\ud310\ubcc4\uc790\ub294 \uc9c4\uc9dc \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ub192\uc740 \ud655\ub960\uc744, \uac00\uc9dc \ub370\uc774\ud130\uc5d0 \ub300\ud574\uc11c\ub294 \ub0ae\uc740 \ud655\ub960\uc744 \ucd9c\ub825\ud558\ub3c4\ub85d \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n2.3 GAN \uad6c\ud604 \ucf54\ub4dc<\/h3>\n
\ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c GAN\uc744 \uad6c\ud604\ud558\ub294 \ud30c\uc774\uc36c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# \uc0dd\uc131\uc790 \ud074\ub798\uc2a4 \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(inplace=True),\n nn.Linear(256, 512),\n nn.ReLU(inplace=True),\n nn.Linear(512, 1024),\n nn.ReLU(inplace=True),\n nn.Linear(1024, 784),\n nn.Tanh()\n )\n\n def forward(self, z):\n return self.model(z)\n\n# \ud310\ubcc4\uc790 \ud074\ub798\uc2a4 \uc815\uc758\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(784, 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)\n\n# \ub370\uc774\ud130 \ub85c\ub529\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5])\n])\nmnist = datasets.MNIST('data', train=True, download=True, transform=transform)\ndataloader = torch.utils.data.DataLoader(mnist, batch_size=64, shuffle=True)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815\ncriterion = nn.BCELoss()\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\n# GAN \ud559\uc2b5\nnum_epochs = 100\nfor epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(dataloader):\n # \uc2e4\uc81c \ub370\uc774\ud130 \ub808\uc774\ube14 \ubc0f \uac00\uc9dc \ub370\uc774\ud130 \ub808\uc774\ube14 \uc124\uc815\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_D.zero_grad()\n outputs = discriminator(imgs.view(imgs.size(0), -1))\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 print(f'Epoch [{epoch+1}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n <\/code><\/pre>\n<\/div>\n\n
3. VAE(Variational Autoencoder)<\/h2>\n
VAE\ub294 D. P. Kingma\uc640 M. Welling\uc774 2013\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \ud655\ub960\uc801 \ubc29\uc2dd\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. VAE\ub294 \uc778\ucf54\ub354\uc640 \ub514\ucf54\ub354\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \uc778\ucf54\ub354\ub294 \ub370\uc774\ud130\ub97c \uc7a0\uc7ac \uacf5\uac04(latent space)\uc73c\ub85c \uc555\ucd95\ud558\uace0, \ub514\ucf54\ub354\ub294 \uc774 latent space\ub85c\ubd80\ud130 \ub370\uc774\ud130\ub97c \uc7ac\uad6c\uc131\ud569\ub2c8\ub2e4.<\/p>\n
3.1 VAE\uc758 \uad6c\uc870<\/h3>\n
VAE\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uc778\ucf54\ub354(Encoder)<\/strong>: \uc785\ub825 \ub370\uc774\ud130\ub97c \uc7a0\uc7ac \ubca1\ud130(latent vector)\ub85c \ubcc0\ud658\ud558\uba70, \uc774 \ubca1\ud130\ub294 \uc815\uaddc\ubd84\ud3ec\ub97c \ub530\ub974\ub3c4\ub85d \ud559\uc2b5\ub429\ub2c8\ub2e4.<\/li>\n
- \ub514\ucf54\ub354(Decoder)<\/strong>: \uc7a0\uc7ac \ubca1\ud130\ub97c \uc785\ub825 \ubc1b\uc544 \uc6d0\ubcf8 \ub370\uc774\ud130\uc640 \ube44\uc2b7\ud55c \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3.2 VAE \ud559\uc2b5 \uacfc\uc815<\/h3>\n
VAE\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\n- \ub370\uc774\ud130\ub97c \uc778\ucf54\ub354\uc5d0 \ud1b5\uacfc\uc2dc\ucf1c \ud3c9\uade0\uacfc \ubd84\uc0b0\uc744 \uc5bb\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc7ac\ud30c\ub77c\ubbf8\ud130\ud654 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0d8\ud50c\ub9c1\ud569\ub2c8\ub2e4.<\/li>\n
- \uc0d8\ud50c\ub9c1\ud55c \uc7a0\uc7ac \ubca1\ud130\ub97c \ub514\ucf54\ub354\uc5d0 \ud1b5\uacfc\uc2dc\ucf1c \ub370\uc774\ud130\ub97c \uc7ac\uad6c\uc131\ud569\ub2c8\ub2e4.<\/li>\n
- \uc7ac\uad6c\uc131\ub41c \ub370\uc774\ud130\uc640 \uc6d0\ubcf8 \ub370\uc774\ud130 \uac04\uc758 \uc190\uc2e4\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n
3.3 VAE \uad6c\ud604 \ucf54\ub4dc<\/h3>\n
