4\ub2e8\uacc4: \uc774 \uacfc\uc815\uc740 \ubc18\ubcf5\ub418\uba70, \uc0dd\uc131\uc790\ub294 \uc810\uc810 \ub354 \ud604\uc2e4\uc801\uc778 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uac8c \ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n1.2 GAN\uc758 \ud30c\uc774\ud1a0\uce58 \uad6c\ud604<\/h3>\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. \ub2e4\uc74c \ucf54\ub4dc\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 GAN\uc758 \uc608\uc785\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nbatch_size = 64\nlearning_rate = 0.0002\nnum_epochs = 50\nlatent_size = 100\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\nmnist = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndata_loader = DataLoader(mnist, batch_size=batch_size, shuffle=True)\n\n# \uc0dd\uc131\uc790 \uc815\uc758\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(latent_size, 128),\n nn.ReLU(),\n nn.Linear(128, 256),\n nn.ReLU(),\n nn.Linear(256, 512),\n nn.ReLU(),\n nn.Linear(512, 784),\n nn.Tanh() # \ucd9c\ub825\uac12\uc740 -1\uc5d0\uc11c 1 \uc0ac\uc774\n )\n \n def forward(self, z):\n return self.model(z).view(-1, 1, 28, 28)\n\n# \ud310\ubcc4\uc790 \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.LeakyReLU(0.2),\n nn.Linear(512, 256),\n nn.LeakyReLU(0.2),\n nn.Linear(256, 1),\n nn.Sigmoid() # \ucd9c\ub825\uac12\uc740 0\uc5d0\uc11c 1 \uc0ac\uc774\n )\n \n def forward(self, img):\n return self.model(img.view(-1, 784))\n\n# \ubaa8\ub378, \uc190\uc2e4 \ud568\uc218, \uc635\ud2f0\ub9c8\uc774\uc800 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\nloss_function = nn.BCELoss()\noptimizer_g = optim.Adam(generator.parameters(), lr=learning_rate)\noptimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate)\n\n# GAN \ud559\uc2b5\nfor epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(data_loader):\n # \uc9c4\uc9dc \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub808\uc774\ube14\n real_labels = torch.ones(imgs.size(0), 1)\n # \uac00\uc9dc \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ub808\uc774\ube14\n z = torch.randn(imgs.size(0), latent_size)\n fake_images = generator(z)\n fake_labels = torch.zeros(imgs.size(0), 1)\n\n # \ud310\ubcc4\uc790 \ud559\uc2b5\n optimizer_d.zero_grad()\n outputs_real = discriminator(imgs)\n loss_real = loss_function(outputs_real, real_labels)\n outputs_fake = discriminator(fake_images.detach())\n loss_fake = loss_function(outputs_fake, fake_labels)\n loss_d = loss_real + loss_fake\n loss_d.backward()\n optimizer_d.step()\n\n # \uc0dd\uc131\uc790 \ud559\uc2b5\n optimizer_g.zero_grad()\n outputs_fake = discriminator(fake_images)\n loss_g = loss_function(outputs_fake, real_labels)\n loss_g.backward()\n optimizer_g.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss D: {loss_d.item():.4f}, Loss G: {loss_g.item():.4f}')<\/code>\n<\/pre>\n\uc704 \ucf54\ub4dc\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub370\uc774\ud130 \ub85c\ub4dc\ub97c \uc704\ud574 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uace0, Generator\uc640 Discriminator\ub97c \uac01\uac01 \ud074\ub798\uc2a4\ub85c \uc815\uc758\ud558\uc600\uc2b5\ub2c8\ub2e4. \uadf8\ub7f0 \ub2e4\uc74c, \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ucd08\uae30\ud654\ud558\uace0, \ud559\uc2b5 \uacfc\uc815\uc744 \ubc18\ubcf5\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n
2. Long Short-Term Memory (LSTM) \ub124\ud2b8\uc6cc\ud06c<\/h2>\n
LSTM\uc740 RNN(Recurrent Neural Network)\uc758 \ud55c \uc885\ub958\ub85c, \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. LSTM\uc740 \uc7a5\uae30 \uc758\uc874\uc131 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uace0\uc548\ub41c \ub124\ud2b8\uc6cc\ud06c\ub85c, \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c\ub294 \uc785\ub825 \uac8c\uc774\ud2b8, \ub9dd\uac01 \uac8c\uc774\ud2b8, \ucd9c\ub825 \uac8c\uc774\ud2b8 \ub4f1\uc758 \uba54\ucee4\ub2c8\uc998\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1 LSTM\uc758 \uc6d0\ub9ac<\/h3>\n
LSTM\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\ub97c \uac00\uc9d1\ub2c8\ub2e4:<\/p>\n
\n- \uc785\ub825 \uac8c\uc774\ud2b8: \uc0c8\ub85c\uc6b4 \uc815\ubcf4\ub97c \uc140 \uc0c1\ud0dc\uc5d0 \ucd94\uac00\ud558\ub294 \uc815\ub3c4\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n
- \ub9dd\uac01 \uac8c\uc774\ud2b8: \uc140 \uc0c1\ud0dc\uc5d0\uc11c \uc774\uc804 \uc815\ubcf4\ub97c \uc5bc\ub9c8\ub098 \uc720\uc9c0\ud560\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n
- \ucd9c\ub825 \uac8c\uc774\ud2b8: \uc140 \uc0c1\ud0dc\uc5d0\uc11c \uc5bc\ub9c8\ub098 \ub9ce\uc740 \uc815\ubcf4\ub97c \ucd9c\ub825\ud560\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
\uc774\ub7ec\ud55c \uad6c\uc131 \ub355\ubd84\uc5d0 LSTM\uc740 \uae34 \uc2dc\ud000\uc2a4\uc5d0\uc11c\ub3c4 \uc815\ubcf4\ub97c \uc783\uc9c0 \uc54a\uace0 \uc815\ud655\ud558\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.2 LSTM\uc758 \ud30c\uc774\ud1a0\uce58 \uad6c\ud604<\/h3>\n
