\ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8Generative Adversarial Networks (GANs)\uc640 Mixture Density Networks (MDN) \uac19\uc740 \ud601\uc2e0\uc801\uc778 \uc544\ud0a4\ud14d\ucc98\uac00 \uc5f0\uad6c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uc740 \uc0dd\uc131\ubaa8\ub378\ub85c\uc368, \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc0c8\ub85c\uc6b4 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc73c\uba70, MDN-RNN\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \ub370 \ucd5c\uc801\ud654\ub41c \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58 \ud504\ub808\uc784\uc6cc\ud06c\ub97c \ud65c\uc6a9\ud558\uc5ec GAN\uacfc MDN-RNN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
GAN\uc740 \uc0dd\uc131\uc790(generator)\uc640 \ud310\ubcc4\uc790(discriminator)\ub85c \uad6c\uc131\ub41c \ub450 \uac1c\uc758 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uc9c4\uc9dc\uc640 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \ub370\uc774\ud130\uac00 \uc9c4\uc9dc\uc778\uc9c0 \uc0dd\uc131\ub41c \uac83\uc778\uc9c0\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uc801\ub300\uc801 \ud6c8\ub828(adversarial training)\uc744 \ud1b5\ud574 \uc774\ub8e8\uc5b4\uc9c0\uba70, \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uacbd\uc7c1\ud558\uba74\uc11c \uac1c\uc120\ub429\ub2c8\ub2e4. GAN\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uba70, \ud2b9\ud788 \uc774\ubbf8\uc9c0 \uc0dd\uc131, \uc2a4\ud0c0\uc77c \uc804\uc774 \ub4f1\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\uacfc\ub97c \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
GAN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ubcf8 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ub2e4\uc74c\uc740 GAN\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc870\ub97c \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# \uc0dd\uc131\uc790 \ub124\ud2b8\uc6cc\ud06c\nclass Generator(nn.Module):\n def __init__(self, input_dim, output_dim):\n super(Generator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(input_dim, 128),\n nn.ReLU(),\n nn.Linear(128, output_dim),\n nn.Tanh()\n )\n\n def forward(self, x):\n return self.model(x)\n\n# \ud310\ubcc4\uc790 \ub124\ud2b8\uc6cc\ud06c\nclass Discriminator(nn.Module):\n def __init__(self, input_dim):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(input_dim, 128),\n nn.LeakyReLU(0.2),\n nn.Linear(128, 1),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n return self.model(x)\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\nlr = 0.0002\ninput_dim = 100 # \uc0dd\uc131\uc790 \uc785\ub825 \ud06c\uae30\noutput_dim = 784 # \uc608: MNIST\uc758 28x28=784\nnum_epochs = 200\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nG = Generator(input_dim, output_dim)\nD = Discriminator(output_dim)\n\n# \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\ncriterion = nn.BCELoss()\noptimizer_G = optim.Adam(G.parameters(), lr=lr)\noptimizer_D = optim.Adam(D.parameters(), lr=lr)\n\n# \ud6c8\ub828 \ub8e8\ud504\nfor epoch in range(num_epochs):\n # \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \ub77c\ubca8 \uc900\ube44\n real_data = torch.randn(128, output_dim) # \uc608\uc2dc \uc9c4\uc9dc \ub370\uc774\ud130\n real_labels = torch.ones(128, 1)\n\n # \uc0dd\uc131\uc790 \ud6c8\ub828\n optimizer_G.zero_grad()\n noise = torch.randn(128, input_dim)\n fake_data = G(noise)\n fake_labels = torch.zeros(128, 1)\n \n output = D(fake_data)\n loss_G = criterion(output, fake_labels)\n loss_G.backward()\n optimizer_G.step()\n\n # \ud310\ubcc4\uc790 \ud6c8\ub828\n optimizer_D.zero_grad()\n \n output_real = D(real_data)\n output_fake = D(fake_data.detach()) # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uae30\n loss_D_real = criterion(output_real, real_labels)\n loss_D_fake = criterion(output_fake, fake_labels)\n \n loss_D = loss_D_real + loss_D_fake\n loss_D.backward()\n optimizer_D.step()\n\n if epoch % 10 == 0:\n print(f'Epoch [{epoch}\/{num_epochs}], Loss D: {loss_D.item():.4f}, Loss G: {loss_G.item():.4f}')\n <\/code><\/pre>\n3. MDN-RNN (Mixture Density Networks – Recurrent Neural Networks)<\/h2>\n
