!pip install torch torchvision<\/code><\/pre>\n3.2 \ub370\uc774\ud130\uc14b \ub85c\ub529<\/h3>\n
\n \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc0dd\uc131\ud560 \uac83\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ub85c\ub529\ud558\uace0 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130\uc14b \ubcc0\ud658 \uc815\uc758: \uc774\ubbf8\uc9c0\ub97c 0-1\ub85c \uc815\uaddc\ud654\ud558\uace0, \ud150\uc11c\ub85c \ubcc0\ud658\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntrain_dataset = torchvision.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\uc640 \ud310\ubcc4\uc790 \uc815\uc758<\/h3>\n
\n \uc774\uc81c \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc55e\uc11c \uc124\uba85\ud55c \uac83\ucc98\ub7fc, \uc0dd\uc131\uc790\ub294 \uc804\uce58 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ud310\ubcc4\ud569\ub2c8\ub2e4.\n <\/p>\n
\nimport torch.nn as nn\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.model = nn.Sequential(\n nn.ConvTranspose2d(100, 256, 4, 1, 0, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(True),\n nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.ReLU(True),\n nn.ConvTranspose2d(128, 1, 4, 2, 1, bias=False),\n nn.Tanh()\n )\n\n def forward(self, input):\n return self.model(input)\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Conv2d(1, 128, 4, 2, 1, bias=False),\n nn.LeakyReLU(0.2),\n nn.Conv2d(128, 256, 4, 2, 1, bias=False),\n nn.BatchNorm2d(256),\n nn.LeakyReLU(0.2),\n nn.Conv2d(256, 1, 4, 1, 0, bias=False),\n nn.Sigmoid()\n )\n\n def forward(self, input):\n return self.model(input)\n <\/code><\/pre>\n3.4 \ubaa8\ub378 \ucd08\uae30\ud654<\/h3>\n
\n \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ubaa8\ub378\uc744 \uc778\uc2a4\ud134\uc2a4\ud654\ud558\uace0, \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ubc14\uc774\ub108\ub9ac \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4 \ud568\uc218\uc640 Adam \ucd5c\uc801\ud654\uae30\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ud654\ngenerator = Generator().to(device)\ndiscriminator = Discriminator().to(device)\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654\uae30 \uc815\uc758\ncriterion = nn.BCELoss()\noptimizerG = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizerD = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n <\/code><\/pre>\n3.5 \ud559\uc2b5 \ub8e8\ud504<\/h3>\n
\n DCGAN\uc758 \ud559\uc2b5\uc744 \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uac01 \uc774\ud130\ub808\uc774\uc158\ub9c8\ub2e4 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uc758 \uc190\uc2e4\uc744 \uae30\ub85d\ud558\uace0, \uba87 \uac00\uc9c0 \uc0d8\ud50c \uc774\ubbf8\uc9c0\ub97c \ucd9c\ub825\ud558\uc5ec \ubaa8\ub378\uc774 \uc81c\ub300\ub85c \ud559\uc2b5\ub418\uace0 \uc788\ub294\uc9c0 \ud655\uc778\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
\nnum_epochs = 50\nfor epoch in range(num_epochs):\n for i, (images, _) in enumerate(train_loader):\n # \ud6c8\ub828 \ub370\uc774\ud130 \uc900\ube44\n images = images.to(device)\n\n # \ub808\uc774\ube14 \uc815\uc758\n batch_size = images.size(0)\n labels = torch.full((batch_size,), 1, device=device) # \uc2e4\uc81c \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub808\uc774\ube14\n noise = torch.randn(batch_size, 100, 1, 1, device=device) # \uc0dd\uc131\uc790 \uc785\ub825 \ub178\uc774\uc988\n\n # ------------------- \ud310\ubcc4\uc790 \ud559\uc2b5 -------------------\n optimizerD.zero_grad()\n\n # \uc2e4\uc81c \uc774\ubbf8\uc9c0\uc758 \uc190\uc2e4\n output = discriminator(images).view(-1)\n lossD_real = criterion(output, labels)\n lossD_real.backward()\n\n # \uac00\uc9dc \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubc0f \uc190\uc2e4 \uacc4\uc0b0\n fake_images = generator(noise)\n labels.fill_(0) # \uac00\uc9dc \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub808\uc774\ube14\n output = discriminator(fake_images.detach()).view(-1)\n lossD_fake = criterion(output, labels)\n lossD_fake.backward()\n\n # \ud310\ubcc4\uc790 \ucd5c\uc801\ud654\n optimizerD.step()\n\n # ------------------- \uc0dd\uc131\uc790 \ud559\uc2b5 -------------------\n optimizerG.zero_grad()\n labels.fill_(1) # \uc0dd\uc131\uc790\ub294 \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc2e4\uc81c\ub85c \ubd84\ub958\ud558\uace0 \uc2f6\uc74c\n output = discriminator(fake_images).view(-1)\n lossG = criterion(output, labels)\n lossG.backward()\n optimizerG.step()\n\n # \uacb0\uacfc \ucd9c\ub825\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss D: {lossD_real.item() + lossD_fake.item()}, Loss G: {lossG.item()}')\n <\/code><\/pre>\n3.6 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\n \ud559\uc2b5\uc774 \uc9c4\ud589\ub41c \ud6c4 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uacb0\uacfc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \uba87 \uac00\uc9c0 \uc0d8\ud50c \uc774\ubbf8\uc9c0\ub97c \ucd9c\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nimport matplotlib.pyplot as plt\n\ndef show_generated_images(num_images=25):\n noise = torch.randn(num_images, 100, 1, 1, device=device)\n with torch.no_grad():\n generated_images = generator(noise).cpu().detach().numpy()\n generated_images = (generated_images + 1) \/ 2 # [0, 1] \ubc94\uc704\ub85c \ubcc0\ud658\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\nshow_generated_images()\n <\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 DCGAN\uc758 \uc774\ub860\uacfc \uad6c\ud604 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0dd\uc131\uc801 \ubaa8\ub378\ub9c1\uc5d0 \uc788\uc5b4 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc73c\uba70, DCGAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc758 \uc601\uc5ed\uc5d0\uc11c \ud2b9\ud788 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc2e4\uc81c \uc0ac\ub840\ub97c \uc801\uc6a9\ud574\ubcf4\uba70 \ubaa8\ub378\uc758 \ud6c8\ub828\uacfc\uc815\uc744 \uc9c1\uc811 \uacbd\ud5d8\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n
DCGAN\uc744 \ud65c\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uac00\uc9c0 \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \uc791\uc5c5\uc5d0 \ub3c4\uc804\ud574\ubcf4\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc778 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uc758 \uc77c\uc885\uc778 DCGAN(Deep Convolutional GAN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. DCGAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubc0f \ubcc0\ud658 \uc791\uc5c5\uc5d0 \ud2b9\ud654\ub41c \ubaa8\ub378\ub85c, \ud2b9\ud788 \uace0\ud574\uc0c1\ub3c4 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc5d0\uc11c \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. 1. GAN\uc758 \uc774\ud574 GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc720\uc0ac\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uba70, \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uac00 \uc2e4\uc81c\uc778\uc9c0 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN”<\/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":[33],"tags":[],"class_list":["post-29957","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN - \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\/29957\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc778 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uc758 \uc77c\uc885\uc778 DCGAN(Deep Convolutional GAN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. DCGAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ubc0f \ubcc0\ud658 \uc791\uc5c5\uc5d0 \ud2b9\ud654\ub41c \ubaa8\ub378\ub85c, \ud2b9\ud788 \uace0\ud574\uc0c1\ub3c4 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc5d0\uc11c \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. 1. GAN\uc758 \uc774\ud574 GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc720\uc0ac\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uba70, \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uac00 \uc2e4\uc81c\uc778\uc9c0 … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29957\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:18:53+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:40+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\/29957\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29957\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN\",\"datePublished\":\"2024-10-28T03:18:53+00:00\",\"dateModified\":\"2024-11-26T06:50:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29957\/\"},\"wordCount\":58,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29957\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29957\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, DCGAN - 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