1~4 \ub2e8\uacc4\ub97c \ubc18\ubcf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n3. \ud30c\uc774\ud1a0\uce58\ub85c GAN \uad6c\ud604\ud558\uae30<\/h2>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud574 GAN\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uae30\ubcf8 GAN \uad6c\uc870\uc758 \uad6c\ud604 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
\ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h3>\n
\uba3c\uc800, \ud30c\uc774\ud1a0\uce58\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \ud30c\uc774\uc36c\uc774 \uc124\uce58\ub41c \ud658\uacbd\uc5d0\uc11c \ub2e4\uc74c \uba85\ub839\uc5b4\ub85c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install torch torchvision<\/code><\/pre>\n\ubaa8\ub378 \uc815\uc758<\/h3>\n
\uba3c\uc800 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# \uc0dd\uc131\uae30\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, x):\n return self.model(x).view(-1, 1, 28, 28)\n\n# \ud310\ubcc4\uae30\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>\n\ud6c8\ub828 \ud568\uc218 \uc815\uc758<\/h3>\n
\ud6c8\ub828 \uacfc\uc815\uc744 \uc815\uc758\ud558\ub294 \ud568\uc218\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4:<\/p>\n
\ndef train_gan(generator, discriminator, data_loader, num_epochs=100, learning_rate=0.0002):\n criterion = nn.BCELoss()\n optimizer_g = optim.Adam(generator.parameters(), lr=learning_rate)\n optimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate)\n\n for epoch in range(num_epochs):\n for real_data, _ in data_loader:\n batch_size = real_data.size(0)\n real_labels = torch.ones(batch_size, 1)\n fake_labels = torch.zeros(batch_size, 1)\n\n # \ud310\ubcc4\uae30 \ud6c8\ub828\n optimizer_d.zero_grad()\n outputs = discriminator(real_data)\n d_loss_real = criterion(outputs, real_labels)\n d_loss_real.backward()\n\n noise = torch.randn(batch_size, 100)\n fake_data = generator(noise)\n outputs = discriminator(fake_data.detach())\n d_loss_fake = criterion(outputs, fake_labels)\n d_loss_fake.backward()\n\n optimizer_d.step()\n\n # \uc0dd\uc131\uae30 \ud6c8\ub828\n optimizer_g.zero_grad()\n outputs = discriminator(fake_data)\n g_loss = criterion(outputs, real_labels)\n g_loss.backward()\n\n optimizer_g.step()\n\n print(f'Epoch [{epoch}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n<\/code><\/pre>\n\ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\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 = DataLoader(dataset, batch_size=64, shuffle=True)\n<\/code><\/pre>\n4. GAN \ud6c8\ub828\ud558\uae30<\/h2>\n
\ubaa8\ub378\uacfc \ub370\uc774\ud130 \ub85c\ub354\uac00 \uc900\ube44\ub418\uc5c8\uc73c\ub2c8, GAN\uc744 \ud6c8\ub828\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ngenerator = Generator()\ndiscriminator = Discriminator()\n\ntrain_gan(generator, discriminator, data_loader, num_epochs=50)\n<\/code><\/pre>\n5. \uacb0\uacfc \uc2dc\uac01\ud654\ud558\uae30<\/h2>\n
\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \uc0dd\uc131\ud55c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport matplotlib.pyplot as plt\n\ndef show_generated_images(generator, num_images=25):\n noise = torch.randn(num_images, 100)\n generated_images = generator(noise).detach().cpu().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>\n6. \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828<\/h2>\n
\uc774\uc81c GAN\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc744 \uc9c4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc740 \uc8fc\uc5b4\uc9c4 \ud658\uacbd\uc5d0\uc11c \ud2b9\uc815 \ubaa9\ud45c\ub97c \ub2ec\uc131\ud558\uae30 \uc704\ud574 \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 GAN\uc744 \uc774\uc6a9\ud558\uc5ec \uc774 \uacfc\uc815\uc744 \uc5b4\ub5bb\uac8c \uc218\ud589\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc5d0 \uc788\uc5b4 GAN\uc758 \ud65c\uc6a9\uc740 \ud765\ubbf8\ub85c\uc6b4 \uc811\uadfc \ubc29\uc2dd\uc785\ub2c8\ub2e4. GAN\uc758 \uc0dd\uc131\uae30\ub294 \ub2e4\uc591\ud55c \uc2dc\ub098\ub9ac\uc624\uc5d0\uc11c\uc758 \ud589\ub3d9\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uc5ed\ud560\uc744 \ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc774\ub7ec\ud55c \ud589\ub3d9\uc774 \uc5bc\ub9c8\ub098 \ubaa9\ud45c\uc5d0 \ubd80\ud569\ud558\ub294\uc9c0 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