MDN-RNN\uc740 Mixture Density Networks (MDN)\ub97c RNN\uacfc \uacb0\ud569\ud558\uc5ec, \uac01 \ud0c0\uc784 \uc2a4\ud15d\uc5d0\uc11c\uc758 \uc608\uce21 \ubd84\ud3ec\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. MDN\uc740 \uc5ec\ub7ec \uac1c\uc758 \uac00\uc6b0\uc2dc\uc548 \ubd84\ud3ec\ub97c \uc0ac\uc6a9\ud558\ub294 \ub124\ud2b8\uc6cc\ud06c\ub85c, \uc8fc\uc5b4\uc9c4 \uc785\ub825\uc5d0 \ub300\ud574 \uc5f0\uc18d\uc801\uc778 \ud655\ub960 \ubd84\ud3ec\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4. RNN\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc778 \uad6c\uc870\uc785\ub2c8\ub2e4.<\/p>\n
3.1 MDN-RNN\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h3>\n
MDN-RNN\uc740 \uc785\ub825 \uc2dc\ud000\uc2a4\uc5d0 \ub530\ub77c \ucd9c\ub825\uc758 \ud655\ub960 \ubd84\ud3ec\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \uc694\uc18c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n
\n- RNN:<\/strong> \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uc5ec \ub0b4\ubd80 \uc0c1\ud0dc\ub97c \uac31\uc2e0\ud569\ub2c8\ub2e4.<\/li>\n
- MDN:<\/strong> RNN\uc758 \ucd9c\ub825\uac12\uc744 \uae30\ubc18\uc73c\ub85c \ud63c\ud569 \uac00\uc6b0\uc2dc\uc548 \ubd84\ud3ec\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3.2 MDN-RNN\uc758 \ud30c\uc774\ud1a0\uce58 \uad6c\ud604<\/h3>\n
\ub2e4\uc74c\uc740 MDN-RNN\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
\ucf54\ub4dc \uc608\uc81c<\/h4>\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass MDN_RNN(nn.Module):\n def __init__(self, input_dim, hidden_dim, output_dim, num_mixtures):\n super(MDN_RNN, self).__init__()\n self.rnn = nn.GRU(input_dim, hidden_dim, batch_first=True)\n self.fc = nn.Linear(hidden_dim, num_mixtures * (output_dim + 2)) # \uac01 \ubd84\ud3ec\uc5d0 \ub300\ud55c \ud3c9\uade0, \ubd84\uc0b0, \uac00\uc911\uce58\n self.num_mixtures = num_mixtures\n self.output_dim = output_dim\n\n def forward(self, x):\n batch_size, seq_length, _ = x.size()\n h_0 = torch.zeros(1, batch_size, hidden_dim).to(x.device)\n rnn_out, _ = self.rnn(x, h_0)\n \n output = self.fc(rnn_out[:, -1, :]) # \ub9c8\uc9c0\ub9c9 \ud0c0\uc784 \uc2a4\ud15d\uc5d0\uc11c\uc758 \ucd9c\ub825\n output = output.view(batch_size, self.num_mixtures, -1)\n return output\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc124\uc815\ninput_dim = 1 \nhidden_dim = 64\noutput_dim = 1 \nnum_mixtures = 5 \nlr = 0.001\nnum_epochs = 100\n\nmodel = MDN_RNN(input_dim, hidden_dim, output_dim, num_mixtures)\noptimizer = optim.Adam(model.parameters(), lr=lr)\ncriterion = nn.MSELoss() # \uc190\uc2e4 \ud568\uc218 \uc124\uc815\n\n# \ud6c8\ub828 \ub8e8\ud504\nfor epoch in range(num_epochs):\n for series in train_loader: # train_loader\ub294 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub85c \uad6c\uc131\ub428\n optimizer.zero_grad()\n \n # \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \uc785\ub825\n input_seq = series[:, :-1, :].to(device)\n target = series[:, -1, :].to(device)\n \n # \ubaa8\ub378 \uc608\uce21\n output = model(input_seq)\n loss = criterion(output, target) # \uc190\uc2e4 \uacc4\uc0b0 (\ub2e8\uc21c \uc608\uc2dc)\n \n loss.backward()\n optimizer.step()\n \n print(f'Epoch [{epoch}\/{num_epochs}], Loss: {loss.item():.4f}')\n <\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \uc5ec\ub7ec \ubd84\uc57c\uc5d0 \ud070 \uc601\ud5a5\uc744 \ub07c\uce58\uace0 \uc788\uc2b5\ub2c8\ub2e4. GAN\uacfc MDN-RNN\uc740 \uac01\uac01\uc758 \ud2b9\uc131\uc73c\ub85c \uc778\ud574 \ub2e4\uc591\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc744 \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc740 \ubcf5\uc7a1\ud558\uc9c0\ub9cc, \uc774 \uae00\uc5d0\uc11c \uc81c\uacf5\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc774 \uc27d\uac8c \uc774\ud574\ud558\uace0 \ud65c\uc6a9\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 GAN\uacfc MDN-RNN\uc744 \uc774\uc6a9\ud55c \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \ud0d0\uad6c\ud558\uace0 \uc5f0\uad6c\ud574 \ubcf4\uae30\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\ub4e4\uc740 \uc608\uc220, \uae08\uc735, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub354\uc6b1 \ubc1c\uc804\ud560 \uac83\uc73c\ub85c \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
5. \ucd94\uac00 \uc790\ub8cc<\/h2>\n
\ub354 \uae4a\uc740 \uc774\ud574\ub97c \uc6d0\ud558\uc2e0\ub2e4\uba74 \ub2e4\uc74c \uc790\ub8cc\ub97c \ucc38\uace0\ud558\uc138\uc694:<\/p>\n