\ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8 \ubd84\ub9ac<\/li>\n<\/ul>\n3.2 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \ub370\uc774\ud130 \uc900\ube44\ud558\uae30<\/h3>\n
\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\uae30 \uc704\ud574 torchvision<\/code>\uacfc \uac19\uc740 \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, CIFAR-10 \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose(\n [transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\ntrainset = torchvision.datasets.CIFAR10(root='.\/data', train=True,\n download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n shuffle=True, num_workers=2)<\/code><\/pre>\n3.3 \ubaa8\ub378 \uad6c\uc131<\/h3>\n
\ubaa8\ub378\uc744 \uad6c\uc131\ud560 \ub54c\ub294 \uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870\ub97c \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c\ub294 torch.nn.Module<\/code> \ud074\ub798\uc2a4\ub97c \uc0c1\uc18d\ubc1b\uc544 \uc0ac\uc6a9\uc790 \uc815\uc758 \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc758 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\nimport 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.conv1 = nn.Conv2d(3, 6, 5)\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(6, 16, 5)\n self.fc1 = nn.Linear(16 * 5 * 5, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = x.view(-1, 16 * 5 * 5)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x<\/code><\/pre>\n3.4 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ub54c\ub294 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ubd84\ub958 \ubb38\uc81c\uc5d0\uc11c\ub294 \ud06c\ub85c\uc2a4 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba70, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 SGD\ub098 Adam\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch.optim as optim\n\nnet = Net()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n\nfor epoch in range(2): # \ub370\uc774\ud130\uc14b\uc744 \uc5ec\ub7ec \ubc88 \ubc18\ubcf5\ud569\ub2c8\ub2e4.\n for i, data in enumerate(trainloader, 0):\n inputs, labels = data\n optimizer.zero_grad() # Gradients \ucd08\uae30\ud654\n outputs = net(inputs) # Forward pass\n loss = criterion(outputs, labels) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # Backward pass\n optimizer.step() # \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n\nprint('Finished Training')<\/code><\/pre>\n3.5 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4\uc5d0\ub294 \uc774\ub97c \ud3c9\uac00\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/p>\n
correct = 0\ntotal = 0\nwith torch.no_grad(): # Gradient \uacc4\uc0b0 \ube44\ud65c\uc131\ud654\n for data in testloader:\n images, labels = data\n outputs = net(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint('Accuracy of the network on the 10000 test images: %d %%' % (\n 100 * correct \/ total))<\/code><\/pre>\n4. \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804 \ubc29\ud5a5<\/h2>\n
\ub525\ub7ec\ub2dd\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d \ubc1c\uc804\ud560 \uac83\uc785\ub2c8\ub2e4. \ud2b9\ud788 \uc790\uc728\uc8fc\ud589\ucc28, \uc758\ub8cc \uc9c4\ub2e8, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc774\ubbf8\uc9c0 \uc0dd\uc131 \ub4f1 \ub9ce\uc740 \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uac00\uc838\uc62c \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58 \ub610\ud55c \uc774\ub7ec\ud55c \ud2b8\ub80c\ub4dc\uc5d0 \ub9de\ucdb0 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud574 \ub098\uac08 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \ub525\ub7ec\ub2dd \ud559\uc2b5 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc900\ube44, \ubaa8\ub378 \uad6c\uc131, \ud559\uc2b5, \ud3c9\uac00\uc758 \ub2e8\uacc4\ub97c \ud1b5\ud574 \ud30c\uc774\ud1a0\uce58\uac00 \uc81c\uacf5\ud558\ub294 \ub2e4\uc591\ud55c \uae30\ub2a5\uacfc \ud3b8\ub9ac\ud568\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uac00 \ub525\ub7ec\ub2dd\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub113\ud788\uace0, \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc801\uc6a9\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \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, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uae30\uacc4\ud559\uc2b5 \ubc29\ubc95\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \uc624\ub298\ub0a0 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub418\uace0 \uc788\ub294 \ub525\ub7ec\ub2dd\uc758 \ud575\uc2ec \uae30\uc220 \uc911 \ud558\ub098\ub294 \ubc14\ub85c \ud30c\uc774\ud1a0\uce58(PyTorch)\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc0ac\uc6a9\ud558\uae30 \uc26c\uc6b4 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\uc640 \uac15\ub825\ud55c \ud150\uc11c \uc5f0\uc0b0 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\uc5d0\uac8c \uc778\uae30\ub97c \ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd\uc758 \ud559\uc2b5 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd \ud559\uc2b5 \uacfc\uc815”<\/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-30022","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd \ud559\uc2b5 \uacfc\uc815 - 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