\uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 Generative Adversarial Networks(GAN)<\/strong>\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. GAN\uc740 2014\ub144 Ian Goodfellow\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \uc0dd\uc131 \ubaa8\ub378\ub85c, \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd(Generator\uc640 Discriminator)\uc744 \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc8fc\ubaa9\ud558\ub294 GAN\uc758 \ud575\uc2ec\uc740 \ub450 \uc2e0\uacbd\ub9dd\uc774 \uc11c\ub85c \uacbd\uc7c1\ud558\ub294 \uad6c\uc870\ub85c, \uc774\ub97c \ud1b5\ud574 \ub354\uc6b1 \uc9c4\ud654\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4.<\/p>\n GAN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uac1c\uc758 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n Generator\uc640 Discriminator\ub294 \uac01\uac01 \ub2e4\uc74c\uacfc \uac19\uc740 \uc190\uc2e4 \ud568\uc218\ub97c \ud1b5\ud574 \ud559\uc2b5\ub429\ub2c8\ub2e4:<\/p>\n GAN \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c GAN\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 GAN \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n GAN\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131\ubfd0\ub9cc \uc544\ub2c8\ub77c \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n \uc774\ubc88 \ud3ec\uc2a4\ud305\uc5d0\uc11c\ub294 GAN\uc758 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \uac04\ub2e8\ud55c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0dd\uc131\uc801 \ubaa8\ub378\uc758 \ud55c \uc885\ub958\ub85c, \ub2e4\uc591\ud55c \ud65c\uc6a9 \uac00\ub2a5\uc131\uc774 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc758 \ubc1c\uc804\uacfc \ub2e4\uc591\ud55c \ubcc0\ud615 \ubaa8\ub378\uc774 \uc81c\uc548\ub418\uace0 \uc788\ub294 \ud604\uc7ac, \uc774\ub97c \ud559\uc2b5\ud558\uace0 \ud65c\uc6a9\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uae30\uc220\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n \uc774 \ud3ec\uc2a4\ud305\uc774 GAN\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub3d5\uace0 \uc2e4\uc81c \uad6c\ud604\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc774\ud6c4\uc5d0\ub3c4 \ub354 \ub2e4\uc591\ud55c \ub525\ub7ec\ub2dd \uc8fc\uc81c\ub85c \ucc3e\uc544\ubd59\uaca0\uc2b5\ub2c8\ub2e4!<\/p>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"1. GAN\uc758 \uae30\ubcf8 \uad6c\uc870<\/h2>\n
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2. GAN\uc758 \ud559\uc2b5 \uacfc\uc815<\/h2>\n
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3. \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c GAN \uad6c\ud604<\/h2>\n
3.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
\n# \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58\n!pip install torch torchvision matplotlib\n<\/code><\/pre>\n3.2 \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\ntrain_set = torchvision.datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n<\/code><\/pre>\n3.3 Generator \ubc0f Discriminator \ubaa8\ub378 \uc815\uc758<\/h3>\n
\nimport torch.nn as nn\n\n# Generator \ubaa8\ub378 \uc815\uc758\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.fc = 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 x = self.fc(x)\n return x.view(-1, 1, 28, 28)\n\n# Discriminator \ubaa8\ub378 \uc815\uc758\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.fc = nn.Sequential(\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 x = x.view(-1, 28 * 28)\n return self.fc(x)\n<\/code><\/pre>\n3.4 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nnum_epochs = 200\nlearning_rate = 0.0002\nbeta1 = 0.5\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc815\uc758\ncriterion = nn.BCELoss()\noptimizerG = torch.optim.Adam(generator.parameters(), lr=learning_rate, betas=(beta1, 0.999))\noptimizerD = torch.optim.Adam(discriminator.parameters(), lr=learning_rate, betas=(beta1, 0.999))\n\n# \ud559\uc2b5 \ub8e8\ud504\nfor epoch in range(num_epochs):\n for i, (data, _) in enumerate(train_loader):\n # \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\uc758 \ub808\uc774\ube14 \uc124\uc815\n real_labels = torch.ones(data.size(0), 1)\n fake_labels = torch.zeros(data.size(0), 1)\n\n # Discriminator \ud559\uc2b5\n optimizerD.zero_grad()\n outputs = discriminator(data)\n lossD_real = criterion(outputs, real_labels)\n lossD_real.backward()\n\n noise = torch.randn(data.size(0), 100)\n fake_data = generator(noise)\n outputs = discriminator(fake_data.detach())\n lossD_fake = criterion(outputs, fake_labels)\n lossD_fake.backward()\n optimizerD.step()\n\n # Generator \ud559\uc2b5\n optimizerG.zero_grad()\n outputs = discriminator(fake_data)\n lossG = criterion(outputs, real_labels)\n lossG.backward()\n optimizerG.step()\n\n if (epoch+1) % 10 == 0:\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss D: {lossD_real.item() + lossD_fake.item():.4f}, Loss G: {lossG.item():.4f}')\n<\/code><\/pre>\n3.5 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\n# \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \uc2dc\uac01\ud654 \ud568\uc218\ndef visualize(generator):\n noise = torch.randn(64, 100)\n fake_data = generator(noise)\n fake_data = fake_data.detach().numpy()\n fake_data = (fake_data + 1) \/ 2 # Normalize to [0, 1]\n\n plt.figure(figsize=(8, 8))\n for i in range(fake_data.shape[0]):\n plt.subplot(8, 8, i+1)\n plt.axis('off')\n plt.imshow(fake_data[i][0], cmap='gray')\n plt.show()\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nvisualize(generator)\n<\/code><\/pre>\n4. GAN\uc758 \ud65c\uc6a9<\/h2>\n
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5. \uacb0\ub860<\/h2>\n