pip install torch torchvision torchaudio numpy matplotlib<\/code><\/pre>\n4.2. \uae30\ubcf8 \ub370\uc774\ud130\uc14b \uc124\uc815<\/h3>\n
MuseGAN\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 MIDI \ud30c\uc77c\uc744 \uc0ac\uc6a9\ud560 \uc608\uc815\uc785\ub2c8\ub2e4. MIDI \ub370\uc774\ud130\ub97c \ud30c\uc774\uc36c\uc73c\ub85c \ucc98\ub9ac\ud558\uae30 \uc704\ud574 mido<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\npip install mido<\/code><\/pre>\n4.3. \ub370\uc774\ud130 \ub85c\ub529<\/h3>\n
\uc774\uc81c MIDI \ub370\uc774\ud130\ub97c \ub85c\ub529\ud558\uace0 \uc804\ucc98\ub9ac\ud558\ub294 \ud568\uc218\ub97c \uc124\uc815\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 MIDI \ud30c\uc77c\uc744 \ub85c\ub4dc\ud558\uace0 \uac01 \uc74c\ud45c\ub97c \ucd94\ucd9c\ud569\ub2c8\ub2e4.<\/p>\n
import mido\nimport numpy as np\n\ndef load_midi(file_path):\n mid = mido.MidiFile(file_path)\n notes = []\n for message in mid.play():\n if message.type == 'note_on':\n notes.append(message.note)\n return np.array(notes)\n<\/code><\/pre>\n4.4. \uc0dd\uc131\uc790 \uc815\uc758\ud558\uae30<\/h3>\n
\uc774\uc81c \uc0dd\uc131\uc790\ub97c \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub79c\ub364 \ub178\uc774\uc988\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uc74c\uc545\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.layer1 = nn.Linear(100, 256)\n self.layer2 = nn.Linear(256, 512)\n self.layer3 = nn.Linear(512, 1024)\n self.layer4 = nn.Linear(1024, 88) # 88\uc740 \ud53c\uc544\ub178 \ud0a4 \uc218\n\n def forward(self, z):\n z = torch.relu(self.layer1(z))\n z = torch.relu(self.layer2(z))\n z = torch.relu(self.layer3(z))\n return torch.tanh(self.layer4(z)) # -1\uc5d0\uc11c 1 \uc0ac\uc774\uc758 \uac12 \ubc18\ud658\n<\/code><\/pre>\n4.5. \ud310\ubcc4\uc790 \uc815\uc758\ud558\uae30<\/h3>\n
\ud310\ubcc4\uc790\ub3c4 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud310\ubcc4\uc790\ub294 \uc785\ub825\ub41c \uc74c\uc545 \uc2e0\ud638\uac00 \uc2e4\uc81c\uc778\uc9c0 \uc0dd\uc131\ub41c \uc2e0\ud638\uc778\uc9c0\ub97c \uad6c\ubcc4\ud569\ub2c8\ub2e4.<\/p>\n
class Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.layer1 = nn.Linear(88, 1024) # 88\uc740 \ud53c\uc544\ub178 \ud0a4 \uc218\n self.layer2 = nn.Linear(1024, 512)\n self.layer3 = nn.Linear(512, 256)\n self.layer4 = nn.Linear(256, 1) # \uc774\uc9c4 \ubd84\ub958\n\n def forward(self, x):\n x = torch.relu(self.layer1(x))\n x = torch.relu(self.layer2(x))\n x = torch.relu(self.layer3(x))\n return torch.sigmoid(self.layer4(x)) # 0\uc5d0\uc11c 1 \uc0ac\uc774\uc758 \ud655\ub960 \ubc18\ud658\n<\/code><\/pre>\n4.6. GAN \ud559\uc2b5 \ub8e8\ud504<\/h3>\n
\uc774\uc81c GAN\uc744 \ud559\uc2b5\ud558\ub294 \uba54\uc778 \ub8e8\ud504\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub97c \ubc88\uac08\uc544\uac00\uba74\uc11c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
