\ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\uc5d0\uc11c GAN(Generative Adversarial Networks)\uc740 \uac00\uc7a5 \ud601\uc2e0\uc801\uc774\uace0 \ub9e4\ub825\uc801\uc778 \uc5f0\uad6c \uc8fc\uc81c \uc911 \ud558\ub098\ub85c \uc790\ub9ac\uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. Ian Goodfellow\uac00 2014\ub144 \ucc98\uc74c \uc81c\uc2dc\ud55c GAN\uc740 \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790 \uac04\uc758 \uacbd\uc7c1\uc801 \uad00\uacc4\ub97c \ud1b5\ud574 \uac15\ub825\ud55c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubaa8\ub378\uc744 \uac00\ub2a5\ud558\uac8c \ud588\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ucd08 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \ucd5c\uadfc 5\ub144\uac04\uc758 \ubc1c\uc804\uc744 \uc0b4\ud3b4\ubcf4\uba70, \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \uad6c\ud604 \uc608\uc81c\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
GAN\uc740 \uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \uad6c\ubd84\uc790\ub294 \uc774 \ub370\uc774\ud130\uac00 \uc2e4\uc81c\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ubcc4\ud569\ub2c8\ub2e4. \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c\ub97c \uacbd\uc7c1\uc801\uc73c\ub85c \ubc1c\uc804\uc2dc\ud0a4\uba70, \uc774\ub97c \ud1b5\ud574 \uc0dd\uc131\uc790\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \ub370\uc774\ud130\ub97c \ub9cc\ub4e4\uc5b4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub450 \uac1c\uc758 \ub124\ud2b8\uc6cc\ud06c\ub97c \ucd5c\uc801\ud654\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub2e4\uc74c\uc758 \uc190\uc2e4 \ud568\uc218\uac00 \uc0ac\uc6a9\ub429\ub2c8\ub2e4:<\/p>\n
\nL(D, G) = E[log D(x)] + E[log(1 – D(G(z)))]<\/p>\n<\/blockquote>\n
\uc5ec\uae30\uc11c D\ub294 \uad6c\ubd84\uc790, G\ub294 \uc0dd\uc131\uc790, x\ub294 \uc2e4\uc81c \ub370\uc774\ud130, z\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988 \ubca1\ud130\uc785\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \ub450 \ub124\ud2b8\uc6cc\ud06c\uac00 \uc81c\ub85c\uc12c \uac8c\uc784\uc744 \ud1b5\ud574 \uc11c\ub85c\ub97c \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
2. GAN\uc758 \ucd5c\uadfc \ubc1c\uc804<\/h2>\n
\ucd5c\uadfc 5\ub144\uac04 GAN\uc740 \uc5ec\ub7ec \uac00\uc9c0 \ubcc0\ud615\uacfc \uac1c\uc120\uc744 \uac70\ucce4\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uadf8 \uc911 \uc77c\ubd80\uc785\ub2c8\ub2e4:<\/p>\n
2.1 DCGAN (Deep Convolutional GAN)<\/h3>\n
DCGAN\uc740 CNN(Convolutional Neural Network)\uc744 \ud65c\uc6a9\ud558\uc5ec GAN\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud588\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 GAN \uad6c\uc870\uc5d0 CNN\uc744 \ub3c4\uc785\ud568\uc73c\ub85c\uc368 \ub192\uc740 \ud488\uc9c8\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc131\uacf5\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n
2.2 WGAN (Wasserstein GAN)<\/h3>\n
WGAN\uc740 GAN\uc758 \ud6c8\ub828 \uc548\uc815\uc131\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 Wasserstein \uac70\ub9ac \uac1c\ub150\uc744 \ub3c4\uc785\ud588\uc2b5\ub2c8\ub2e4. WGAN\uc740 \uae30\uc874 GAN\ubcf4\ub2e4 \ub354 \ube60\ub974\uace0 \uc548\uc815\uc801\uc73c\ub85c \uc218\ub834\ud558\uba70, \ub354 \ub098\uc740 \ud488\uc9c8\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.3 CycleGAN<\/h3>\n
CycleGAN\uc740 \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc0ac\uc9c4 \uc774\ubbf8\uc9c0\ub97c \ud654\ud48d\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc5d0 \ud65c\uc6a9\ub429\ub2c8\ub2e4. CycleGAN\uc740 \uc8fc\uc5b4\uc9c4 \uc774\ubbf8\uc9c0 \uc30d \uc5c6\uc774\ub3c4 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.4 StyleGAN<\/h3>\n
StyleGAN\uc740 \uace0\ud488\uc9c8 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ucd5c\uc2e0 GAN \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc0dd\uc131 \uacfc\uc815\uc5d0\uc11c \uc2a4\ud0c0\uc77c\uc744 \uc870\uc815\ud560 \uc218 \uc788\uc5b4 \ub2e4\uc591\ud55c \uc2a4\ud0c0\uc77c\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \uad6c\ud604<\/h2>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae30\ubcf8 GAN\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uac04\ub2e8\ud55c GAN\uc758 \uad6c\ud604 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
