ubcc4\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uad6c\ubcc4\ud558\ub294 \ubc29\ubc95\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n

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

GAN\uc758 \ud559\uc2b5\uc740 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ud310\ubcc4\uc790\ub97c \uc18d\uc774\uae30 \uc704\ud574 \uc810\uc810 \ub354 \ub098\uc740 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uba70, \ud310\ubcc4\uc790\ub294 \uc774\ub7ec\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc778\uc2dd\ud558\uae30 \uc704\ud574 \ub178\ub825\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc774 \ubc18\ubcf5\ub418\uba74\uc11c \ub450 \ubaa8\ub378\uc740 \uc810\uc810 \ub354 \ubc1c\uc804\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

2. GAN\uc758 \uad6c\uc131\uc694\uc18c \uad6c\ud604\ud558\uae30<\/h2>\n

\uc774\uc81c GAN\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub4e4\uc744 \ucf54\ub4dc\ub97c \ud1b5\ud574 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uac04\ub2e8\ud55c GAN\uc744 \uad6c\ud604\ud558\uace0, MNIST \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc0dd\uc131\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

2.1 \ud658\uacbd \uc124\uc815<\/h3>\n

\uc6b0\uc120, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uc5ec \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n

!pip install torch torchvision matplotlib\nimport torch\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport matplotlib.pyplot as plt<\/code><\/pre>\n
2.2 \ub370\uc774\ud130\uc14b \ub85c\ub4dc<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uace0, \uc804\ucc98\ub9ac\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
# \ub370\uc774\ud130\uc14b \uc900\ube44\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\nmnist = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndataloader = torch.utils.data.DataLoader(mnist, batch_size=64, shuffle=True)<\/code><\/pre>\n
2.3 \uc0dd\uc131\uc790(Generator) \ubaa8\ub378 \uad6c\ud604<\/h3>\n
\uc0dd\uc131\uc790\ub294 \uc785\ub825\ub41c \ub178\uc774\uc988 \ubca1\ud130\ub97c \ubc1b\uc544\uc11c \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\ud558\ub294 \uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4.<\/p>\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, 28 * 28),\n            nn.Tanh()\n        )\n    \n    def forward(self, z):\n        return self.model(z).view(-1, 1, 28, 28)<\/code><\/pre>\n
2.4 \ud310\ubcc4\uc790(Discriminator) \ubaa8\ub378 \uad6c\ud604<\/h3>\n
\ud310\ubcc4\uc790\ub294 \uc785\ub825\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2e4\uc81c \uc774\ubbf8\uc9c0\uc778\uc9c0 \uac00\uc9dc \uc774\ubbf8\uc9c0\uc778\uc9c0 \ud310\ubcc4\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/p>\n
class Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(28 * 28, 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.view(-1, 28 * 28))<\/code><\/pre>\n
2.5 \ubaa8\ub378 \ucd08\uae30\ud654<\/h3>\n
\uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\uace0, \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
generator = Generator()\ndiscriminator = Discriminator()\n\ncriterion = nn.BCELoss()\noptimizer_gen = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizer_disc = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))<\/code><\/pre>\n
2.6 GAN \ud559\uc2b5 \ub8e8\ud504<\/h3>\n
\uc774\uc81c GAN\uc758 \ud559\uc2b5 \ub8e8\ud504\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uc758 \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0, \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n
def train_gan(num_epochs):\n    for epoch in range(num_epochs):\n        for i, (imgs, _) in enumerate(dataloader):\n            z = torch.randn(imgs.size(0), 100)\n            real_labels = torch.ones(imgs.size(0), 1)\n            fake_labels = torch.zeros(imgs.size(0), 1)\n\n            # \ud310\ubcc4\uc790 \ud559\uc2b5\n            optimizer_disc.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            optimizer_disc.step()\n\n            # \uc0dd\uc131\uc790 \ud559\uc2b5\n            optimizer_gen.zero_grad()\n            outputs = discriminator(fake_imgs)\n            g_loss = criterion(outputs, real_labels)\n            g_loss.backward()\n            optimizer_gen.step()\n\n        if (epoch + 1) % 10 == 0:\n            print(f'Epoch [{epoch + 1}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item():.4f}, g_loss: {g_loss.item():.4f}')<\/code><\/pre>\n
3. GAN \uc2e4\ud589\ud558\uae30<\/h2>\n
\uc774\uc81c GAN\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
num_epochs = 100\ntrain_gan(num_epochs)\n\ndef show_generated_images(generator, num_images=16):\n    z = torch.randn(num_images, 100)\n    fake_images = generator(z).detach()\n    plt.figure(figsize=(10, 10))\n    for i in range(num_images):\n        plt.subplot(4, 4, i + 1)\n        plt.imshow(fake_images[i][0], cmap='gray')\n        plt.axis('off')\n    plt.show()\n\nshow_generated_images(generator)<\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c GAN \ubaa8\ub378 \uad6c\ud604 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, GAN\uc744 \ud1b5\ud574 \uc778\uacf5\uc9c0\ub2a5\uc758 \uac00\ub2a5\uc131\uc744 \ub354\uc6b1 \ud655\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub97c \ubc14\ud0d5\uc73c\ub85c \ub354 \ubcf5\uc7a1\ud55c GAN \ubcc0\ud615 \ubaa8\ub378\ub4e4\uc744 \ud0d0\uad6c\ud574\ubcf4\ub294 \uac83\ub3c4 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc0c1\uc774 \ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \uad6c\ud604\uc5d0 \ub300\ud55c \uac15\uc88c\uc600\uc2b5\ub2c8\ub2e4. \ud559\uc2b5 \uc911 \uad81\uae08\ud55c \uc810\uc774\ub098 \ub354 \uc54c\uace0 \uc2f6\uc740 \ub0b4\uc6a9\uc774 \uc788\ub2e4\uba74 \uc5b8\uc81c\ub4e0\uc9c0 \ub313\uae00\ub85c \ubb38\uc758\ud574 \uc8fc\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 GAN(Generative Adversarial Network)\uc744 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uae4a\uc740 \uc124\uba85\uc744 \ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc88b\uc740 \uc0dd\uc131 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub3c4\uad6c\ub85c\uc11c, \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658, \ub370\uc774\ud130 \ubcf4\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uac15\uc88c\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uac01 \uad6c\uc131 \uc694\uc18c\ub97c \uad6c\ud604\ud558\uace0, \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc2e4\uc81c\uc801\uc778 \uc608\uc81c\ub97c \ud1b5\ud574 GAN\uc774 \uc5b4\ub5bb\uac8c \uc791\ub3d9\ud558\ub294\uc9c0 \uc774\ud574\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ubcf8 \uac1c\ub150 GAN\uc740 \ub450 \uac00\uc9c0 \uc8fc\uc694 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c,  GAN \uad6c\ud604”<\/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-29959","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GAN \uad6c\ud604 - \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\/29959\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, GAN \uad6c\ud604 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 GAN(Generative Adversarial Network)\uc744 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uae4a\uc740 \uc124\uba85\uc744 \ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc88b\uc740 \uc0dd\uc131 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub3c4\uad6c\ub85c\uc11c, \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658, \ub370\uc774\ud130 \ubcf4\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uac15\uc88c\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uac01 \uad6c\uc131 \uc694\uc18c\ub97c \uad6c\ud604\ud558\uace0, \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc2e4\uc81c\uc801\uc778 \uc608\uc81c\ub97c \ud1b5\ud574 GAN\uc774 \uc5b4\ub5bb\uac8c \uc791\ub3d9\ud558\ub294\uc9c0 \uc774\ud574\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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