\ub525\ub7ec\ub2dd\uc740 \ud604\uc7ac \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \uac00\uc7a5 \uc8fc\ubaa9\ubc1b\ub294 \uae30\uc220 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uba70, \ud2b9\ud788 GAN(Generative Adversarial Network)\uacfc LSTM(Long Short-Term Memory)\uc740 \uac01\uac01 \ub370\uc774\ud130 \uc0dd\uc131\uacfc \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0\uc11c \ub450\ub4dc\ub7ec\uc9c4 \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc LSTM\uc744 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
GAN\uc740 2014\ub144 Ian Goodfellow\uc640 \uadf8\uc758 \ub3d9\ub8cc\ub4e4\uc5d0 \uc758\ud574 \uc81c\uc548\ub41c \uc0dd\uc131 \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd(Generator\uc640 Discriminator)\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. Generator\ub294 \ub79c\ub364 \ub178\uc774\uc988\ub85c\ubd80\ud130 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, Discriminator\ub294 \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uad6c\ubd84\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n
\uc774 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
LSTM\uc740 RNN(Recurrent Neural Network)\uc758 \ud55c \uc885\ub958\ub85c, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub098 \uc21c\ucc28\uc801\uc778 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uac15\uc810\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. LSTM \uc140\uc740 \uae30\uc5b5 \uc140\uc744 \uac00\uc9c0\uace0 \uc788\uc5b4 \uacfc\uac70\uc758 \uc815\ubcf4\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uae30\uc5b5\ud558\uace0 \uc78a\uc5b4\ubc84\ub9ac\ub294 \uacfc\uc815\uc744 \uc870\uc808\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \uae34 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
LSTM\uc758 \uae30\ubcf8 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\ud30c\uc774\ud1a0\uce58\ub294 \ud398\uc774\uc2a4\ubd81\uc5d0\uc11c \uac1c\ubc1c\ub41c \uc624\ud508\uc18c\uc2a4 \uba38\uc2e0 \ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc9c0\uc6d0\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uad6c\uc131\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. \ub610\ud55c, \ub2e4\uc591\ud55c \ucef4\ud4e8\ud130 \ube44\uc804, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud30c\uc774\ud1a0\uce58\uc640 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4. \uc544\ub798\uc640 \uac19\uc774 pip\ub97c \ud1b5\ud574 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install torch torchvision<\/code><\/pre>\n4.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc744 \uc608\ub85c \ub4e4\uc5b4 \uc190\uae00\uc528 \uc22b\uc790\ub97c \uc0dd\uc131\ud558\ub294 GAN\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\nmnist = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ndataloader = DataLoader(mnist, batch_size=64, shuffle=True)\n <\/code><\/pre>\n4.3 Generator \ubc0f Discriminator \uc815\uc758<\/h3>\n
Generator\uc640 Discriminator\ub294 \uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\ud604\ub429\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc774 \uac01 \ubaa8\ub378\uc744 \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch.nn as nn\n\n# Generator \ubaa8\ub378\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(),\n nn.Linear(256, 512),\n nn.ReLU(),\n nn.Linear(512, 1024),\n nn.ReLU(),\n nn.Linear(1024, 28*28),\n nn.Tanh()\n )\n\n def forward(self, z):\n return self.model(z).view(-1, 1, 28, 28) # \uc774\ubbf8\uc9c0 \ud615\ud0dc\ub85c \ubcc0\ud658\n\n# Discriminator \ubaa8\ub378\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Flatten(),\n nn.Linear(28*28, 512),\n nn.LeakyReLU(0.2),\n nn.Linear(512, 256),\n nn.LeakyReLU(0.2),\n nn.Linear(256, 1),\n nn.Sigmoid()\n )\n\n def forward(self, img):\n return self.model(img)\n <\/code><\/pre>\n4.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815<\/h3>\n
\uc190\uc2e4 \ud568\uc218\ub294 Binary Cross Entropy\ub97c \uc0ac\uc6a9\ud558\uba70, \uc635\ud2f0\ub9c8\uc774\uc800\ub294 Adam\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ngenerator = Generator()\ndiscriminator = Discriminator()\n\n# \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\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 <\/code><\/pre>\n4.5 GAN \ud6c8\ub828 Loop<\/h3>\n
\uc774\uc81c GAN\uc758 \ud6c8\ub828\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Generator\ub294 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, Discriminator\ub294 \uc774\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4.<\/p>\n
