\ubaa8\ub378\uc758 \uc131\ub2a5 \ud5a5\uc0c1 \uac00\ub2a5\uc131\uc744 \ud3c9\uac00<\/li>\n<\/ul>\n\uc774\ub7ec\ud55c \uc774\uc720\ub85c \ud6c8\ub828 \uacfc\uc815\uc744 \uc2dc\uac01\ud654\ud558\uace0 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uac83\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n
2. \ud30c\uc774\ud1a0\uce58 \uc124\uce58<\/h2>\n
\uc6b0\uc120, \ud30c\uc774\ud1a0\uce58\uac00 \uc124\uce58\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc758 \uba85\ub839\uc5b4\ub85c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install torch torchvision<\/code><\/pre>\n3. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h2>\n
\uc5ec\uae30\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc22b\uc790\ub97c \ubd84\ub958\ud558\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \ud6c8\ub828 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc8fc\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\uc758 torchvision \ud328\ud0a4\uc9c0\ub97c \ud1b5\ud574 MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# \ud6c8\ub828 \ub370\uc774\ud130\uc14b\ntrainset = torchvision.datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n\n# \uac80\uc99d \ub370\uc774\ud130\uc14b\ntestset = torchvision.datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n<\/code><\/pre>\n4. \ubaa8\ub378 \uc815\uc758<\/h2>\n
\ub2e4\uc74c\uc73c\ub85c, \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP) \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.nn as nn\nimport torch.nn.functional as F\n\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.fc1 = nn.Linear(28 * 28, 128)\n self.fc2 = nn.Linear(128, 64)\n self.fc3 = nn.Linear(64, 10)\n\n def forward(self, x):\n x = x.view(-1, 28 * 28) # \ud3c9\ud0c4\ud654\n x = F.relu(self.fc1(x)) # \uccab\ubc88\uc9f8 \uce35\n x = F.relu(self.fc2(x)) # \ub450\ubc88\uc9f8 \uce35\n x = self.fc3(x) # \ucd9c\ub825\uce35\n return x\n\n# \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4 \uc0dd\uc131\nmodel = Net()\n<\/code><\/pre>\n5. \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998<\/h2>\n
\uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4 \ud568\uc218\uc640 Adam \ucd5c\uc801\ud654\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n<\/code><\/pre>\n6. \ud6c8\ub828 \uacfc\uc815 \uc124\uc815<\/h2>\n
\ud6c8\ub828 \uacfc\uc815\uc744 \uc124\uc815\ud558\uace0 \ubaa8\ub2c8\ud130\ub9c1\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. \ub9e4 \uc5d0\ud3ec\ud06c\ub9c8\ub2e4 \uc190\uc2e4 \uac12\uacfc \uc815\ud655\ub3c4\ub97c \uc800\uc7a5\ud558\uace0 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\ub3c4\ub85d \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\n\nnum_epochs = 10\ntrain_losses = []\ntest_losses = []\ntrain_accuracies = []\ntest_accuracies = []\n\n# \ud6c8\ub828 \ud568\uc218\ndef train():\n model.train() # \ubaa8\ub378\uc744 \ud6c8\ub828 \ubaa8\ub4dc\ub85c \uc804\ud658\n running_loss = 0.0\n correct = 0\n total = 0\n \n for inputs, labels in trainloader:\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n outputs = model(inputs) # \uc608\uce21\n loss = criterion(outputs, labels) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # \uc5ed\uc804\ud30c\n optimizer.step() # \ub9e4\uac1c\ubcc0\uc218 \uc5c5\ub370\uc774\ud2b8\n \n running_loss += loss.item()\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n \n # \ud6c8\ub828 \uc190\uc2e4\uacfc \uc815\ud655\ub3c4 \uc800\uc7a5\n train_losses.append(running_loss \/ len(trainloader))\n train_accuracies.append(correct \/ total)\n\n# \uac80\uc99d \ud568\uc218\ndef test():\n model.eval() # \ubaa8\ub378\uc744 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\n running_loss = 0.0\n correct = 0\n total = 0\n\n with torch.no_grad(): # \uae30\uc6b8\uae30 \uacc4\uc0b0 \ube44\ud65c\uc131\ud654\n for inputs, labels in testloader:\n outputs = model(inputs) # \uc608\uce21\n loss = criterion(outputs, labels) # \uc190\uc2e4 \uacc4\uc0b0\n \n running_loss += loss.item()\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n \n # \uac80\uc99d \uc190\uc2e4\uacfc \uc815\ud655\ub3c4 \uc800\uc7a5\n test_losses.append(running_loss \/ len(testloader))\n test_accuracies.append(correct \/ total)\n<\/code><\/pre>\n7. \ud6c8\ub828 \ub8e8\ud504<\/h2>\n