\ub2e4\uc74c\uc740 \uac04\ub2e8\ud55c VAE\ub97c \uad6c\ud604\ud558\ub294 \ud30c\uc774\uc36c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\nclass VAE(nn.Module):\n def __init__(self):\n super(VAE, self).__init__()\n self.encoder = nn.Sequential(\n nn.Linear(784, 400),\n nn.ReLU()\n )\n self.fc_mu = nn.Linear(400, 20)\n self.fc_logvar = nn.Linear(400, 20)\n self.decoder = nn.Sequential(\n nn.Linear(20, 400),\n nn.ReLU(),\n nn.Linear(400, 784),\n nn.Sigmoid()\n )\n\n def reparametrize(self, mu, logvar):\n std = torch.exp(0.5 * logvar)\n eps = torch.randn_like(std)\n return mu + eps * std\n\n def forward(self, x):\n h1 = self.encoder(x.view(-1, 784))\n mu = self.fc_mu(h1)\n logvar = self.fc_logvar(h1)\n z = self.reparametrize(mu, logvar)\n return self.decoder(z), mu, logvar\n\n# VAE \ud559\uc2b5\nvae = VAE()\noptimizer = optim.Adam(vae.parameters(), lr=0.001)\ncriterion = nn.BCELoss(reduction='sum')\n\nnum_epochs = 10\nfor epoch in range(num_epochs):\n for imgs, _ in dataloader:\n optimizer.zero_grad()\n recon_batch, mu, logvar = vae(imgs)\n recon_loss = criterion(recon_batch, imgs.view(-1, 784))\n # Kullback-Leibler divergence\n kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n loss = recon_loss + kld\n loss.backward()\n optimizer.step()\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item()}')\n <\/code><\/pre>\n<\/div>\n\n
4. \uacb0\ub860<\/h2>\n
GAN\uacfc VAE\ub294 \uac01\uac01 \uace0\uc720\ud55c \uc7a5\uc810\uc774 \uc788\uc73c\uba70, \ub2e4\uc591\ud55c \uc0dd\uc131\uc801 \uc791\uc5c5\uc5d0\uc11c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc VAE\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uc600\uc73c\uba70, \uac01 \ubaa8\ub378\uc758 \ub3d9\uc791 \uc6d0\ub9ac\ub97c \uc774\ud574\ud558\uace0 \uc2e4\uc81c\ub85c \ucf54\ub4dc\ub85c \uad6c\ud604\ud574\ubcf4\ub294 \uae30\ud68c\ub97c \uc81c\uacf5\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc0dd\uc131 \ubaa8\ub378\uc778 GAN\uacfc VAE\ub294 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658, \ub370\uc774\ud130 \uc99d\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \uc55e\uc73c\ub85c \ub354\uc6b1 \ubc1c\uc804\ud560 \uac00\ub2a5\uc131\uc774 \uc788\uc73c\uba70, \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uac8c \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/div>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
1. \uc11c\ub860 \uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc0dd\uc131 \ubaa8\ub378(Generative Models)\uc758 \uc911\uc694\uc131\uc774 \ucee4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131 \ubaa8\ub378\uc740 \uad6c\uc870\uc801\uc73c\ub85c \uc11c\ub85c \ub2e4\ub978 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uc5ed\ud560\uc744 \ud558\uba70, \ud2b9\ud788 GAN(Generative Adversarial Networks)\uacfc VAE(Variational Autoencoder)\uac00 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc VAE\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 2. GAN(Generative Adversarial Networks) GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \uc81c\uc548\ud55c \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd(\uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790)\uc774 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud569\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, VAE \ub9cc\ub4e4\uae30”<\/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-29812","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, VAE \ub9cc\ub4e4\uae30 - \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\/29812\/\" \/>\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, VAE \ub9cc\ub4e4\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"1. \uc11c\ub860 \uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uc0dd\uc131 \ubaa8\ub378(Generative Models)\uc758 \uc911\uc694\uc131\uc774 \ucee4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131 \ubaa8\ub378\uc740 \uad6c\uc870\uc801\uc73c\ub85c \uc11c\ub85c \ub2e4\ub978 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uc5ed\ud560\uc744 \ud558\uba70, \ud2b9\ud788 GAN(Generative Adversarial Networks)\uacfc VAE(Variational Autoencoder)\uac00 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc VAE\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 2. 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