{"id":29808,"date":"2024-10-28T03:00:16","date_gmt":"2024-10-28T03:00:16","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29808"},"modified":"2024-11-26T06:51:17","modified_gmt":"2024-11-26T06:51:17","slug":"%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98%eb%a5%bc-%ed%99%9c%ec%9a%a9%ed%95%9c-gan-%eb%94%a5%eb%9f%ac%eb%8b%9d-rnn-%ed%99%95%ec%9e%a5","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29808\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, RNN \ud655\uc7a5"},"content":{"rendered":"

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\ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c GAN(Generative Adversarial Network)\uacfc RNN(Recurrent Neural Network)\uc740 \ub9ce\uc740 \uc8fc\ubaa9\uc744 \ubc1b\uc73c\uba70 \ubc1c\uc804\ud574\uc654\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uba70, RNN\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc801\ud569\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \uc774 \ub450 \ubaa8\ub378\uc744 \uc5b4\ub5bb\uac8c \ud655\uc7a5\ud560 \uc218 \uc788\ub294\uc9c0 \uc608\uc81c\ub97c \ud1b5\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. GAN(\uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd)\uc758 \uae30\ucd08<\/h2>\n

1.1 GAN\uc758 \uad6c\uc870<\/h3>\n

GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uae30(Generator)\uc640 \ud310\ubcc4\uae30(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uae30\ub294 \ub79c\ub364 \ub178\uc774\uc988\ub97c \uc785\ub825\ubc1b\uc544 \uc9c4\uc9dc \uac19\uc740 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uba70, \ud310\ubcc4\uae30\ub294 \uc785\ub825\ubc1b\uc740 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uc0dd\uc131\ub41c \uac83\uc778\uc9c0 \ud310\ubcc4\ud569\ub2c8\ub2e4. \uc774 \ub458\uc740 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

1.2 GAN\uc758 \uc791\ub3d9 \uc6d0\ub9ac<\/h3>\n

GAN\uc758 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

    \n
  1. \uc0dd\uc131\uae30\uac00 \ub79c\ub364\ud55c \ub178\uc774\uc988\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n
  2. \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc640 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ud310\ubcc4\uae30\uc5d0\uac8c \uc785\ub825\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ud310\ubcc4\uae30\ub294 \uc2e4\uc81c \ub370\uc774\ud130\uc640 \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub97c \uad6c\ubcc4\ud558\uace0, \uc774 \uc815\ubcf4\ub294 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    \uc774 \uacfc\uc815\uc740 \ubc18\ubcf5\ub418\uba74\uc11c \uc0dd\uc131\uae30\ub294 \uc810\uc810 \ub354 \uc9c4\uc9dc \uac19\uc740 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uac8c \ub418\uace0, \ud310\ubcc4\uae30\ub294 \uc774\ub97c \ub354\uc6b1 \uc798 \uad6c\ubcc4\ud574\ub0b4\ub294 \ub2a5\ub825\uc744 \ud0a4\uc6b0\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

    1.3 PyTorch\ub85c GAN \uad6c\ud604\ud558\uae30<\/h3>\n

    \uc774\uc81c GAN\uc744 PyTorch\ub85c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uae30\ubcf8\uc801\uc778 GAN \uad6c\uc870\ub97c \uc124\uba85\ud558\uace0 \ucf54\ub4dc \uc608\uc81c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n