def train_gan(generator, discriminator, data_loader, num_epochs=100):\n criterion = nn.BCELoss()\n optimizer_g = torch.optim.Adam(generator.parameters(), lr=0.0002)\n optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.0002)\n\n for epoch in range(num_epochs):\n for real_data in data_loader:\n batch_size = real_data.size(0)\n\n # \uc9c4\uc9dc \ub370\uc774\ud130\uc640 \uac00\uc9dc \ub370\uc774\ud130 \ub77c\ubca8 \uc0dd\uc131\n real_labels = torch.ones(batch_size, 1)\n fake_labels = torch.zeros(batch_size, 1)\n\n # \ud310\ubcc4\uc790 \ud559\uc2b5\n optimizer_d.zero_grad()\n outputs = discriminator(real_data)\n d_loss_real = criterion(outputs, real_labels)\n d_loss_real.backward()\n\n z = torch.randn(batch_size, 100)\n fake_data = generator(z)\n outputs = discriminator(fake_data.detach())\n d_loss_fake = criterion(outputs, fake_labels)\n d_loss_fake.backward()\n\n optimizer_d.step()\n\n # \uc0dd\uc131\uc790 \ud559\uc2b5\n optimizer_g.zero_grad()\n outputs = discriminator(fake_data)\n g_loss = criterion(outputs, real_labels)\n g_loss.backward()\n optimizer_g.step()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], d_loss: {d_loss_real.item() + d_loss_fake.item()}, g_loss: {g_loss.item()}')\n<\/code><\/pre>\n5. \ud30c\uc774\ud1a0\uce58 \ubaa8\ub378 \uc800\uc7a5 \ubc0f \ubd88\ub7ec\uc624\uae30<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ub098\uc911\uc5d0 \uc7ac\uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
# \ubaa8\ub378 \uc800\uc7a5\ntorch.save(generator.state_dict(), 'generator.pth')\ntorch.save(discriminator.state_dict(), 'discriminator.pth')\n\n# \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\ngenerator.load_state_dict(torch.load('generator.pth'))\ndiscriminator.load_state_dict(torch.load('discriminator.pth'))\n<\/code><\/pre>\n6. MuseGAN \uacb0\uacfc \uc0dd\uc131<\/h2>\n
\uc774\uc81c \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c GAN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc74c\uc545\uc744 \uc0dd\uc131\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
def generate_music(generator, num_samples=5):\n generator.eval()\n with torch.no_grad():\n for _ in range(num_samples):\n z = torch.randn(1, 100)\n generated_music = generator(z)\n print(generated_music.numpy())\n<\/code><\/pre>\n6.1. \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\uc0dd\uc131\ub41c \uc74c\uc545\uc744 \uc2dc\uac01\ud654\ud558\uc5ec \ubd84\uc11d\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub97c \uadf8\ub798\ud504\ub85c \ub098\ud0c0\ub0b4\uac70\ub098 MIDI\ub85c \ubcc0\ud658\ud558\uc5ec \uc2e4\ud589\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\n\ndef plot_generated_music(music):\n plt.figure(figsize=(10, 4))\n plt.plot(music.numpy().flatten())\n plt.xlabel('Time Steps')\n plt.ylabel('Amplitude')\n plt.title('Generated Music')\n plt.show()\n<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
MuseGAN\uc744 \uc0ac\uc6a9\ud558\uba74 \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc790\ub3d9\uc73c\ub85c \uc74c\uc545\uc744 \uc0dd\uc131\ud558\ub294 \uac83\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4. \uc774\uc640 \uac19\uc740 GAN \uae30\ubc18 \ubaa8\ub378\ub4e4\uc740 \ub2e4\uc591\ud55c \uc74c\uc545 \uc2a4\ud0c0\uc77c\uacfc \uad6c\uc870\ub97c \ud559\uc2b5\ud558\uc5ec \uace0\uc720\ud55c \uc74c\uc545\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4. \ud5a5\ud6c4 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \uad6c\uc870\uc640 \ub2e4\uc591\ud55c \uc694\uc18c\ub97c \uacb0\ud569\ud558\uc5ec \ub354 \uace0\ud488\uc9c8\uc758 \uc74c\uc545 \uc0dd\uc131\uc774 \uac00\ub2a5\ud574\uc9c8 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\n
