\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \uae30\uc220\uc774 \ub208\uc5d0 \ub744\uac8c \ubc1c\uc804\ud574 \uc654\uc2b5\ub2c8\ub2e4. Generative Adversarial Networks (GANs)\ub294 \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \uc911\uc2ec\uc5d0 \uc11c \uc788\uc73c\uba70, \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \uc5ec\uc804\ud788 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub418\uc5b4 \uc11c\ub85c \uacbd\uc7c1\ud558\uba74\uc11c \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uc744 \ud65c\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud569\ub2c8\ub2e4.<\/p>\n
\n GAN\uc740 Ian Goodfellow\uc640 \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc774 2014\ub144\uc5d0 \uc18c\uac1c\ud55c \ubaa8\ub378\ub85c, \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub79c\ub364 \ub178\uc774\uc988 \ubca1\ud130\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc785\ub825\ub41c \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\uc790\uc5d0 \uc758\ud574 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0 \ud310\ubcc4\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c\uc758 \ucd9c\ub825\uc744 \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\uba70, \uc774 \uacbd\uc7c1\uc801\uc778 \uacfc\uc815\uc774 GAN\uc758 \ud575\uc2ec\uc785\ub2c8\ub2e4.\n <\/p>\n
\n GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc694\uc57d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n
\ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc5d0 GAN\uc744 \uc0ac\uc6a9\ud558\ub294 \uacfc\uc815\uc740 \uc774\ubbf8\uc9c0 \uc0dd\uc131\uacfc \uc720\uc0ac\ud558\uc9c0\ub9cc, \ud14d\uc2a4\ud2b8\uc758 \ud2b9\uc218\uc131 \ub54c\ubb38\uc5d0 \uba87 \uac00\uc9c0 \ucc28\ubcc4\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \uacbd\uc6b0, \ubca1\ud130 \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\uace0 \uc774\ub97c \ubaa8\ub378\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc744 \uc704\ud55c \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc18c\uc124, \ub274\uc2a4 \uae30\uc0ac, \ud639\uc740 \uc778\ud130\ub137 \uac8c\uc2dc\ubb3c \ub4f1\uc5d0\uc11c \uc218\uc9d1\ud55c \ud14d\uc2a4\ud2b8\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ubaa8\ub378\uc5d0 \uc785\ub825\ud560 \uc218 \uc788\ub294 \ud615\ud0dc\ub85c \ubcc0\ud658\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub294 \uc815\uc81c \ubc0f \ud1a0\ud070\ud654 \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c\ub294 \ub2e4\uc74c\uc758 \ub2e8\uacc4\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4:<\/p>\n
\uc774\uc81c GAN \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ub124\ud2b8\uc6cc\ud06c\ub97c \uc815\uc758\ud558\uace0 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5 \uacfc\uc815\uc744 \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\n\nclass Generator(nn.Module):\n def __init__(self, noise_dim, embed_dim, vocab_size):\n super(Generator, self).__init__()\n self.embed = nn.Embedding(vocab_size, embed_dim)\n self.lstm = nn.LSTM(embed_dim, 256, batch_first=True)\n self.fc = nn.Linear(256, vocab_size)\n\n def forward(self, z):\n x = self.embed(z)\n x, _ = self.lstm(x)\n x = self.fc(x[:, -1, :])\n return x\n <\/code><\/pre>\n3.3.2 \ud310\ubcc4\uc790 \ubaa8\ub378<\/h4>\n\nclass Discriminator(nn.Module):\n def __init__(self, vocab_size, embed_dim):\n super(Discriminator, self).__init__()\n self.embed = nn.Embedding(vocab_size, embed_dim)\n self.lstm = nn.LSTM(embed_dim, 256, batch_first=True)\n self.fc = nn.Linear(256, 1)\n\n def forward(self, x):\n x = self.embed(x)\n x, _ = self.lstm(x)\n x = self.fc(x[:, -1, :])\n return torch.sigmoid(x)\n <\/code><\/pre>\n3.4 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\uc774\uc81c GAN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0ac \ucc28\ub840\uc785\ub2c8\ub2e4. \uc801\uc808\ud55c \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc124\uc815\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uc2e4\ud5d8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \uc190\uc2e4\uc740 \uc11c\ub85c \ubc18\ub300\ub418\ub294 \uad00\uacc4\ub97c \uac00\uc9d1\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nnoise_dim = 100\nembed_dim = 128\nvocab_size = 5000\ngenerator = Generator(noise_dim, embed_dim, vocab_size)\ndiscriminator = Discriminator(vocab_size, embed_dim)\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \ud568\uc218 \uc124\uc815\ncriterion = nn.BCELoss()\nd_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)\ng_optimizer = optim.Adam(generator.parameters(), lr=0.0002)\n\n# \ud559\uc2b5 \uacfc\uc815\nnum_epochs = 10000\nfor epoch in range(num_epochs):\n # \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130 \uc0dd\uc131\n real_data = ... # \uc2e4\uc81c \ub370\uc774\ud130 \ub85c\ub4dc\n noise = torch.randint(0, vocab_size, (batch_size, noise_dim)) # \ubb34\uc791\uc704 \ub178\uc774\uc988\n fake_data = generator(noise)\n\n # \ud310\ubcc4\uc790 \ud559\uc2b5\n discriminator.zero_grad()\n real_labels = torch.ones(batch_size, 1)\n fake_labels = torch.zeros(batch_size, 1)\n output_real = discriminator(real_data)\n output_fake = discriminator(fake_data.detach())\n d_loss = criterion(output_real, real_labels) + criterion(output_fake, fake_labels)\n d_loss.backward()\n d_optimizer.step()\n\n # \uc0dd\uc131\uc790 \ud559\uc2b5\n generator.zero_grad()\n output_fake = discriminator(fake_data)\n g_loss = criterion(output_fake, real_labels) # \ud310\ubcc4\uc790\uac00 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc9c4\uc9dc\ub77c\uace0 \ud310\ub2e8\ud558\uac8c\ub054 \ud559\uc2b5\n g_loss.backward()\n g_optimizer.step()\n <\/code><\/pre>\n4. \ud3c9\uac00 \ubc0f \uacb0\uacfc<\/h2>\n
\ubaa8\ub378\uc758 \ud559\uc2b5\uc774 \uc644\ub8cc\ub418\uba74, \uc0dd\uc131\ub41c \ud14d\uc2a4\ud2b8\uc758 \uc9c8\uc744 \ud3c9\uac00\ud574\uc57c \ud569\ub2c8\ub2e4. \uc0dd\uc131\ub41c \ud14d\uc2a4\ud2b8\ub294 \uc2e4\uc81c \uc785\ub825 \ub370\uc774\ud130\uc640 \ube44\uad50\ud558\uc5ec \uc720\uc0ac\uc131, \ubb38\ubc95, \uc758\ubbf8 \ub4f1\uc744 \uace0\ub824\ud558\uc5ec \ud3c9\uac00\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc8fc\ub85c BLEU(Bilingual Evaluation Understudy) \ub4f1\uc758 \uba54\ud2b8\ub9ad\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
4.1 \ud14d\uc2a4\ud2b8 \uc0dd\uc131<\/h4>\n
\ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc0c8\ub85c\uc6b4 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc9c4\ud589\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ndef generate_text(generator, start_token, max_length):\n generator.eval()\n input_seq = torch.tensor([[start_token]])\n generated_text = []\n\n for _ in range(max_length):\n with torch.no_grad():\n output = generator(input_seq)\n next_token = torch.argmax(output[-1]).item()\n generated_text.append(next_token)\n input_seq = torch.cat((input_seq, torch.tensor([[next_token]])), dim=1)\n\n return generated_text\n\n# \uc2dc\uc791 \ud1a0\ud070\uacfc \ucd5c\ub300 \uae38\uc774\ub97c \uc124\uc815\ud558\uc5ec \ud14d\uc2a4\ud2b8 \uc0dd\uc131\nstart_token = ... # \uc2dc\uc791 \ud1a0\ud070 \uc124\uc815\ngenerated_sequence = generate_text(generator, start_token, max_length=50)\n <\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
GAN\uc744 \uc774\uc6a9\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc740 \ud765\ubbf8\ub86d\uace0 \uc2e0\uc120\ud55c \uc8fc\uc81c\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \uae30\ubc18\uc73c\ub85c GAN\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \ud14d\uc2a4\ud2b8 \uc0dd\uc131\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc0dd\uc131\ub41c \ud14d\uc2a4\ud2b8\ub294 \uc6d0\ubcf8 \ub370\uc774\ud130\uc758 \ud1b5\uacc4\uc801 \ud2b9\uc131\uc744 \ubc18\uc601\ud558\ubbc0\ub85c, \ub2e4\uc591\ud55c \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc744 \ud1b5\ud55c \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \uc5f0\uad6c\ub294 \uacc4\uc18d \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \uac00\ub2a5\uc131\uc740 \ubb34\uad81\ubb34\uc9c4\ud569\ub2c8\ub2e4.<\/p>\n
6. \ucc38\uace0\ubb38\ud5cc<\/h2>\n\n- Goodfellow, I., et al. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems<\/i>.<\/li>\n
- PyTorch Documentation. pytorch.org\/docs\/stable\/index.html<\/a><\/li>\n<\/ol>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"