    import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# \uc0dd\uc131\uae30 \ud074\ub798\uc2a4 \uc815\uc758\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(100, 256),\n            nn.ReLU(inplace=True),\n            nn.Linear(256, 512),\n            nn.ReLU(inplace=True),\n            nn.Linear(512, 1024),\n            nn.ReLU(inplace=True),\n            nn.Linear(1024, 784),\n            nn.Tanh()\n        )\n\n    def forward(self, z):\n        return self.model(z)\n\n# \ud310\ubcc4\uae30 \ud074\ub798\uc2a4 \uc815\uc758\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Linear(784, 512),\n            nn.ReLU(inplace=True),\n            nn.Linear(512, 256),\n            nn.ReLU(inplace=True),\n            nn.Linear(256, 1),\n            nn.Sigmoid()\n        )\n\n    def forward(self, x):\n        return self.model(x)\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc \ubc0f \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ndataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)\n\n# GAN \ud559\uc2b5\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\ngenerator = Generator().to(device)\ndiscriminator = Discriminator().to(device)\n\ncriterion = nn.BCELoss()\noptimizer_G = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\noptimizer_D = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n\nfor epoch in range(50):\n    for i, (images, _) in enumerate(dataloader):\n        images = images.view(images.size(0), -1).to(device)\n        batch_size = images.size(0)\n\n        # \uc9c4\uc9dc\uc640 \uac00\uc9dc \ub808\uc774\ube14 \uc0dd\uc131\n        real_labels = torch.ones(batch_size, 1).to(device)\n        fake_labels = torch.zeros(batch_size, 1).to(device)\n\n        # \ud310\ubcc4\uae30 \ud559\uc2b5\n        optimizer_D.zero_grad()\n        outputs = discriminator(images)\n        d_loss_real = criterion(outputs, real_labels)\n        d_loss_real.backward()\n\n        z = torch.randn(batch_size, 100).to(device)\n        fake_images = generator(z)\n        outputs = discriminator(fake_images.detach())\n        d_loss_fake = criterion(outputs, fake_labels)\n        d_loss_fake.backward()\n        optimizer_D.step()\n\n        # \uc0dd\uc131\uae30 \ud559\uc2b5\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(f'Epoch [{epoch+1}\/{50}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n\n# \uc0dd\uc131\ud55c \uc774\ubbf8\uc9c0 \ubcf4\uae30 (\uc2e4\uc81c \ucf54\ub4dc\uc5d0\uc11c\ub294 \uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ud568\uc218 \ud544\uc694)\n<\/code><\/pre>\n
    2. RNN(\uc21c\ud658 \uc2e0\uacbd\ub9dd)\uc758 \uae30\ucd08<\/h2>\n
    2.1 RNN\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\n
    RNN\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \ubaa8\ub378\ub85c, \uc774\uc804 \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. RNN\uc740 \uc785\ub825 \uc2dc\ud000\uc2a4\uc758 \uac01 \uc694\uc18c\ub97c \ucc98\ub9ac\ud560 \ub54c\ub9c8\ub2e4 hidden state\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uc5ec \ub2e4\uc74c \uc694\uc18c\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
    2.2 RNN\uc758 \ub3d9\uc791 \uc6d0\ub9ac<\/h3>\n
    RNN\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\ub3d9\ud569\ub2c8\ub2e4:<\/p>\n
      \n
    1. \uccab \ubc88\uc9f8 \uc785\ub825\uc744 \ubc1b\uc544 hidden state\ub97c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4.<\/li>\n
    2. \uac01 \uc785\ub825\uc774 \uc8fc\uc5b4\uc9c8 \ub54c\ub9c8\ub2e4, \uc785\ub825\uacfc \uc774\uc804 hidden state\ub97c \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 hidden state\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/li>\n
    3. \ubaa8\ub4e0 \uc2dc\ud000\uc2a4\uc5d0 \ub300\ud574 \ucd5c\uc885 hidden state\uc5d0\uc11c \uc608\uce21 \uacb0\uacfc\ub97c \uc5bb\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ol>\n
      2.3 PyTorch\ub85c RNN \uad6c\ud604\ud558\uae30<\/h3>\n
      RNN\uc744 PyTorch\ub85c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 RNN\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \uc124\uba85\ud558\ub294 \ucf54\ub4dc \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
      import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# RNN \ubaa8\ub378 \uc815\uc758\nclass RNNModel(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(RNNModel, self).