Generative Adversarial Networks (GANs)\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30 \uac04\uc758 \uacbd\uc7c1\uc744 \ud1b5\ud574 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \uc0dd\uc131\uae30\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc720\uc0ac\ud55c \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uae30\ub294 \uc774 \ub370\uc774\ud130\uac00 \uc2e4\uc81c\uc778\uc9c0 \uac00\uc9dc\uc778\uc9c0 \ud310\ub2e8\ud569\ub2c8\ub2e4. \uc774 \uacbd\uc7c1 \uacfc\uc815\uc744 \ud1b5\ud574 \ub450 \uc2e0\uacbd\ub9dd\uc740 \uc11c\ub85c \ubc1c\uc804\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n
GAN\uc740 Ian Goodfellow\uac00 2014\ub144\uc5d0 \ucc98\uc74c \uc81c\uc548\ud558\uc600\uc73c\uba70, \ud2b9\ud788 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \ubcc0\ud658, \ub370\uc774\ud130 \uc99d\uac15 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc751\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
MuseGAN\uc740 \uc74c\uc545 \uc0dd\uc131\uc744 \uc704\ud55c GAN \uc544\ud0a4\ud14d\ucc98\uc758 \ud558\ub098\ub85c, \uc774 \ubaa8\ub378\uc740 \uc5ec\ub7ec \uc545\uae30\uc758 \ud63c\ud569\ub41c \uc74c\uc545\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. MuseGAN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ud2b9\uc9d5\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
MuseGAN\uc758 \ud6a8\uacfc\uc801\uc778 \ud559\uc2b5\uc744 \uc704\ud574 \ube44\ud3c9\uc790\uac00 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ube44\ud3c9\uc790\ub294 \uc0dd\uc131\ub41c \uc74c\uc545\uc774 \uc5bc\ub9c8\ub098 \uc790\uc5f0\uc2a4\ub7ec\uc6b4\uc9c0\ub97c \ud3c9\uac00\ud558\uace0, \uc0dd\uc131\uae30\uc5d0\uac8c \uac1c\uc120\ud560 \uc218 \uc788\ub294 \ud53c\ub4dc\ubc31\uc744 \uc90d\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uac15\ub825\ud55c \uc801\ub300\uc801 \ud6c8\ub828\uc744 \ud1b5\ud574 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n
MuseGAN\uc740 \uc5ec\ub7ec \uce35\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\ud604\ub41c \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \uc785\ub825\ub41c \ub79c\ub364 \ubca1\ud130\ub97c \ubc1b\uc544\ub4e4\uc5ec \uc74c\uc545 \uc870\uac01\uc744 \uc0dd\uc131\ud558\uba70, \ud310\ubcc4\uae30\ub294 \uc774 \uc870\uac01\uc774 \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uc5bc\ub9c8\ub098 \uc720\uc0ac\ud55c\uc9c0\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
\uc0dd\uc131\uae30\uc758 \uc544\ud0a4\ud14d\ucc98\ub294 RNN \ub610\ub294 CNN\uc744 \uae30\ubc18\uc73c\ub85c \ud560 \uc218 \uc788\uc73c\uba70, \uc8fc\ub85c LSTM \ub610\ub294 GRU \uc140\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n
\ud310\ubcc4\uae30 \ub610\ud55c RNN \ub610\ub294 CNN\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uac01 \uc545\uae30\uc758 \uc74c\uc545 \ud328\ud134\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ubd84\ubcc4\ud558\ub3c4\ub85d \uc124\uacc4\ub429\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c MuseGAN\uc758 GAN \uc544\ud0a4\ud14d\ucc98\ub97c \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798 \uc608\uc81c \ucf54\ub4dc\ub294 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub97c \uac04\ub2e8\ud788 \uad6c\ud604\ud55c \uac83\uc785\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\n\n# \uc0dd\uc131\uae30 \ub124\ud2b8\uc6cc\ud06c\nclass Generator(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(Generator, self).__init__()\n self.l1 = nn.Linear(input_size, hidden_size)\n self.relu = nn.ReLU()\n self.l2 = nn.Linear(hidden_size, output_size)\n self.tanh = nn.Tanh()\n\n def forward(self, x):\n x = self.l1(x)\n x = self.relu(x)\n x = self.l2(x)\n return self.tanh(x)\n\n# \ud310\ubcc4\uae30 \ub124\ud2b8\uc6cc\ud06c\nclass Discriminator(nn.Module):\n def __init__(self, input_size, hidden_size):\n super(Discriminator, self).