{"id":29786,"date":"2024-10-28T03:00:02","date_gmt":"2024-10-28T03:00:02","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29786"},"modified":"2024-11-26T06:51:23","modified_gmt":"2024-11-26T06:51:23","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-ae-%ec%98%a4%ed%86%a0%ec%9d%b8%ec%bd%94%eb%8d%94","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29786\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, AE – \uc624\ud1a0\uc778\ucf54\ub354"},"content":{"rendered":"

<\/p>\n

1. GAN(Generative Adversarial Network)<\/h2>\n

\n GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uae30(generator)\uc640 \ud310\ubcc4\uae30(discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uc774 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uc0dd\uc131\uae30\ub294 \uc2e4\uc81c\ucc98\ub7fc \ubcf4\uc774\ub294 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4.\n <\/p>\n

1.1 GAN\uc758 \uad6c\uc870<\/h3>\n

\n GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ub79c\ub364 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \uad6c\ubcc4\ud569\ub2c8\ub2e4. \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub294 \uac01\uac01\uc758 \ubaa9\uc801\uc744 \uac00\uc9c0\uace0 \ud6c8\ub828\ub429\ub2c8\ub2e4.\n <\/p>\n

1.2 GAN\uc758 \uc190\uc2e4 \ud568\uc218<\/h3>\n

\n GAN\uc758 \uc190\uc2e4 \ud568\uc218\ub294 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ud310\ubcc4\uae30\ub97c \uc18d\uc774\uae30 \uc704\ud574 \ub178\ub825\ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc774\ub97c \uad6c\ubcc4\ud558\uae30 \uc704\ud574 \ub178\ub825\ud569\ub2c8\ub2e4.
\n \\[
\n \\text{Loss}_D = – \\mathbb{E}_{x \\sim p_{data}(x)}[\\log(D(x))] – \\mathbb{E}_{z \\sim p_z(z)}[\\log(1 – D(G(z)))]
\n \\]
\n \\[
\n \\text{Loss}_G = – \\mathbb{E}_{z \\sim p_z(z)}[\\log(D(G(z)))]
\n \\]\n <\/p>\n

1.3 GAN \uc608\uc81c \ucf54\ub4dc<\/h3>\n

\ub2e4\uc74c\uc740 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c GAN\uc744 \uad6c\ud604\ud55c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n