__init__()\n        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n        self.fc = nn.Linear(hidden_size, output_size)\n\n    def forward(self, x):\n        rnn_out, _ = self.rnn(x)\n        out = self.fc(rnn_out[:, -1, :])  # \ub9c8\uc9c0\ub9c9 time step\uc758 \ucd9c\ub825\uc744 \uc0ac\uc6a9\n        return out\n\n# Hyperparameters\ninput_size = 1\nhidden_size = 128\noutput_size = 1\nnum_epochs = 100\nlearning_rate = 0.01\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131 (\uc608\uc2dc\ub85c \uac04\ub2e8\ud55c sin \ud568\uc218 \ub370\uc774\ud130)\ndata = torch.sin(torch.linspace(0, 20, steps=100)).reshape(-1, 1, 1)\nlabels = torch.sin(torch.linspace(0.1, 20.1, steps=100)).reshape(-1, 1)\n\n# \ub370\uc774\ud130\uc14b\uacfc \ub370\uc774\ud130\ub85c\ub354\ntrain_dataset = torch.utils.data.TensorDataset(data, labels)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=10, shuffle=True)\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \ucd08\uae30\ud654\nmodel = RNNModel(input_size, hidden_size, output_size)\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n# RNN \ud559\uc2b5\nfor epoch in range(num_epochs):\n    for inputs, target in train_loader:\n        optimizer.zero_grad()\n        outputs = model(inputs)\n        loss = criterion(outputs, target)\n        loss.backward()\n        optimizer.step()\n\n    if (epoch+1) % 10 == 0:\n        print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {loss.item():.4f}')\n\n# \uc608\uce21 \uacb0\uacfc \ubcf4\uae30 (\uc2e4\uc81c \ucf54\ub4dc\uc5d0\uc11c\ub294 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ud568\uc218 \ud544\uc694)\n<\/code><\/pre>\n
      3. GAN\uacfc RNN\uc758 \ud655\uc7a5<\/h2>\n
      3.1 GAN\uacfc RNN\uc758 \uc870\ud569<\/h3>\n
      GAN\uacfc RNN\uc744 \uc870\ud569\ud558\uc5ec \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub54c \uc2dc\uac04\uc801 \uc815\ubcf4\uac00 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uc0dd\uc131\uae30\ub294 RNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ud2b9\ud788 \uc74c\uc545 \uc0dd\uc131, \ud14d\uc2a4\ud2b8 \uc0dd\uc131 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
      3.2 GAN\uacfc RNN\uc744 \uacb0\ud569\ud55c \uc608\uc81c<\/h3>\n
      \ub2e4\uc74c\uc740 GAN\uacfc RNN\uc744 \uacb0\ud569\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud558\ub294 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\uc758 \uc608\uc2dc \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
      class RNNGenerator(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(RNNGenerator, self).__init__()\n        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n        self.fc = nn.Linear(hidden_size, output_size)\n\n    def forward(self, z):\n        rnn_out, _ = self.rnn(z)\n        return self.fc(rnn_out)\n\nclass RNNDiscriminator(nn.Module):\n    def __init__(self, input_size, hidden_size):\n        super(RNNDiscriminator, self).__init__()\n        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n        self.fc = nn.Linear(hidden_size, 1)\n\n    def forward(self, x):\n        rnn_out, _ = self.rnn(x)\n        return torch.sigmoid(self.fc(rnn_out[:, -1, :]))\n\n# Hyperparameters\ninput_size = 1\nhidden_size = 128\noutput_size = 1\n\n# \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30 \ucd08\uae30\ud654\ngenerator = RNNGenerator(input_size, hidden_size, output_size)\ndiscriminator = RNNDiscriminator(input_size, hidden_size)\n\n# GAN \ud559\uc2b5 \ucf54\ub4dc (\uc704\uc640 \ub3d9\uc77c\ud55c \ud328\ud134\uc73c\ub85c \uc801\uc6a9)\n# (\uc0dd\ub7b5)\n<\/code><\/pre>\n
      4. \uacb0\ub860<\/h2>\n
      GAN\uacfc RNN\uc740 \uac01\uac01 \ub9e4\uc6b0 \uac15\ub825\ud55c \ubaa8\ub378\uc774\uba70, \uc774\ub4e4\uc744 \uacb0\ud569\ud558\uc5ec \uc218\ud589\ud560 \uc218 \uc788\ub294 \uc791\uc5c5\uc758 \ubc94\uc704\uac00 \ub113\uc5b4\uc9d1\ub2c8\ub2e4. PyTorch\ub97c \uc0ac\uc6a9\ud558\uba74 \ucf54\ub4dc\uac00 \uac04\ud3b8\ud558\uace0 \uc9c1\uad00\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uc124\uacc4\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 GAN\uacfc RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud65c\uc6a9 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc73c\uba70, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ub354 \ub2e4\uc591\ud55c \uc751\uc6a9 \uc0ac\ub840\uc5d0 \ub3c4\uc804\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
      \ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \ub9e4\uc6b0 \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc0c8\ub85c\uc6b4 \uae30\uc220\uacfc \uc5f0\uad6c\uac00 \uafb8\uc900\ud788 \ubc1c\ud45c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ucd5c\uc2e0 \ud2b8\ub80c\ub4dc\uc640 \uc5f0\uad6c\uc5d0 \ub300\ud55c \uc9c0\uc18d\uc801\uc778 \uad00\uc2ec\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4.<\/p>\n<\/article>\n
      <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
      \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c GAN(Generative Adversarial Network)\uacfc RNN(Recurrent Neural Network)\uc740 \ub9ce\uc740 \uc8fc\ubaa9\uc744 \ubc1b\uc73c\uba70 \ubc1c\uc804\ud574\uc654\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uba70, RNN\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc801\ud569\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 PyTorch\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc RNN\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc124\uba85\ud558\uace0, \uc774 \ub450 \ubaa8\ub378\uc744 \uc5b4\ub5bb\uac8c \ud655\uc7a5\ud560 \uc218 \uc788\ub294\uc9c0 \uc608\uc81c\ub97c \ud1b5\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. 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