__init__()\n self.l1 = nn.Linear(input_size, hidden_size)\n self.leaky_relu = nn.LeakyReLU(0.2)\n self.l2 = nn.Linear(hidden_size, 1)\n self.sigmoid = nn.Sigmoid()\n\n def forward(self, x):\n x = self.l1(x)\n x = self.leaky_relu(x)\n x = self.l2(x)\n return self.sigmoid(x)\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\ninput_size = 100\nhidden_size = 256\noutput_size = 128 # \uac00\uc9dc \uc74c\uc545\uc758 \ucc28\uc6d0\nbatch_size = 64\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator(input_size, hidden_size, output_size)\ndiscriminator = Discriminator(output_size, hidden_size)\n<\/code><\/pre>\n5.1 \ud6c8\ub828 \ub8e8\ud504<\/h3>\n
\ud6c8\ub828 \ub8e8\ud504\uc5d0\uc11c\ub294 \uc0dd\uc131\uae30\uc758 \uc190\uc2e4\uacfc \ud310\ubcc4\uae30\uc758 \uc190\uc2e4\uc744 \ubaa8\ub450 \uacc4\uc0b0\ud558\uc5ec \ucd5c\uc801\ud654\ud569\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 \uae30\ubcf8\uc801\uc778 GAN \ud6c8\ub828 \ub8e8\ud504\uc758 \uc608\uc785\ub2c8\ub2e4.<\/p>\n
# \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\ncriterion = nn.BCELoss()\noptimizer_g = torch.optim.Adam(generator.parameters(), lr=0.0002)\noptimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.0002)\n\n# \ud6c8\ub828 \ub8e8\ud504\nnum_epochs = 10000\nfor epoch in range(num_epochs):\n # \ud310\ubcc4\uae30 \ud6c8\ub828\n optimizer_d.zero_grad()\n real_data = torch.randn(batch_size, output_size)\n fake_data = generator(torch.randn(batch_size, input_size)).detach() # \uc0dd\uc131\uae30\uc5d0\uc11c \uc0dd\uc131\ub41c \ub370\uc774\ud130\n real_labels = torch.ones(batch_size, 1) # \uc2e4\uc81c \ub370\uc774\ud130 \ub808\uc774\ube14\n fake_labels = torch.zeros(batch_size, 1) # \uac00\uc9dc \ub370\uc774\ud130 \ub808\uc774\ube14\n\n real_loss = criterion(discriminator(real_data), real_labels)\n fake_loss = criterion(discriminator(fake_data), fake_labels)\n d_loss = real_loss + fake_loss\n d_loss.backward()\n optimizer_d.step()\n\n # \uc0dd\uc131\uae30 \ud6c8\ub828\n optimizer_g.zero_grad()\n fake_data = generator(torch.randn(batch_size, input_size))\n g_loss = criterion(discriminator(fake_data), real_labels) # \uc0dd\uc131\uae30\uac00 \ub9cc\ub4e0 \ub370\uc774\ud130\ub294 '\uc9c4\uc9dc'\ub77c\uace0 \ud310\ub2e8\ud574\uc57c \ud568\n g_loss.backward()\n optimizer_g.step()\n\n if epoch % 1000 == 0:\n print(f\"Epoch [{epoch}\/{num_epochs}] | D Loss: {d_loss.item():.4f} | G Loss: {g_loss.item():.4f}\")\n<\/code><\/pre>\n6. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h2>\n
\ud6c8\ub828\uc774 \ub05d\ub09c \ud6c4\uc5d0\ub294 \uc0dd\uc131\ub41c \uc74c\uc545\uc758 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\uace0, \ud544\uc694\ud558\ub2e4\uba74 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\uac70\ub098 \ub124\ud2b8\uc6cc\ud06c \uc544\ud0a4\ud14d\ucc98\ub97c \uac1c\uc120\ud558\uc5ec \ubaa8\ub378\uc744 \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \uacb0\ub860<\/h2>\n
MuseGAN\uacfc \uac19\uc740 GAN \uc544\ud0a4\ud14d\ucc98\ub294 \uc74c\uc545 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc720\ub9dd\ud55c \uacb0\uacfc\ub97c \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9c1\uc811 GAN \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uc218 \uc788\ub294 \uac83\uc740 \ub370\uc774\ud130 \uacfc\ud559\uc790\uc640 \uc5f0\uad6c\uc790\ub4e4\uc5d0\uac8c \ud070 \uc7a5\uc810\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ub354\uc6b1 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\uc640 \uac1c\uc120\ub41c \ud559\uc2b5 \uae30\ubc95\uc744 \ud1b5\ud574 \ub69c\ub837\ud55c \ubc1c\uc804\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
8. \ucc38\uace0\ubb38\ud5cc<\/h2>\n