3.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc784\ud3ec\ud2b8<\/h3>\n
import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nimport matplotlib.pyplot as plt\nimport numpy as np\n <\/code><\/pre>\n3.2 \ub370\uc774\ud130\uc14b \ub85c\ub4dc<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uace0 \ubcc0\ud658\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \ubcc0\ud658\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\nmnist = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndataloader = torch.utils.data.DataLoader(mnist, batch_size=64, shuffle=True)\n <\/code><\/pre>\n3.3 \uc0dd\uc131\uc790 \ubc0f \uad6c\ubd84\uc790 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc0dd\uc131\uc790\uc640 \uad6c\ubd84\uc790 \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
# \uc0dd\uc131\uc790 \ubaa8\ub378 \uc815\uc758\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, 784),\n nn.Tanh(),\n )\n\n def forward(self, z):\n return self.model(z)\n\n# \uad6c\ubd84\uc790 \ubaa8\ub378 \uc815\uc758\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, 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 nn.Sigmoid(),\n )\n\n def forward(self, img):\n return self.model(img.view(img.size(0), -1))\n <\/code><\/pre>\n3.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/h3>\n
GAN\uc758 \ud559\uc2b5\uc744 \uc704\ud55c \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
criterion = nn.BCELoss()\ngenerator = Generator()\ndiscriminator = Discriminator()\n\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 <\/code><\/pre>\n3.5 GAN \ud559\uc2b5 \ud504\ub85c\uc138\uc2a4<\/h3>\n
\uc774\uc81c GAN\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
def train_gan(num_epochs=50):\n G_losses = []\n D_losses = []\n for epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(dataloader):\n # \uc9c4\uc9dc \uc774\ubbf8\uc9c0 \ub808\uc774\ube14\uc740 1, \uac00\uc9dc \uc774\ubbf8\uc9c0 \ub808\uc774\ube14\uc740 0\n real_labels = torch.ones(imgs.size(0), 1)\n fake_labels = torch.zeros(imgs.size(0), 1)\n\n # \uad6c\ubd84\uc790 \ud559\uc2b5\n optimizer_D.zero_grad()\n outputs = discriminator(imgs)\n D_loss_real = criterion(outputs, real_labels)\n D_loss_real.backward()\n\n z = torch.randn(imgs.size(0), 100)\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_D.step()\n\n # \uc0dd\uc131\uc790 \ud559\uc2b5\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 G_losses.append(G_loss.item())\n D_losses.append(D_loss_real.item() + D_loss_fake.item())\n\n print(f'Epoch [{epoch}\/{num_epochs}], D_loss: {D_loss_fake.item() + D_loss_real.item()}, G_loss: {G_loss.item()}')\n \n return G_losses, D_losses\n <\/code><\/pre>\n3.6 \ud559\uc2b5 \uc2e4\ud589 \ubc0f \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ud559\uc2b5\uc744 \uc2e4\ud589\ud558\uace0 \uc190\uc2e4 \uac12\uc744 \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/p>\n
G_losses, D_losses = train_gan(num_epochs=50)\n\nplt.plot(G_losses, label='Generator Loss')\nplt.plot(D_losses, label='Discriminator Loss')\nplt.title('Losses during Training')\nplt.xlabel('Iterations')\nplt.ylabel('Loss')\nplt.legend()\nplt.show()\n <\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\uc774 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ucd5c\uadfc 5\ub144\uac04\uc758 \ubc1c\uc804\uc744 \uc0b4\ud3b4\ubcf4\uace0, \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uc744 \uad6c\ud604\ud558\ub294 \uc608\uc81c\ub97c \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. GAN\uc740 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \ub525\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc704\uce58\ub97c \ucc28\uc9c0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uc5f0\uad6c \ubc29\ud5a5\uc73c\ub85c\ub294 \ub354\uc6b1 \uc548\uc815\uc801\uc778 \ud559\uc2b5 \ubc29\ubc95\uacfc \uace0\ud574\uc0c1\ub3c4 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc744 \uc704\ud55c \uc5f0\uad6c\uac00 \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
5. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n