\n \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional Neural Networks, CNN)\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uace0, \ud30c\uc774\ud1a0\uce58(PyTorch) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec CNN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n CNN\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \ucc98\ub9ac\uc5d0 \uc0ac\uc6a9\ub418\uba70, \uc774\ubbf8\uc9c0 \ub0b4\uc5d0\uc11c \ud2b9\uc9d5\uc744 \uc798 \ucd94\ucd9c\ud558\ub294 \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\n CNN\uc740 \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\ub4e4\uc5ec, \uc5ec\ub7ec \uacc4\uce35\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0\uc758 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0 \ubd84\ub958\ud558\ub294 \uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4. CNN\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \uc694\uc18c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:\n<\/p>\n
\n CNN\uc758 \uc791\ub3d9 \uc6d0\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4:\n<\/p>\n
\n \uc774\uc81c \ud30c\uc774\uc36c\uc758 \ud30c\uc774\ud1a0\uce58 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c CNN\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0 \uc784\ud3ec\ud2b8\ud574\uc57c \ud569\ub2c8\ub2e4.\n<\/p>\n
pip install torch torchvision matplotlib<\/code><\/pre>\n1. \ub77c\uc774\ube0c\ub7ec\ub9ac \uc784\ud3ec\ud2b8<\/h3>\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt<\/code><\/pre>\n2. \ub370\uc774\ud130\uc14b \ub85c\ub4dc<\/h3>\n
\n MNIST \ub370\uc774\ud130\uc14b\uc740 \uc190\uae00\uc528 \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc73c\ub85c, CNN\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \ub85c\ub4dc\ud569\ub2c8\ub2e4.\n<\/p>\n
# \ub370\uc774\ud130 \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n\n# \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \uc138\ud2b8 \ub2e4\uc6b4\ub85c\ub4dc\ntrainset = torchvision.datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n\ntestset = torchvision.datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)<\/code><\/pre>\n3. CNN \ubaa8\ub378 \uc815\uc758<\/h3>\n
\n \uc774\uc81c CNN \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c CNN \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uba70, \ub450 \uac1c\uc758 \ud569\uc131\uacf1 \uce35\uacfc \ud480\ub9c1 \uce35, \uadf8\ub9ac\uace0 \ub450 \uac1c\uc758 \uc644\uc804 \uc5f0\uacb0 \uce35\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4.\n<\/p>\n
class CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Conv2d(1, 32, kernel_size=5) # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 \uce35\n self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # \ud480\ub9c1 \uce35\n self.conv2 = nn.Conv2d(32, 64, kernel_size=5) # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1 \uce35\n self.fc1 = nn.Linear(64 * 4 * 4, 128) # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0 \uce35\n self.fc2 = nn.Linear(128, 10) # \ub450 \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0 \uce35 (10\uac1c \ud074\ub798\uc2a4)\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x))) # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 \ubc0f \ud480\ub9c1\n x = self.pool(F.relu(self.conv2(x))) # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1 \ubc0f \ud480\ub9c1\n x = x.view(-1, 64 * 4 * 4) # Flatten\n x = F.relu(self.fc1(x)) # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\n x = self.fc2(x) # \ub450 \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\n return x<\/code><\/pre>\n4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\n \ubaa8\ub378\uc744 \uc815\uc758\ud55c \ud6c4, \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud558\uace0 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. CrossEntropyLoss\ub97c \uc190\uc2e4 \ud568\uc218\ub85c \uc0ac\uc6a9\ud558\uace0, SGD(Stochastic Gradient Descent)\ub97c \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n
device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nmodel = CNN().to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(10): # 10 \uc5d0\ud3ed \ub3d9\uc548 \ud559\uc2b5\n running_loss = 0.0\n for i, data in enumerate(trainloader, 0):\n inputs, labels = data\n inputs, labels = inputs.to(device), labels.to(device)\n\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n outputs = model(inputs) # \ubaa8\ub378\uc758 \ucd9c\ub825 \uacc4\uc0b0\n loss = criterion(outputs, labels) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # \uae30\uc6b8\uae30 \uacc4\uc0b0\n optimizer.step() # \ub9e4\uac1c\ubcc0\uc218 \uc5c5\ub370\uc774\ud2b8\n\n running_loss += loss.item()\n print(f'Epoch {epoch + 1}, Loss: {running_loss \/ len(trainloader)}')\n\nprint('Finished Training')<\/code><\/pre>\n5. \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\n \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.\n<\/p>\n
correct = 0\ntotal = 0\n\nwith torch.no_grad():\n for data in testloader:\n images, labels = data\n images, labels = images.to(device), labels.to(device)\n outputs = model(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy of the network on the 10000 test images: {100 * correct \/ total}%')<\/code><\/pre>\n6. \ubaa8\ub378 \uc800\uc7a5 \ubc0f \ubd88\ub7ec\uc624\uae30<\/h3>\n
\n \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ub098\uc911\uc5d0 \ub2e4\uc2dc \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n<\/p>\n
# \ubaa8\ub378 \uc800\uc7a5\ntorch.save(model.state_dict(), 'model.pth')\n\n# \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\nmodel = CNN()\nmodel.load_state_dict(torch.load('model.pth'))<\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\n \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790 \uc778\uc2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
\n CNN\uc740 \uc774\ubbf8\uc9c0 \ubd84\ub958 \ubc0f \ub2e4\uc591\ud55c \ucef4\ud4e8\ud130 \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uae30\ubcf8\uc801\uc778 \uc774\ud574\uc640 \uad6c\ud604 \ubc29\ubc95\uc744 \uc544\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.
\n \ub354 \ub098\uc544\uac00 \uc5ec\ub7ec \uac00\uc9c0 \uace0\uae09 \ubc29\ubc95\ub860\uc744 \ud559\uc2b5\ud558\uc5ec \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ub354\uc6b1 \ubc1c\uc804\uc2dc\ud0a4\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"