\n!pip install torch torchvision matplotlib\n <\/code><\/pre>\n3.2 \uc0dd\uc131\uc790 \ubc0f \ud310\ubcc4\uc790 \ub124\ud2b8\uc6cc\ud06c \uc815\uc758<\/h3>\n
\uba3c\uc800 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ub124\ud2b8\uc6cc\ud06c\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc774\ub4e4\uc740 \uac01\uac01 \uc18d\uc131\uc5d0 \ub530\ub77c \uc124\uacc4\ub429\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\n\n# \uc0dd\uc131\uc790 \uc815\uc758\nclass Generator(nn.Module):\n def __init__(self, input_size, output_size):\n super(Generator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(input_size, 128),\n nn.ReLU(),\n nn.Linear(128, 256),\n nn.ReLU(),\n nn.Linear(256, output_size),\n nn.Tanh() # \ucd9c\ub825\uac12\uc744 -1\uacfc 1 \uc0ac\uc774\ub85c \uc81c\ud55c\n )\n \n def forward(self, z):\n return self.model(z)\n\n# \ud310\ubcc4\uc790 \uc815\uc758\nclass Discriminator(nn.Module):\n def __init__(self, input_size):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(input_size, 256),\n nn.LeakyReLU(0.2),\n nn.Linear(256, 128),\n nn.LeakyReLU(0.2),\n nn.Linear(128, 1),\n nn.Sigmoid() # \ucd9c\ub825\uac12\uc744 0\uacfc 1 \uc0ac\uc774\ub85c \uc81c\ud55c\n )\n \n def forward(self, x):\n return self.model(x)\n <\/code><\/pre>\n3.3 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uac83\uc785\ub2c8\ub2e4. MNIST\ub294 \uc190\uc73c\ub85c \uc4f4 \uc22b\uc790 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc14b\uc73c\ub85c, 0\ubd80\ud130 9\uae4c\uc9c0\uc758 \uc22b\uc790\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ubcc0\ud658\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,)) # \uc815\uaddc\ud654\n])\n\nmnist = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndataloader = torch.utils.data.DataLoader(mnist, batch_size=64, shuffle=True)\n <\/code><\/pre>\n3.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uae30\ubc95 \uc815\uc758<\/h3>\n
GAN\uc740 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\uac00 \uc11c\ub85c \uacbd\ud569\ud558\ub294 \uad6c\uc870\uc774\ubbc0\ub85c \uac01\uac01\uc758 \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc774\uc9c4 \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uae30\ubc95 \uc124\uc815\ncriterion = nn.BCELoss()\nlr = 0.0002\nbeta1 = 0.5\n\ngenerator = Generator(input_size=100, output_size=784).cuda()\ndiscriminator = Discriminator(input_size=784).cuda()\n\noptimizer_G = torch.optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))\noptimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))\n <\/code><\/pre>\n3.5 GAN \ud559\uc2b5 \uacfc\uc815 \uad6c\ud604<\/h3>\n
\uc774\uc81c GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uac01 \ubc30\uce58\ub9c8\ub2e4 \uc0dd\uc131\uc790 \ubc0f \ud310\ubcc4\uc790\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ubc29\ubc95\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n
