\n \ub525\ub7ec\ub2dd\uc740 \uae30\uacc4\ud559\uc2b5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc9c0\ub2a5 \uc5f0\uad6c\uc758 \uc8fc\uc694\ud55c \uc601\uc5ed \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional Neural Networks, CNN)\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubc0f \ucc98\ub9ac\uc5d0 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \uad6c\uc870\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec CNN\uc758 \uae30\ubcf8 \uad6c\uc870\uc640 \ub3d9\uc791 \uc6d0\ub9ac\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n
\n \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n <\/p>\n
\n \uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c CNN\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 Fashion MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc758\ub958 \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0 \uac00\uc838\uc635\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud30c\uc774\ud1a0\uce58\ub97c \uc124\uce58\ud569\ub2c8\ub2e4:\n <\/p>\n
pip install torch torchvision<\/code><\/pre>\n4.2. \ub370\uc774\ud130\uc14b \ub85c\ub529<\/h3>\n
\n Fashion MNIST \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\uace0 \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub85c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nimport torch\nimport torchvision.transforms as transforms\nimport torchvision.datasets as datasets\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# \ud2b8\ub808\uc774\ub2dd \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ntrain_dataset = datasets.FashionMNIST(root='.\/data', train=True, transform=transform, download=True)\ntest_dataset = datasets.FashionMNIST(root='.\/data', train=False, transform=transform)\n\n# \ub370\uc774\ud130\ub85c\ub354 \uc124\uc815\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n <\/code><\/pre>\n4.3. CNN \ubaa8\ub378 \uc815\uc758<\/h3>\n
\n CNN \ubaa8\ub378\uc744 \uc815\uc758\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud569\uc131\uacf1\uce35, \ud65c\uc131\ud654\uce35, \ud480\ub9c1\uce35, \uc644\uc804 \uc5f0\uacb0\uce35\uc73c\ub85c \uad6c\uc131\ub41c \uac04\ub2e8\ud55c CNN\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4.\n <\/p>\n
\nimport torch.nn as nn\n\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1) # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1\uce35\n self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1\uce35\n self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # \ucd5c\ub300 \ud480\ub9c1\uce35\n self.fc1 = nn.Linear(64 * 7 * 7, 128) # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n self.fc2 = nn.Linear(128, 10) # \ub450 \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n\n def forward(self, x):\n x = self.pool(torch.relu(self.conv1(x))) # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1\uacfc \ud480\ub9c1\n x = self.pool(torch.relu(self.conv2(x))) # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1\uacfc \ud480\ub9c1\n x = x.view(-1, 64 * 7 * 7) # \ud150\uc11c\ub97c \ud3c9\ud0c4\ud654\n x = torch.relu(self.fc1(x)) # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n x = self.fc2(x) # \ub450 \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0\uce35\n return x\n <\/code><\/pre>\n4.4. \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h3>\n
\n \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828\uc744 \uc704\ud55c \uc124\uc815\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nimport torch.optim as optim\n\n# \ubaa8\ub378, \uc190\uc2e4\ud568\uc218 \ubc0f \ucd5c\uc801\ud654\uae30 \uc815\uc758\nmodel = CNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n\n# \ubaa8\ub378 \ud6c8\ub828\nnum_epochs = 5\nfor epoch in range(num_epochs):\n model.train()\n running_loss = 0.0\n for images, labels in train_loader:\n optimizer.zero_grad() # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n outputs = model(images) # \ubaa8\ub378\uc5d0 \ub300\ud55c \uc608\uce21\n loss = criterion(outputs, labels) # \uc190\uc2e4 \uacc4\uc0b0\n loss.backward() # \uc5ed\uc804\ud30c\n optimizer.step() # \uac00\uc911\uce58 \uac31\uc2e0\n running_loss += loss.item()\n\n print(f'Epoch [{epoch+1}\/{num_epochs}], Loss: {running_loss\/len(train_loader):.4f}')\n <\/code><\/pre>\n4.5. \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n
\n \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uc5ec \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\ub97c \ud655\uc778\ud569\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud3c9\uac00\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nmodel.eval()\ncorrect = 0\ntotal = 0\nwith torch.no_grad():\n for images, labels in test_loader:\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 model on the test images: {100 * correct \/ total:.2f}%')\n <\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \ud575\uc2ec \uad6c\uc131 \uc694\uc18c\uc778 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc758 \uae30\ubcf8 \uad6c\uc870\uc640 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \uc2e4\uc804 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. CNN\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\uc758 \ud2b9\uc9d5\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uad6c\ubcc4\ud558\uace0 \ubd84\ub958\ud558\ub294 \ubc29\ubc95\uc744 \uc774\ud574\ud558\uc168\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc758 \uc138\uacc4\ub294 \ubb34\uad81\ubb34\uc9c4\ud558\uba70, \uc55e\uc73c\ub85c \ub9ce\uc740 \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\uc2b5\uc744 \ud1b5\ud574 \uc2e4\ub825 \ud5a5\uc0c1\uc5d0 \ud798\uc4f0\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n
6. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n
\n – Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning<\/em>, MIT Press, 2016
\n – PyTorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/a>\n<\/p>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"