\uc0d8\ud50c\ub9c1:<\/strong> \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n4. PyTorch\uc5d0\uc11c\uc758 \uc0dd\uc131 \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\uc774\uc81c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc0dd\uc131 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c GAN \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 GAN\uc758 \uac1c\uc694<\/h3>\n
GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd \ubaa8\ub378, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\uc758 \ubaa9\ud45c\ub294 \uc9c4\uc9dc\uc640 \uc720\uc0ac\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc0b0\ud558\ub294 \uac83\uc774\uace0, \ud310\ubcc4\uae30\uc758 \ubaa9\ud45c\ub294 \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0\ub97c \ud310\ubcc4\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uacbd\uc7c1 \uad00\uacc4\ub97c \uac00\uc9c0\uba70, \uc774\ub97c \ud1b5\ud574 \uc11c\ub85c\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud569\ub2c8\ub2e4.<\/p>\n
4.2 GAN \ucf54\ub4dc \uc608\uc81c<\/h3>\n
\uc544\ub798\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uc744 \uad6c\ud604\ud55c \uc608\uc81c \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
\n \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\n\n# Hyperparameters\nlatent_size = 100\nnum_epochs = 200\nbatch_size = 64\nlearning_rate = 0.0002\n\n# Transforms\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST dataset\nmnist = torchvision.datasets.MNIST(root='.\/data\/', train=True, transform=transform, download=True)\ndata_loader = torch.utils.data.DataLoader(dataset=mnist, batch_size=batch_size, shuffle=True)\n\n# Generator model\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.network = nn.Sequential(\n nn.Linear(latent_size, 256),\n nn.ReLU(True),\n nn.Linear(256, 512),\n nn.ReLU(True),\n nn.Linear(512, 784),\n nn.Tanh()\n )\n\n def forward(self, x):\n return self.network(x).view(-1, 1, 28, 28)\n\n# Discriminator model\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.network = 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, x):\n return self.network(x.view(-1, 784))\n\n# Initialize models\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# Loss and optimizer\ncriterion = nn.BCELoss()\noptimizer_g = optim.Adam(generator.parameters(), lr=learning_rate)\noptimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate)\n\n# Training\nfor epoch in range(num_epochs):\n for i, (real_images, _) in enumerate(data_loader):\n # Labels\n real_labels = torch.ones(batch_size, 1)\n fake_labels = torch.zeros(batch_size, 1)\n\n # Train discriminator\n optimizer_d.zero_grad()\n outputs = discriminator(real_images)\n d_loss_real = criterion(outputs, real_labels)\n\n z = torch.randn(batch_size, latent_size)\n fake_images = generator(z)\n outputs = discriminator(fake_images.detach())\n d_loss_fake = criterion(outputs, fake_labels)\n\n d_loss = d_loss_real + d_loss_fake\n d_loss.backward()\n optimizer_d.step()\n\n # Train generator\n optimizer_g.zero_grad()\n outputs = discriminator(fake_images)\n g_loss = criterion(outputs, real_labels)\n g_loss.backward()\n optimizer_g.step()\n\n # Print losses and save generated images\n if (epoch + 1) % 10 == 0:\n print(f'Epoch [{epoch + 1}\/{num_epochs}], d_loss: {d_loss.item():.4f}, g_loss: {g_loss.item():.4f}')\n\n with torch.no_grad():\n fake_images = generator(z)\n fake_images = fake_images.view(-1, 1, 28, 28)\n grid = torchvision.utils.make_grid(fake_images, normalize=True)\n plt.imshow(grid.detach().numpy().transpose(1, 2, 0))\n plt.show()\n <\/code>\n <\/pre>\n4.3 \ucf54\ub4dc \uc124\uba85<\/h3>\n
\uc704 \ucf54\ub4dc\ub294 \uac04\ub2e8\ud55c GAN \ubaa8\ub378\uc758 \uad6c\ud604\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uac01 \ubd80\ubd84\uc744 \uc880 \ub354 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ub370\uc774\ud130 \ub85c\ub4dc:<\/strong> MNIST \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \uc815\uaddc\ud654\ud569\ub2c8\ub2e4.<\/li>\n
- \uc0dd\uc131\uae30(Generator):<\/strong> 100 \ucc28\uc6d0\uc758 \ub79c\ub364 \ubca1\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 28×28 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
- \ud310\ubcc4\uae30(Discriminator):<\/strong> \uc785\ub825\ub41c \uc774\ubbf8\uc9c0\ub97c \ubc1b\uc544 \uc9c4\uc9dc\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n
- \ud6c8\ub828 \uacfc\uc815:<\/strong> \ud310\ubcc4\uae30\uc640 \uc0dd\uc131\uae30\ub97c \uad50\ub300\ub85c \ud6c8\ub828\ud569\ub2c8\ub2e4. \ud310\ubcc4\uae30\ub294 \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc640 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0\ub97c \uad6c\ubd84\ud558\ub294 \ubc95\uc744 \ubc30\uc6b0\uace0, \uc0dd\uc131\uae30\ub294 \ud310\ubcc4\uae30\ub97c \uc18d\uc774\uae30 \uc704\ud55c \uc774\ubbf8\uc9c0\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
5. \uc0dd\uc131 \ubaa8\ub378\uc758 \ubbf8\ub798\uc640 \ubc1c\uc804 \ubc29\ud5a5<\/h2>\n
\uc0dd\uc131 \ubaa8\ub378\uc740 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c\uc758 \uc751\uc6a9\uc774 \uae30\ub300\ub429\ub2c8\ub2e4. \ud2b9\ud788, GAN\uacfc VAE\uc640 \uac19\uc740 \uae4a\uc740 \uc0dd\uc131 \ubaa8\ub378\uc740 \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ud070 \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc73c\uba70, \uc774\ub97c \uc704\ud55c \uc0c8\ub85c\uc6b4 \uae30\ubc95\uacfc \uc544\ud0a4\ud14d\ucc98\uac00 \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub610\ud55c \uc0dd\uc131 \ubaa8\ub378\uc740 \uc758\ub8cc, \uc608\uc220, \uc790\uc728\uc8fc\ud589\ucc28 \ubc0f \ub85c\ubcf4\ud2f1\uc2a4\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uae30\ud68c\ub97c \uc81c\uacf5\ud558\uace0 \uc788\uc73c\uba70, \uc774\uc5d0 \ub530\ub978 \uc724\ub9ac\uc801 \ubc0f \ubc95\uc801 \ubb38\uc81c\ub3c4 \ud568\uaed8 \uace0\ub824\ud574\uc57c \ud560 \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4.<\/p>\n
\uacb0\ub860<\/h2>\n
\uc624\ub298\uc740 \uc0dd\uc131 \ubaa8\ub378\uc758 \uac1c\ub150\uacfc PyTorch\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c GAN \uad6c\ud604\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc0dd\uc131 \ubaa8\ub378\uc740 \ub370\uc774\ud130 \uc0dd\uc131, \ub370\uc774\ud130 \uc99d\uac15 \ubc0f \uae30\ud0c0 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud070 \uc7a0\uc7ac\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \ubc1c\uc804\uc774 \uae30\ub300\ub429\ub2c8\ub2e4. \uc774\uc81c \uc5ec\ub7ec\ubd84\ub3c4 \uc0dd\uc131 \ubaa8\ub378\uc758 \uc138\uacc4\ub85c \ubc1c\uc744 \ub0b4\ub51b\uc5b4 \ubcf4\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n