\nnum_epochs = 50\nfor epoch in range(num_epochs):\n for i, (imgs, _) in enumerate(dataloader):\n # \uc9c4\uc9dc \ub370\uc774\ud130\ub97c \uc704\ud55c \ub77c\ubca8\uacfc \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc704\ud55c \ub77c\ubca8 \uc0dd\uc131\n real_labels = torch.ones(imgs.size(0), 1)\n fake_labels = torch.zeros(imgs.size(0), 1)\n\n # Discriminator \ud6c8\ub828\n optimizer_D.zero_grad()\n outputs = discriminator(imgs)\n d_loss_real = criterion(outputs, real_labels)\n\n z = torch.randn(imgs.size(0), 100)\n fake_imgs = generator(z)\n outputs = discriminator(fake_imgs.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 # Generator \ud6c8\ub828\n optimizer_G.zero_grad()\n outputs = discriminator(fake_imgs)\n g_loss = criterion(outputs, real_labels)\n\n g_loss.backward()\n optimizer_G.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], d_loss: {d_loss.item():.4f}, g_loss: {g_loss.item():.4f}')\n <\/code><\/pre>\n4.6 \uc0dd\uc131\ub41c \uc774\ubbf8\uc9c0 \uc2dc\uac01\ud654<\/h3>\n
\ud6c8\ub828\uc774 \ub05d\ub09c \ud6c4 Generator\uac00 \uc0dd\uc131\ud55c \uc774\ubbf8\uc9c0\ub4e4\uc744 \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/p>\n
\nimport matplotlib.pyplot as plt\n\n# Generator \ubaa8\ub378\uc744 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \ubcc0\uacbd\ngenerator.eval()\nz = torch.randn(64, 100)\nfake_imgs = generator(z).detach().numpy()\n\n# \uc774\ubbf8\uc9c0 \ucd9c\ub825\nplt.figure(figsize=(8, 8))\nfor i in range(64):\n plt.subplot(8, 8, i + 1)\n plt.imshow(fake_imgs[i][0], cmap='gray')\n plt.axis('off')\nplt.show()\n <\/code><\/pre>\n5. LSTM \ub124\ud2b8\uc6cc\ud06c \uad6c\ud604<\/h2>\n5.1 LSTM\uc744 \ud65c\uc6a9\ud55c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21<\/h3>\n
LSTM\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21\uc5d0\uc11c\ub3c4 \ub9e4\uc6b0 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uac04\ub2e8\ud55c LSTM \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uc5ec sin \ud568\uc218\uc758 \uac12\uc744 \uc608\uce21\ud558\ub294 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
sin \ud568\uc218 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0 \uc774\ub97c LSTM \ubaa8\ub378\uc5d0 \ub9de\uac8c \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n
\nimport numpy as np\n\n# \ub370\uc774\ud130 \uc0dd\uc131\ntime = np.arange(0, 100, 0.1)\ndata = np.sin(time)\n\n# LSTM \uc785\ub825\uc5d0 \ub9de\uac8c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ndef create_sequences(data, seq_length):\n sequences = []\n labels = []\n for i in range(len(data) - seq_length):\n sequences.append(data[i:i+seq_length])\n labels.append(data[i+seq_length])\n return np.array(sequences), np.array(labels)\n\nseq_length = 10\nX, y = create_sequences(data, seq_length)\nX = X.reshape((X.shape[0], X.shape[1], 1))\n <\/code><\/pre>\n5.3 LSTM \ubaa8\ub378 \uc815\uc758<\/h3>\n
\uc774\uc81c LSTM \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
\nclass LSTMModel(nn.Module):\n def __init__(self):\n super(LSTMModel, self).__init__()\n self.lstm = nn.LSTM(input_size=1, hidden_size=50, num_layers=2, batch_first=True)\n self.fc = nn.Linear(50, 1)\n \n def forward(self, x):\n out, (hn, cn) = self.lstm(x)\n out = self.fc(hn[-1])\n return out\n <\/code><\/pre>\n5.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815<\/h3>\n\nmodel = LSTMModel()\ncriterion = nn.MSELoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n <\/code><\/pre>\n5.5 LSTM \ud6c8\ub828 Loop<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\nnum_epochs = 100\nfor epoch in range(num_epochs):\n model.train()\n optimizer.zero_grad()\n output = model(torch.FloatTensor(X))\n loss = criterion(output, torch.FloatTensor(y).unsqueeze(1))\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 <\/code><\/pre>\n5.6 \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/p>\n
\nimport matplotlib.pyplot as plt\n\n# \uc608\uce21\nmodel.eval()\npredictions = model(torch.FloatTensor(X)).detach().numpy()\n\n# \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(data, label='Real Data')\nplt.plot(np.arange(seq_length, seq_length + len(predictions)), predictions, label='Predicted Data', color='red')\nplt.legend()\nplt.show()\n <\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 GAN\uacfc LSTM\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0dd\uc131 \ubaa8\ub378\ub85c\uc11c \uc774\ubbf8\uc9c0\uc640 \uac19\uc740 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ud65c\uc6a9\ub418\uba70, LSTM\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21 \ubaa8\ub378\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub450 \uae30\uc220 \ubaa8\ub450 \uac01\uac01\uc758 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud558\uba70, \ud30c\uc774\ud1a0\uce58\ub97c \ud1b5\ud574 \uc190\uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub354 \ub098\uc544\uac00 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubc29\ubc95\uc744 \ud0d0\uc0c9\ud558\uace0, \uc790\uc2e0\ub9cc\uc758 \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc801\uc6a9\ud574\ubcf4\uc2dc\uae38 \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n
7. \ucc38\uace0 \uc790\ub8cc<\/h2>\n
\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c \ub2e4\ub8ec \uc8fc\uc81c\uc5d0 \ub300\ud55c \ub354 \uae4a\uc740 \uc774\ud574\ub97c \uc704\ud574 \uc544\ub798\uc758 \uc790\ub8cc\ub4e4\uc744 \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n