\n<\/li>\n<\/li>\n<\/ol>\n\uc774 \uacfc\uc815\uc740 \ubc18\ubcf5\uc801\uc73c\ub85c \uc218\ud589\ub418\uc5b4 \ub450 \ub124\ud2b8\uc6cc\ud06c \ubaa8\ub450 \uac1c\uc120\ub429\ub2c8\ub2e4. \ub2e4\ub9cc, \uae30\uc874 GAN\uc740 \ud6c8\ub828 \ubd88\uc548\uc815\uc131\uacfc \ubaa8\ub4dc \ubd95\uad34(mode collapse) \ubb38\uc81c\uac00 \uc885\uc885 \ubc1c\uc0dd\ud558\uc5ec \ubcf4\ub2e4 \uc548\uc815\uc801\uc778 \ud559\uc2b5\uc744 \uc704\ud55c \uc5ec\ub7ec \uc811\uadfc\ubc95\uc774 \uc5f0\uad6c\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
2. WGAN-GP \uc18c\uac1c<\/h2>\n
WGAN\uc5d0\uc11c\ub294 Wasserstein \uac70\ub9ac \uac1c\ub150\uc744 \ub3c4\uc785\ud558\uc5ec GAN\uc758 \ubcf8\uc9c8\uc801\uc778 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uace0\uc790 \ud558\uc600\uc2b5\ub2c8\ub2e4. Wasserstein \uac70\ub9ac\ub294 \ub450 \ubd84\ud3ec \uac04\uc758 \ucc28\uc774\ub97c \ubcf4\ub2e4 \uba85\ud655\ud788 \uc815\uc758\ud560 \uc218 \uc788\uc5b4, \ub124\ud2b8\uc6cc\ud06c\ub97c \ud6c8\ub828\ud558\ub294 \ub370 \uc720\ub9ac\ud569\ub2c8\ub2e4. WGAN\uc758 \ud575\uc2ec \uc544\uc774\ub514\uc5b4\ub294 \ud310\ubcc4\uc790\uac00 \uc544\ub2cc “\ube44\ud3c9\uac00(critic)”\ub77c\ub294 \uac1c\ub150\uc744 \ub3c4\uc785\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ube44\ud3c9\uac00\ub294 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc640 \uc2e4\uc81c \ub370\uc774\ud130 \uac04\uc758 \uac70\ub9ac\ub97c \ud3c9\uac00\ud558\uace0, \uc774 \ud3c9\uac00 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(MSE) \uc190\uc2e4\uc774 \uc544\ub2cc Wasserstein \uc190\uc2e4\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub124\ud2b8\uc6cc\ud06c\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n
WGAN\uc5d0\uc11c Gradient Penalty (GP)\ub97c \ucd94\uac00\ud568\uc73c\ub85c\uc368 \ud310\ubcc4\uc790\uc758 Lipschitz \uc870\uac74\uc744 \uc900\uc218\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\uc5ec \ud6c8\ub828\uc758 \uc548\uc815\uc131\uc744 \ub354\uc6b1 \ub192\uc600\uc2b5\ub2c8\ub2e4. Gradient Penalty\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ub429\ub2c8\ub2e4:<\/p>\n
GP = \u03bb * E[(||\u2207D(x) ||2 - 1)\u00b2]<\/code><\/p>\n\uc5ec\uae30\uc11c \u03bb<\/code>\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc774\uba70, D(x)<\/code>\ub294 \ud310\ubcc4\uc790\uc758 \ucd9c\ub825\uc785\ub2c8\ub2e4. Gradient Penalty\ub97c \ud1b5\ud574 \ud310\ubcc4\uc790\uc758 \uae30\uc6b8\uae30\uac00 1\ub85c \uc720\uc9c0\ub418\ub3c4\ub85d \uac15\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc744 \ud1b5\ud574 WGAN-GP\ub294 GAN\uc758 \ubd88\uc548\uc815\uc744 \uadf9\ubcf5\ud558\uace0 \ubcf4\ub2e4 \uc548\uc815\uc801\uc778 \ud6c8\ub828\uc774 \uac00\ub2a5\ud574\uc9d1\ub2c8\ub2e4.<\/p>\n3. WGAN-GP \ud30c\uc774\ud1a0\uce58 \uad6c\ud604<\/h2>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec WGAN-GP\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc758 \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n
\n- \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58 \ubc0f \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30<\/li>\n
- \uc0dd\uc131\uc790 \ubc0f \ud310\ubcc4\uc790 \ubaa8\ub378 \uc815\uc758<\/li>\n
- WGAN-GP \ud6c8\ub828 \ub8e8\ud504 \uad6c\ud604<\/li>\n
- \uacb0\uacfc \uc2dc\uac01\ud654<\/li>\n<\/ol>\n
3.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58 \ubc0f \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30<\/h3>\n
\uc6b0\uc120 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0, MNIST \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.<\/p>\n