\uc544\ub798\ub294 GAN\uc744 \ud65c\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ucee8\ud2b8\ub864\ub7ec\ub97c \ud6c8\ub828\ud558\ub294 \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
\n# \ucee8\ud2b8\ub864\ub7ec \ub124\ud2b8\uc6cc\ud06c \uc815\uc758\nclass Controller(nn.Module):\n def __init__(self):\n super(Controller, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(100, 256),\n nn.ReLU(),\n nn.Linear(256, 3) # \uc608\ub97c \ub4e4\uc5b4 \ud589\ub3d9\uc758 \ucc28\uc6d0 \uc218(3D \ud589\ub3d9)\n )\n\n def forward(self, x):\n return self.model(x)\n\n# \ud6c8\ub828 \uacfc\uc815 \uc815\uc758\ndef train_controller(gan, controller, num_epochs=100):\n optimizer_c = optim.Adam(controller.parameters(), lr=0.001)\n\n for epoch in range(num_epochs):\n noise = torch.randn(64, 100)\n actions = controller(noise)\n \n # GAN\uc758 \uc0dd\uc131\uae30\ub85c \ud589\ub3d9\uc744 \uc0dd\uc131\n generated_data = gan.generator(noise)\n \n # \ud589\ub3d9\uc744 \ud3c9\uac00\ud558\uace0 \uc190\uc2e4 \uacc4\uc0b0\n loss = calculate_loss(generated_data, actions) # \uc190\uc2e4\ud568\uc218\ub294 \uc0ac\uc6a9\uc790\uc758 \uc815\uc758 \ud544\uc694\n optimizer_c.zero_grad()\n loss.backward()\n optimizer_c.step()\n\n if epoch % 10 == 0:\n print(f'Epoch [{epoch}\/{num_epochs}], Controller Loss: {loss.item()}')\n\n# \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828 \uc2dc\uc791\ncontroller = Controller()\ntrain_controller(generator, controller)\n<\/code><\/pre>\n7. \ub9c8\ubb34\ub9ac<\/h2>\n
\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub85c GAN\uc744 \uad6c\ud604\ud558\uace0, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uac04\ub2e8\ud55c \ucee8\ud2b8\ub864\ub7ec\ub97c \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc2e4\uc81c\uc640 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud558\uba70, \ub2e4\uc591\ud55c \uc801\uc6a9 \uac00\ub2a5\uc131\ub3c4 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc744 \ud1b5\ud574 GAN\uc758 \ud65c\uc6a9 \ubc94\uc704\ub97c \ud655\uc7a5\ud560 \uc218 \uc788\uc74c\uc744 \ubcf4\uc5ec\ub4dc\ub838\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub354 \ub098\uc544\uac00 GAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131 \uc678\uc5d0\ub3c4 \ud14d\uc2a4\ud2b8, \ube44\ub514\uc624 \uc0dd\uc131 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc73c\ubbc0\ub85c, \uc774 \uac1c\ub150\uc744 \ud65c\uc6a9\ud574 \ub098\ub9cc\uc758 \ud504\ub85c\uc81d\ud2b8\uc5d0 \ub3c4\uc804\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN(Generative Adversarial Networks)\uc744 \uad6c\ud604\ud558\uace0, \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\uac00 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\ub85c, \uc9c4\uc9dc \uac19\uc740 \ub370\uc774\ud130 \uc0dd\uc131\uc744 \ubaa9\uc801\uc73c\ub85c \ud569\ub2c8\ub2e4. 1. GAN\uc758 \uae30\ubcf8 \uad6c\uc870 GAN\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4: \uc0dd\uc131\uae30(Generator): \ub79c\ub364\ud55c \ub178\uc774\uc988\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\uc11c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ud310\ubcc4\uae30(Discriminator): \uc785\ub825 \ub370\uc774\ud130\ub97c \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\ub85c \ubd84\ub958\ud569\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828”<\/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-29877","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, \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828 - \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\/29877\/\" \/>\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, \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc548\ub155\ud558\uc138\uc694! \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec GAN(Generative Adversarial Networks)\uc744 \uad6c\ud604\ud558\uace0, \ucee8\ud2b8\ub864\ub7ec \ud6c8\ub828\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 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