\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c LSTM \uc608\uc81c\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. LSTM\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc5b4\uc9c4 \uc2dc\ud000\uc2a4\uc758 \ub2e4\uc74c \uac12\uc744 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport numpy as np\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\ninput_size = 1 # \uc785\ub825 \ud06c\uae30\nhidden_size = 10 # LSTM\uc758 \uc740\ub2c9\uce35 \ud06c\uae30\nnum_layers = 1 # LSTM \uce35\uc218\nnum_epochs = 100\nlearning_rate = 0.01\n\n# LSTM \uc815\uc758\nclass LSTM(nn.Module):\n def __init__(self):\n super(LSTM, self).__init__()\n self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n self.fc = nn.Linear(hidden_size, 1) # \ucd9c\ub825 \ud06c\uae30 1\n\n def forward(self, x):\n out, (h_n, c_n) = self.lstm(x)\n out = self.fc(out[:, -1, :]) # \ub9c8\uc9c0\ub9c9 \uc2dc\uac04\uc2a4\ud15d\uc758 \ucd9c\ub825\uac12\n return out\n\n# \ub370\uc774\ud130 \uc0dd\uc131\ndef create_data(seq_length=10):\n x = np.arange(0, seq_length + 10, 0.1)\n y = np.sin(x)\n return x[:-10].reshape(-1, seq_length, 1), y[10:].reshape(-1, 1)\n\nx_train, y_train = create_data()\n\n# \ub370\uc774\ud130 \ud150\uc11c\ub85c \ubcc0\ud658\nx_train_tensor = torch.Tensor(x_train)\ny_train_tensor = torch.Tensor(y_train)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = LSTM()\ncriterion = nn.MSELoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n# LSTM \ud559\uc2b5\nfor epoch in range(num_epochs):\n model.train()\n optimizer.zero_grad()\n outputs = model(x_train_tensor)\n loss = criterion(outputs, y_train_tensor)\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}')<\/code>\n<\/pre>\n\uc704 \ucf54\ub4dc\ub294 LSTM \ubaa8\ub378\uc744 \uad6c\ud604\ud55c \uac83\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \uc0ac\uc778 \ud568\uc218\ub85c \uc0dd\uc131\ub418\uba70, LSTM \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uc5ec \ub2e4\uc74c \uac12\uc744 \uc608\uce21\ud558\ub3c4\ub85d \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc5d0\ud3ec\ud06c\ub9c8\ub2e4 \uc190\uc2e4 \uac12\uc744 \ucd9c\ub825\ud558\uc5ec \ud559\uc2b5 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud569\ub2c8\ub2e4.<\/p>\n
3. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 GAN\uacfc LSTM \ub124\ud2b8\uc6cc\ud06c\uc758 \uae30\ubcf8 \uac1c\ub150 \ubc0f \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc0dd\uc131\uc5d0, LSTM\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \ud6a8\uc728\uc801\uc785\ub2c8\ub2e4. \uc774 \ub450 \uae30\ubc95\uc740 \uac01\uac01\uc758 \ud2b9\uc131\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc73c\uba70, \ub354 \ub098\uc544\uac00 \ubcf5\uc7a1\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \uc2e4\ud5d8\uacfc \uc5f0\uad6c\ub97c \ud1b5\ud574 \uc774 \uae30\uc220\ub4e4\uc744 \uae4a\uc774 \uc788\uac8c \ud0d0\uad6c\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \uae30\uacc4\uac00 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uace0 \uadf8 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \uc778\uc2dd\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub450 \uac00\uc9c0 \uc911\uc694\ud55c \ub525\ub7ec\ub2dd \uae30\uc220\uc778 GAN(Generative Adversarial Network)\uacfc LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\uc5d0 \ub300\ud574 \uc18c\uac1c\ud558\uace0, \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uc81c \ucf54\ub4dc\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. Generative Adversarial Network (GAN) GAN\uc740 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub77c\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \uc0dd\uc131\uc790\uac00 \uc2e4\uc81c\uc640 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub3c4\ub85d \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac83\uc785\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, LSTM \ub124\ud2b8\uc6cc\ud06c \uc18c\uac1c”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32],"tags":[],"class_list":["post-29796","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"\n\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, LSTM \ub124\ud2b8\uc6cc\ud06c \uc18c\uac1c - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29796\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, LSTM \ub124\ud2b8\uc6cc\ud06c \uc18c\uac1c - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \uae30\uacc4\uac00 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uace0 \uadf8 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \uc778\uc2dd\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub450 \uac00\uc9c0 \uc911\uc694\ud55c \ub525\ub7ec\ub2dd \uae30\uc220\uc778 GAN(Generative Adversarial Network)\uacfc LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\uc5d0 \ub300\ud574 \uc18c\uac1c\ud558\uace0, \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uc81c \ucf54\ub4dc\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. Generative Adversarial Network (GAN) GAN\uc740 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub77c\ub294 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. 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