\nnum_epochs = 50\n\nfor epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(dataloader):\n # \uc9c4\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc704\ud55c \ub808\uc774\ube14 (1)\n real_imgs = imgs.view(imgs.size(0), -1).cuda()\n real_labels = torch.ones((imgs.size(0), 1)).cuda()\n\n # \uac00\uc9dc \uc774\ubbf8\uc9c0\ub97c \uc704\ud55c \ub808\uc774\ube14 (0)\n noise = torch.randn((imgs.size(0), 100)).cuda()\n fake_imgs = generator(noise)\n fake_labels = torch.zeros((imgs.size(0), 1)).cuda()\n\n # \ud310\ubcc4\uc790 \uc5c5\ub370\uc774\ud2b8\n optimizer_D.zero_grad()\n outputs = discriminator(real_imgs)\n d_loss_real = criterion(outputs, real_labels)\n d_loss_real.backward()\n\n outputs = discriminator(fake_imgs.detach())\n d_loss_fake = criterion(outputs, fake_labels)\n d_loss_fake.backward()\n\n optimizer_D.step()\n \n # \uc0dd\uc131\uc790 \uc5c5\ub370\uc774\ud2b8\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>\n3.6 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \uc2dc\uac01\ud654<\/h3>\n
\ud559\uc2b5\uc774 \ub05d\ub09c \ud6c4\uc5d0 \uc0dd\uc131\uc790\uc5d0 \uc758\ud574 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport matplotlib.pyplot as plt\n\n# \uc774\ubbf8\uc9c0 \uc0dd\uc131\nnoise = torch.randn(16, 100).cuda()\nfake_imgs = generator(noise).view(-1, 1, 28, 28).cpu().data\n\n# \uc774\ubbf8\uc9c0 \uc2dc\uac01\ud654\nplt.figure(figsize=(10, 10))\nfor i in range(16):\n plt.subplot(4, 4, i+1)\n plt.imshow(fake_imgs[i].squeeze(), cmap='gray')\n plt.axis('off')\nplt.show()\n <\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 GAN\uc758 \uc774\ub860\uc801 \ubc30\uacbd\uacfc \ub354\ubd88\uc5b4 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \uae30\ubcf8\uc801\uc778 GAN \ubaa8\ub378\uc758 \uad6c\ud604 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0dd\uc131 \ubaa8\ub378\ub9c1 \ubd84\uc57c\uc5d0\uc11c \ub9ce\uc740 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \ubc1c\uc804\uc774 \uae30\ub300\ub429\ub2c8\ub2e4. \ubcf8 \uc608\uc81c\ub97c \ud1b5\ud574 GAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\uc640 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\uc758 \uad6c\ud604 \ubc29\ubc95\uc744 \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
GAN\uc758 \ubc1c\uc804\uc740 \uc6b0\ub9ac\uac00 \ub370\uc774\ud130 \uc0dd\uc131 \ubc0f \ucc98\ub9ac\ud558\ub294 \ubc29\uc2dd\uc744 \ubcc0\ud654\uc2dc\ud0ac \uac83\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 GAN\uacfc \uac19\uc740 \uc0dd\uc131\uc801 \ubaa8\ub378\uc758 \uc5f0\uad6c\uac00 \ub354\uc6b1 \ud65c\ubc1c\ud788 \uc774\ub8e8\uc5b4\uc9c0\uae38 \uae30\ub300\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \ub9ce\uc740 \uc601\ud5a5\uc744 \ubbf8\uce58\uace0 \uc788\uc73c\uba70, \ud2b9\ud788 \uc0dd\uc131 \ubaa8\ub378\ub9c1(Generative Modeling)\uc740 \ub370\uc774\ud130 \uc0dd\uc131\uc758 \uc0c8\ub85c\uc6b4 \uc9c0\ud3c9\uc744 \uc5f4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GAN)\uc740 \uc774\ub7ec\ud55c \uc0dd\uc131 \ubaa8\ub378\ub9c1 \uc911\uc5d0\uc11c \uac00\uc7a5 \uc720\uba85\ud55c \ubaa8\ub378 \uc911 \ud558\ub098\ub85c, \uc6d0\uc2dc \ub370\uc774\ud130\uc5d0\uc11c \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub2a5\ub825\uc774 \ub6f0\uc5b4\ub0a9\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc758 \uc8fc\uc694 \uac1c\ub150, \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95 \ubc0f \uc774\ub97c \ud1b5\ud55c \uc2e4\uc2b5 \uc608\uc81c\ub97c \uc790\uc138\ud788 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc774\ub780”<\/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-29845","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, \uc0dd\uc131 \ubaa8\ub378\ub9c1\uc774\ub780 - 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