!pip install torch torchvision matplotlib<\/code><\/pre>\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.datasets as dsets\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\n<\/code><\/pre>\n3.2 \uc0dd\uc131\uc790 \ubc0f \ud310\ubcc4\uc790 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub79c\ub364\ud55c \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc785\ub825\ub41c \uc774\ubbf8\uc9c0\ub97c \ubc14\ud0d5\uc73c\ub85c \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
class 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, 28 * 28),\n nn.Tanh()\n )\n \n def forward(self, z):\n return self.model(z).reshape(-1, 1, 28, 28)\n\nclass Critic(nn.Module):\n def __init__(self):\n super(Critic, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(28 * 28, 1024),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(1024, 512),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(512, 256),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(256, 1)\n )\n\n def forward(self, x):\n return self.model(x.view(-1, 28 * 28))\n<\/code><\/pre>\n3.3 WGAN-GP \ud6c8\ub828 \ub8e8\ud504 \uad6c\ud604<\/h3>\n
\uc774\uc81c WGAN-GP\uc758 \ud6c8\ub828 \ub8e8\ud504\ub97c \uad6c\ud604\ud569\ub2c8\ub2e4. \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c\ub294 \ud310\ubcc4\uc790\ub97c \uc77c\uc815 \ud69f\uc218\ub9cc\ud07c \uc5c5\ub370\uc774\ud2b8\ud55c \ud6c4 \uc0dd\uc131\uc790\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4. Gradient Penalty\ub3c4 \uc190\uc2e4\uc5d0 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n
def compute_gradient_penalty(critic, real_samples, fake_samples):\n alpha = torch.rand(real_samples.size(0), 1, 1, 1).expand_as(real_samples)\n interpolated_samples = alpha * real_samples + (1 - alpha) * fake_samples\n interpolated_samples.requires_grad_(True)\n\n d_interpolated = critic(interpolated_samples)\n\n gradients = torch.autograd.grad(outputs=d_interpolated, inputs=interpolated_samples,\n grad_outputs=torch.ones_like(d_interpolated),\n create_graph=True, retain_graph=True)[0]\n\n gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n return gradient_penalty\n<\/code><\/pre>\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ngenerator = Generator().to(device)\ncritic = Critic().to(device)\n\nlearning_rate = 0.00005\nnum_epochs = 100\ncritic_iterations = 5\nlambda_gp = 10\n\ncriterion = nn.MSELoss()\noptimizer_generator = optim.Adam(generator.parameters(), lr=learning_rate)\noptimizer_critic = optim.Adam(critic.parameters(), lr=learning_rate)\n\nReal_data = dsets.MNIST(root='.\/data', train=True, transform=transforms.ToTensor(), download=True)\n\ndata_loader = torch.utils.data.DataLoader(Real_data, batch_size=64, shuffle=True)\n\nfor epoch in range(num_epochs):\n for i, (real_images, _) in enumerate(data_loader):\n real_images = real_images.to(device)\n\n for _ in range(critic_iterations):\n optimizer_critic.zero_grad()\n\n # Generate fake images\n z = torch.randn(real_images.size(0), 100).to(device)\n fake_images = generator(z)\n\n # Get critic scores\n real_validity = critic(real_images)\n fake_validity = critic(fake_images)\n gradient_penalty = compute_gradient_penalty(critic, real_images.data, fake_images.data)\n\n # Compute loss\n critic_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + lambda_gp * gradient_penalty\n critic_loss.backward()\n optimizer_critic.step()\n\n # Update generator\n optimizer_generator.zero_grad()\n \n # Get generator score\n fake_images = generator(z)\n validity = critic(fake_images)\n generator_loss = -torch.mean(validity)\n generator_loss.backward()\n optimizer_generator.step()\n\n if epoch % 10 == 0:\n print(f\"Epoch: {epoch}\/{num_epochs}, Critic Loss: {critic_loss.item():.4f}, Generator Loss: {generator_loss.item():.4f}\")\n<\/code><\/pre>\n3.4 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ucd5c\uc885\uc801\uc73c\ub85c \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4. \uc774\ub294 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc0dd\uc131\uc790\uac00 \uc5bc\ub9c8\ub098 \uc798 \ud559\uc2b5\ub418\uc5c8\ub294\uc9c0\ub97c \ud655\uc778\ud558\ub294 \uc88b\uc740 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
def show_generated_images(generator, num_images=25):\n z = torch.randn(num_images, 100).to(device)\n generated_images = generator(z).cpu().detach().numpy()\n\n plt.figure(figsize=(5, 5))\n for i in range(num_images):\n plt.subplot(5, 5, i + 1)\n plt.imshow(generated_images[i][0], cmap='gray')\n plt.axis('off')\n plt.show()\n\nshow_generated_images(generator)\n<\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\uc774 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \ubcc0\ud615 \ubaa8\ub378\uc778 WGAN-GP\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0, \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec WGAN-GP\ub97c \uad6c\ud604\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4. WGAN-GP\ub294 Wasserstein \uac70\ub9ac\uc640 Gradient Penalty\ub97c \ud65c\uc6a9\ud558\uc5ec \ubcf4\ub2e4 \uc548\uc815\uc801\uc778 \ud6c8\ub828\uc774 \uac00\ub2a5\ud558\ub2e4\ub294 \uc7a5\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c GAN \uacc4\uc5f4 \ubaa8\ub378\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc774\ubbf8\uc9c0 \ubcc0\ud658, \uc2a4\ud0c0\uc77c \uc804\uc774 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 GAN\uacfc \uadf8 \ubcc0\ud615 \ubaa8\ub378\ub4e4\uc740 \uacc4\uc18d\ud574\uc11c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \ubc1c\uc804\uc774 \uae30\ub300\ub429\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\ub3c4 GAN \ubc0f WGAN-GP\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc804\ud574\ubcf4\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
Generative Adversarial Networks(\uc774\ud558 GAN)\ub294 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \uac15\ub825\ud55c \uc0dd\uc131 \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub418\uba70, \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uacbd\uc7c1\ud558\uc5ec \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc720\uc0ac\ud55c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \uad6c\ubcc4\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774\ub4e4\uc740 \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\uc120\ub418\uba74\uc11c \ucd5c\uc885\uc801\uc73c\ub85c \ub9e4\uc6b0 \ud3c9\ubc94\ud55c \ub370\uc774\ud130\ub97c \uc2e0\ub8b0\uc131 \uc788\uac8c \uc0dd\uc131\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, WGAN-GP”<\/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-29818","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, WGAN-GP - \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\/29818\/\" \/>\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, WGAN-GP - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"Generative Adversarial Networks(\uc774\ud558 GAN)\ub294 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \uac15\ub825\ud55c \uc0dd\uc131 \ubaa8\ub378\uc785\ub2c8\ub2e4. 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