\n        \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc815\uc758\nlatent_size = 100\nbatch_size = 64\nnum_epochs = 200\nlearning_rate = 0.0002\n\n# \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ndataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndata_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n\n# \uc0dd\uc131\uae30 \uc815\uc758\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(latent_size, 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\uae30 \uc815\uc758\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(28*28, 1024),\n            nn.LeakyReLU(0.2),\n            nn.Linear(1024, 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))\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654\uae30 \uc815\uc758\ncriterion = nn.BCELoss()\noptimizer_G = optim.Adam(generator.parameters(), lr=learning_rate)\noptimizer_D = optim.Adam(discriminator.parameters(), lr=learning_rate)\n\n# \ud559\uc2b5 \uacfc\uc815\nfor epoch in range(num_epochs):\n    for i, (imgs, _) in enumerate(data_loader):\n        # \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \uac00\uc9dc \uc774\ubbf8\uc9c0 \ub808\uc774\ube14\n        real_imgs = imgs\n        real_labels = torch.ones(imgs.size(0), 1)  # \uc9c4\uc9dc \ub808\uc774\ube14\n        fake_labels = torch.zeros(imgs.size(0), 1)  # \uac00\uc9dc \ub808\uc774\ube14\n\n        # \ud310\ubcc4\uae30 \ud559\uc2b5\n        optimizer_D.zero_grad()\n        outputs = discriminator(real_imgs)\n        d_loss_real = criterion(outputs, real_labels)\n\n        z = torch.randn(imgs.size(0), latent_size)\n        fake_imgs = generator(z)\n        outputs = discriminator(fake_imgs.detach())\n        d_loss_fake = criterion(outputs, fake_labels)\n\n        d_loss = d_loss_real + d_loss_fake\n        d_loss.backward()\n        optimizer_D.step()\n\n        # \uc0dd\uc131\uae30 \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    print(f\"Epoch [{epoch}\/{num_epochs}], d_loss: {d_loss.item()}, g_loss: {g_loss.item()}\")\n        <\/code>\n    <\/pre>\n
2. \uc624\ud1a0\uc778\ucf54\ub354(Autoencoder)<\/h2>\n
\n        \uc624\ud1a0\uc778\ucf54\ub354\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc555\ucd95\ud558\uace0 \ubcf5\uc6d0\ud558\ub294 \ube44\uc9c0\ub3c4 \ud559\uc2b5 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc785\ub825\uacfc \ub3d9\uc77c\ud55c \ucd9c\ub825\uc744 \ubaa9\ud45c\ub85c \ud558\uba70, \ud2b9\uc131\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc555\ucd95\ud569\ub2c8\ub2e4.\n    <\/p>\n
2.1 \uc624\ud1a0\uc778\ucf54\ub354\uc758 \uad6c\uc870<\/h3>\n
\n        \uc624\ud1a0\uc778\ucf54\ub354\ub294 \uc778\ucf54\ub354\uc640 \ub514\ucf54\ub354 \ub450 \ubd80\ubd84\uc73c\ub85c \ub098\ub269\ub2c8\ub2e4. \uc778\ucf54\ub354\ub294 \uc785\ub825\uc744 \uc800\ucc28\uc6d0 \uc7a0\uc7ac \ud45c\ud604(latent representation)\uc73c\ub85c \ubcc0\ud658\ud558\uba70, \ub514\ucf54\ub354\ub294 \uc774 \uc7a0\uc7ac \ud45c\ud604\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc6d0\ub798\uc758 \uc785\ub825\uc744 \ubcf5\uc6d0\ud569\ub2c8\ub2e4.\n    <\/p>\n
2.2 \uc624\ud1a0\uc778\ucf54\ub354\uc758 \uc190\uc2e4 \ud568\uc218<\/h3>\n
\uc624\ud1a0\uc778\ucf54\ub354\ub294 \uc8fc\ub85c Mean Squared Error(MSE)\ub97c \uc190\uc2e4 \ud568\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc785\ub825\uacfc \ucd9c\ub825 \uac04\uc758 \ucc28\uc774\ub97c \ucd5c\uc18c\ud654\ud569\ub2c8\ub2e4.
\n        \\[
\n        \\text{Loss} = \\frac{1}{N} \\sum_{i=1}^N (x_i – \\hat{x}_i)^2
\n        \\]\n    <\/p>\n
2.3 \uc624\ud1a0\uc778\ucf54\ub354 \uc608\uc81c \ucf54\ub4dc<\/h3>\n
\ub2e4\uc74c\uc740 PyTorch\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c \uc624\ud1a0\uc778\ucf54\ub354\uc758 \uad6c\ud604 \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\n        \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc815\uc758\nbatch_size = 64\nnum_epochs = 20\nlearning_rate = 0.001\n\n# \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ndataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndata_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n\n# \uc624\ud1a0\uc778\ucf54\ub354 \uc815\uc758\nclass Autoencoder(nn.Module):\n    def __init__(self):\n        super(Autoencoder, self).__init__()\n        self.encoder = nn.Sequential(\n            nn.Linear(28*28, 128),\n            nn.ReLU(),\n            nn.Linear(128, 64),\n            nn.ReLU()\n        )\n        self.decoder = nn.Sequential(\n            nn.Linear(64, 128),\n            nn.ReLU(),\n            nn.Linear(128, 28*28),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        x = x.view(-1, 28*28)\n        encoded = self.encoder(x)\n        reconstructed = self.decoder(encoded)\n        return reconstructed.view(-1, 1, 28, 28)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nautoencoder = Autoencoder()\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654\uae30 \uc815\uc758\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(autoencoder.parameters(), lr=learning_rate)\n\n# \ud559\uc2b5 \uacfc\uc815\nfor epoch in range(num_epochs):\n    for imgs, _ in data_loader:\n        optimizer.zero_grad()\n        outputs = autoencoder(imgs)\n        loss = criterion(outputs, imgs)\n        loss.backward()\n        optimizer.step()\n\n    print(f\"Epoch [{epoch}\/{num_epochs}], Loss: {loss.item()}\")\n        <\/code>\n    <\/pre>\n
3. \uacb0\ub860<\/h2>\n
\n        GAN\uacfc \uc624\ud1a0\uc778\ucf54\ub354\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubc0f \ub370\uc774\ud130 \ud45c\ud604 \ubc0f \uc555\ucd95\uc744 \uc704\ud55c \uac15\ub825\ud55c \ub525\ub7ec\ub2dd \uae30\ubc95\uc785\ub2c8\ub2e4. \uac01\uac01\uc758 \uad6c\uc870\uc640 \ud559\uc2b5 \ubc29\ubc95\uc744 \uc774\ud574\ud558\uace0 \uc2e4\uc2b5\ud568\uc73c\ub85c\uc368, \ub354 \ub192\uc740 \uc218\uc900\uc758 \ub525\ub7ec\ub2dd \uc9c0\uc2dd\uc744 \uc313\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n        \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, \ucee4\uc2a4\ud130\ub9c8\uc774\uc9d5\ub41c \uc544\ud0a4\ud14d\ucc98\ub85c \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
1. GAN(Generative Adversarial Network) GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uae30(generator)\uc640 \ud310\ubcc4\uae30(discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uc774 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uc0dd\uc131\uae30\ub294 \uc2e4\uc81c\ucc98\ub7fc \ubcf4\uc774\ub294 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4. 1.1 GAN\uc758 \uad6c\uc870 GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ub79c\ub364 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \uad6c\ubcc4\ud569\ub2c8\ub2e4. \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub294 \uac01\uac01\uc758 \ubaa9\uc801\uc744 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, AE – \uc624\ud1a0\uc778\ucf54\ub354”<\/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-29786","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, AE - \uc624\ud1a0\uc778\ucf54\ub354 - \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\/29786\/\" \/>\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, AE - \uc624\ud1a0\uc778\ucf54\ub354 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"1. GAN(Generative Adversarial Network) GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc778 \uc0dd\uc131\uae30(generator)\uc640 \ud310\ubcc4\uae30(discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uc774 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uc0dd\uc131\uae30\ub294 \uc2e4\uc81c\ucc98\ub7fc \ubcf4\uc774\ub294 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4. 1.1 GAN\uc758 \uad6c\uc870 GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ub79c\ub364 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \uad6c\ubcc4\ud569\ub2c8\ub2e4. \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub294 \uac01\uac01\uc758 \ubaa9\uc801\uc744 … \ub354 \ubcf4\uae30 "\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, AE – \uc624\ud1a0\uc778\ucf54\ub354"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29786\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:00:02+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:51:23+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=\"3\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29786\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29786\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, AE – \uc624\ud1a0\uc778\ucf54\ub354\",\"datePublished\":\"2024-10-28T03:00:02+00:00\",\"dateModified\":\"2024-11-26T06:51:23+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29786\/\"},\"wordCount\":83,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"GAN \ub525\ub7ec\ub2dd \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29786\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29786\/\",\"name\":\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, AE - 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