pip install torch torchvision matplotlib<\/code><\/pre>\n3.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\uc5d0\uc11c\ub294 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uc27d\uac8c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130 \ubcc0\ud658\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ub85c\ub4dc\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n<\/code><\/pre>\n3.3 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator) \uad6c\ud604<\/h3>\n
\uc774\uc81c GAN\uc758 \ub450 \ud575\uc2ec \uad6c\uc131 \uc694\uc18c\uc778 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub97c \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uac04\ub2e8\ud55c \uc644\uc804\uc5f0\uacb0 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uace0, \ud310\ubcc4\uc790\ub294 CNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.nn as nn\n\n# \uc0dd\uc131\uc790 \ubaa8\ub378\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)\n\n# \ud310\ubcc4\uc790 \ubaa8\ub378\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Flatten(),\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, x):\n return self.model(x)\n<\/code><\/pre>\n3.4 \uc190\uc2e4\ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815<\/h3>\n
GAN\uc758 \ud6c8\ub828\uc744 \uc704\ud574\uc11c \uc190\uc2e4\ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub294 \uac01\uac01 \ub2e4\ub978 \uc190\uc2e4\ud568\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc774\uc9c4 \uad50\ucc28 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
criterion = nn.BCELoss()\noptimizer_G = torch.optim.Adam(Generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizer_D = torch.optim.Adam(Discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n<\/code><\/pre>\n3.5 GAN \ud6c8\ub828 \ub8e8\ud504<\/h3>\n
\uc774\uc81c GAN \ud6c8\ub828\uc744 \uc704\ud55c \ub8e8\ud504\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc77c\uc815 \uc218\uc758 \uc5d0\ud3ec\ud06c \ub3d9\uc548 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub97c \ubc88\uac08\uc544\uac00\uba70 \ud6c8\ub828\ud569\ub2c8\ub2e4.<\/p>\n
def train_gan(generator, discriminator, train_loader, num_epochs=100):\n for epoch in range(num_epochs):\n for i, (real_images, _) in enumerate(train_loader):\n batch_size = real_images.size(0)\n\n # \uc9c4\uc9dc \ub77c\ubca8\uacfc \uac00\uc9dc \ub77c\ubca8 \uc815\uc758\n real_labels = torch.ones(batch_size, 1)\n fake_labels = torch.zeros(batch_size, 1)\n\n # \ud310\ubcc4\uc790 \ud6c8\ub828\n discriminator.zero_grad()\n outputs = discriminator(real_images)\n d_loss_real = criterion(outputs, real_labels)\n d_loss_real.backward()\n\n z = torch.randn(batch_size, 100)\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\uc790 \ud6c8\ub828\n generator.zero_grad()\n outputs = discriminator(fake_images)\n g_loss = criterion(outputs, real_labels)\n g_loss.backward()\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\n# GAN \ud6c8\ub828 \uc2dc\uc791\ngenerator = Generator()\ndiscriminator = Discriminator()\ntrain_gan(generator, discriminator, train_loader)\n<\/code><\/pre>\n4. \uacb0\uacfc \uc2dc\uac01\ud654<\/h2>\n
\ud6c8\ub828\uc774 \uc9c4\ud589\ub41c \ud6c4\uc5d0\ub294 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub4e4\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \uacb0\uacfc\ub97c \ud655\uc778\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\n\ndef show_generated_images(generator, num_images=25):\n z = torch.randn(num_images, 100)\n generated_images = generator(z).detach().numpy()\n \n plt.figure(figsize=(10, 10))\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\n# \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \ubcf4\uc5ec\uc8fc\uae30\nshow_generated_images(generator)\n<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \uac04\ub2e8\ud55c \uad6c\ud604 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub9e4\uc6b0 \uac15\ub825\ud55c \uc0dd\uc131 \ubaa8\ub378\ub85c, \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc0dd\uc131 \ubb38\uc81c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc GAN\uc758 \ud6c8\ub828\uc740 \ubd88\uc548\uc815\ud560 \uc218 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c \uae30\ubc95\uacfc \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub354 \ubcf5\uc7a1\ud55c GAN \uc544\ud0a4\ud14d\ucc98\ub4e4(\uc608: DCGAN, WGAN)\ub3c4 \ud0d0\uc0c9\ud574\ubcf4\uba74 \ud765\ubbf8\ub85c\uc6b4 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c \uc5ec\ub7ec\ubd84\uc740 GAN\uc758 \uae30\ubcf8\uc801\uc778 \ub3d9\uc791 \ubc29\uc2dd\uacfc \uc774\ub97c \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ub354 \ub098\uc544\uac00 \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \uc2dc\ub3c4\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Network, GAN)\uc740 2014\ub144 Ian Goodfellow\uc640 \ub3d9\ub8cc\ub4e4\uc774 \ubc1c\ud45c\ud55c \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uacbd\uc7c1\ud558\uba74\uc11c \ud6c8\ub828\ub429\ub2c8\ub2e4. GAN\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ubcc0\ud658, \uc7ac\uad6c\uc131 \ub4f1\uc758 \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub418\uba70, \ud2b9\ud788 \uace0\ud574\uc0c1\ub3c4\uc758 \uc0ac\uc9c4\uc774\ub098 \uc608\uc220\uc791\ud488 \uc0dd\uc131\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uc758 \uc804\uccb4 \uad6c\uc870\uc640 \ud6c8\ub828 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uad6c\uc870 GAN\uc740 \ub450 \uac1c\uc758 \uc8fc\uc694 \uad6c\uc131 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud6c8\ub828 \uacfc\uc815”<\/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-29886","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, \ud6c8\ub828 \uacfc\uc815 - \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\/29886\/\" \/>\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, \ud6c8\ub828 \uacfc\uc815 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Network, GAN)\uc740 2014\ub144 Ian Goodfellow\uc640 \ub3d9\ub8cc\ub4e4\uc774 \ubc1c\ud45c\ud55c \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\uac00 \uacbd\uc7c1\ud558\uba74\uc11c \ud6c8\ub828\ub429\ub2c8\ub2e4. GAN\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc0dd\uc131, \ubcc0\ud658, \uc7ac\uad6c\uc131 \ub4f1\uc758 \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub418\uba70, \ud2b9\ud788 \uace0\ud574\uc0c1\ub3c4\uc758 \uc0ac\uc9c4\uc774\ub098 \uc608\uc220\uc791\ud488 \uc0dd\uc131\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uc758 \uc804\uccb4 \uad6c\uc870\uc640 \ud6c8\ub828 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. GAN\uc758 \uad6c\uc870 GAN\uc740 \ub450 \uac1c\uc758 \uc8fc\uc694 \uad6c\uc131 … \ub354 \ubcf4\uae30 "\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud6c8\ub828 \uacfc\uc815"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29886\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:00:45+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:58+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29886\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29886\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud6c8\ub828 \uacfc\uc815\",\"datePublished\":\"2024-10-28T03:00:45+00:00\",\"dateModified\":\"2024-11-26T06:50:58+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29886\/\"},\"wordCount\":39,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"GAN \ub525\ub7ec\ub2dd \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29886\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29886\/\",\"name\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ud6c8\ub828 \uacfc\uc815 - 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