\ud6c8\ub828 \ub8e8\ud504\ub97c \uc2e4\ud589\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0, \uac01 \uc5d0\ud3ec\ud06c\ub9c8\ub2e4 \ud6c8\ub828 \ubc0f \uac80\uc99d \uc190\uc2e4, \uc815\ud655\ub3c4\ub97c \uae30\ub85d\ud569\ub2c8\ub2e4.<\/p>\n
for epoch in range(num_epochs):\n train() # \ud6c8\ub828 \ud568\uc218 \ud638\ucd9c\n test() # \uac80\uc99d \ud568\uc218 \ud638\ucd9c\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], '\n f'Train Loss: {train_losses[-1]:.4f}, Train Accuracy: {train_accuracies[-1]:.4f}, '\n f'Test Loss: {test_losses[-1]:.4f}, Test Accuracy: {test_accuracies[-1]:.4f}')\n<\/code><\/pre>\n8. \uacb0\uacfc \uc2dc\uac01\ud654<\/h2>\n
\ud6c8\ub828 \uacfc\uc815\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc73c\ub85c Matplotlib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc2e4\uacfc \uc815\ud655\ub3c4\ub97c \uadf8\ub798\ud504\ub85c \ud45c\ud604\ud569\ub2c8\ub2e4.<\/p>\n
plt.figure(figsize=(12, 5))\n\n# \uc190\uc2e4 \uc2dc\uac01\ud654\nplt.subplot(1, 2, 1)\nplt.plot(train_losses, label='Train Loss')\nplt.plot(test_losses, label='Test Loss')\nplt.title('Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\n\n# \uc815\ud655\ub3c4 \uc2dc\uac01\ud654\nplt.subplot(1, 2, 2)\nplt.plot(train_accuracies, label='Train Accuracy')\nplt.plot(test_accuracies, label='Test Accuracy')\nplt.title('Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\n\nplt.tight_layout()\nplt.show()\n<\/code><\/pre>\n9. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(Pytorch)\ub97c \uc774\uc6a9\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud6c8\ub828 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc2dc\uac01\ud654 \uae30\ubc95\uacfc \uc9c0\ud45c\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \uc778\uc0ac\uc774\ud2b8\ub97c \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\ucc98\ub7fc \ud6c8\ub828\uacfc\uc815\uc5d0\uc11c\uc758 \ubaa8\ub2c8\ud130\ub9c1\uacfc \uc2dc\uac01\ud654\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\ubbc0\ub85c, \ud56d\uc0c1 \uc5fc\ub450\uc5d0 \ub450\uace0 \ud574\ub2f9 \ub0b4\uc6a9\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc801\uc808\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \ubaa8\ub378\uc758 \uacfc\uc801\ud569 \ubc29\uc9c0 \ubc0f \uc77c\ubc18\ud654 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ub3c4\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1\uc758 \uc911\uc694\uc131 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \ub54c, \ub2e8\uc21c\ud788 \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub9cc \ud655\uc778\ud558\ub294 \uac83\uc740 \ucda9\ubd84\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130\uc5d0\uc11c\uc758 \uc190\uc2e4(loss)\uacfc … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1”<\/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":[33],"tags":[],"class_list":["post-30158","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1 - \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\/30158\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc801\uc808\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \ubaa8\ub378\uc758 \uacfc\uc801\ud569 \ubc29\uc9c0 \ubc0f \uc77c\ubc18\ud654 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ub3c4\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch) \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1\uc758 \uc911\uc694\uc131 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \ub54c, \ub2e8\uc21c\ud788 \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub9cc \ud655\uc778\ud558\ub294 \uac83\uc740 \ucda9\ubd84\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ud6c8\ub828 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130\uc5d0\uc11c\uc758 \uc190\uc2e4(loss)\uacfc … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud6c8\ub828 \uacfc\uc815 \ubaa8\ub2c8\ud130\ub9c1"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30158\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:52+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:49:39+00:00\" 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