\uc8fc\uc758:<\/strong> \ubcf8 \uc544\ud2f0\ud074\uc5d0\uc11c\ub294 MuseGAN\uc758 \uae30\ubcf8 \uad6c\uc870\uc640 \ubc29\ubc95\ub860\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud55c \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub294 \ub354 \ub9ce\uc740 \uad6c\uc131 \uc694\uc18c\uc640 \ubcf5\uc7a1\ub3c4\uac00 \ucd94\uac00\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc74c\uc545\uc801 \ub370\uc774\ud130\uc14b\uacfc \uc870\uac74\uc744 \uc774\uc6a9\ud558\uc5ec MuseGAN\uc744 \ud655\uc7a5\ud574\ubcf4\uc2ed\uc2dc\uc624.\n<\/div>\n\n\uc774 \ube14\ub85c\uadf8 \ud3ec\uc2a4\ud2b8\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c MuseGAN\uc758 \uae30\ubcf8\uc801\uc778 \uc774\ud574\uc640 \uad6c\ud604 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uc600\uc2b5\ub2c8\ub2e4. \ub354 \uae4a\uc740 \ud559\uc2b5\uc774 \ud544\uc694\ud558\ub2e4\uba74 \uad00\ub828 \ub17c\ubb38\uc744 \ucc38\uace0\ud558\uac70\ub098 \ub354 \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc2a4\uc2a4\ub85c \uc2e4\ud5d8\ud574 \ubcf4\uc2dc\uae38 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
Generative Adversarial Networks (GANs)\ub780 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc774 \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \ub370\uc774\ud130 \ubd84\ud3ec\ub97c \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ucd5c\uadfc\uc5d0\ub294 GAN\uc744 \ud65c\uc6a9\ud55c \ub2e4\uc591\ud55c \uc751\uc6a9\uc774 \ub098\ud0c0\ub098\uace0 \uc788\uc73c\uba70 \uadf8 \uc911\uc5d0\uc11c\ub3c4 MuseGAN\uc740 \uc74c\uc545 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 MuseGAN\uc758 \uac1c\ub150, \uad6c\uc870, \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. MuseGAN\uc758 \uac1c\uc694 MuseGAN\uc740 \uc74c\uc545 \uc0dd\uc131\uc5d0 \ud2b9\ud654\ub41c GAN\uc73c\ub85c, \ud2b9\ud788 \ub2e4\uce35\uc801\uc778 … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uccab \ubc88\uc9f8 MuseGAN”<\/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-29869","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, \uccab \ubc88\uc9f8 MuseGAN - \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\/29869\/\" \/>\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, \uccab \ubc88\uc9f8 MuseGAN - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"Generative Adversarial Networks (GANs)\ub780 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd\uc774 \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. GAN\uc758 \ubaa9\ud45c\ub294 \ub370\uc774\ud130 \ubd84\ud3ec\ub97c \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ucd5c\uadfc\uc5d0\ub294 GAN\uc744 \ud65c\uc6a9\ud55c \ub2e4\uc591\ud55c \uc751\uc6a9\uc774 \ub098\ud0c0\ub098\uace0 \uc788\uc73c\uba70 \uadf8 \uc911\uc5d0\uc11c\ub3c4 MuseGAN\uc740 \uc74c\uc545 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 MuseGAN\uc758 \uac1c\ub150, \uad6c\uc870, \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. 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