\uc624\ub958 \ubd84\uc11d \ubc0f \ubaa8\ub378 \uac1c\uc120\uc744 \uc704\ud55c \uc778\uc0ac\uc774\ud2b8\ub97c \uc81c\uacf5\ud55c\ub2e4.<\/li>\n<\/ul>\n3. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58\ud558\uae30<\/h2>\n
\n \ud2b9\uc131 \ub9f5\uc744 \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\uc640 \uba87 \uac00\uc9c0 \ucd94\uac00\uc801\uc778 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.
\n \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub85c \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install torch torchvision matplotlib<\/code><\/pre>\n<\/p>\n4. \ub370\uc774\ud130\uc14b \uc900\ube44\ud558\uae30<\/h2>\n
\n MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574,
\n torchvision\uc758 datasets<\/code> \ubaa8\ub4c8\uc744 \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n\nimport torch\nimport torchvision.transforms as transforms\nfrom torchvision import datasets\nfrom torch.utils.data import DataLoader\n\n# Transform \uc815\uc758\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\nmnist_data = datasets.MNIST(root='.\/data', train=True, transform=transform, download=True)\ndata_loader = DataLoader(mnist_data, batch_size=64, shuffle=True)\n <\/code><\/pre>\n5. \uac04\ub2e8\ud55c CNN \ubaa8\ub378 \uad6c\ucd95\ud558\uae30<\/h2>\n
\n \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uc5ec \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc774
\n \uad6c\uc131\ub429\ub2c8\ub2e4: Convolutional Layer -> ReLU -> Max Pooling -> Fully Connected Layer\n <\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleCNN(nn.Module):\n def __init__(self):\n super(SimpleCNN, self).__init__()\n self.conv1 = nn.Conv2d(1, 16, kernel_size=5)\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(16, 32, kernel_size=5)\n self.fc1 = nn.Linear(32 * 4 * 4, 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, 32 * 4 * 4)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\nmodel = SimpleCNN()\n <\/code><\/pre>\n6. \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654\ud558\uae30<\/h2>\n
\n \ubaa8\ub378\uc758 \ud2b9\uc815 \ub808\uc774\uc5b4\uc5d0\uc11c \ud2b9\uc131 \ub9f5\uc744 \ucd94\ucd9c\ud558\uace0 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uccab \ubc88\uc9f8
\n \ud569\uc131\uacf1 \uce35\uc758 \ud2b9\uc131 \ub9f5\uc744 \uc2dc\uac01\ud654\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nimport matplotlib.pyplot as plt\n\n# \uac00\uc0c1\uc758 \uc774\ubbf8\uc9c0\ub97c \ubc30\uce58 \ud558\ub098 \uac00\uc838\uc624\uae30\ndata_iter = iter(data_loader)\nimages, labels = next(data_iter)\n\n# \ubaa8\ub378\uc744 \ud1b5\uacfc\uc2dc\ud0a4\uace0 \ud2b9\uc131 \ub9f5 \ucd94\ucd9c\nwith torch.no_grad():\n feature_maps = model.conv1(images)\n\n# \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654\ndef show_feature_maps(feature_maps):\n feature_maps = feature_maps[0].detach().numpy()\n num_feature_maps = feature_maps.shape[0]\n\n plt.figure(figsize=(15, 15))\n for i in range(num_feature_maps):\n plt.subplot(8, 8, i + 1)\n plt.imshow(feature_maps[i], cmap='gray')\n plt.axis('off')\n plt.show()\n\nshow_feature_maps(feature_maps)\n <\/code><\/pre>\n7. \uacb0\uacfc \ud574\uc11d\ud558\uae30<\/h2>\n
\n \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \uac01 \uc785\ub825 \uc774\ubbf8\uc9c0\uc5d0\uc11c \uc5b4\ub5a4 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0 \uc788\ub294\uc9c0\ub97c
\n \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \ud2b9\uc131 \ub9f5\uc740 \uc11c\ub85c \ub2e4\ub978 \ud544\ud130\uac00 \uc801\uc6a9\ub41c \uacb0\uacfc\ubb3c\ub85c,
\n \ud2b9\uc815\ud55c \ud328\ud134\uc774\ub098 \ud615\ud0dc\uac00 \uac15\uc870\ub418\uc5b4 \ub098\ud0c0\ub098\uac8c \ub429\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \ud559\uc2b5
\n \uacfc\uc815\uc5d0 \ub300\ud55c \ud1b5\ucc30\ub825\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
8. \uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub85c \uac04\ub2e8\ud55c CNN \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 \uce35\uc5d0\uc11c\uc758
\n \ud2b9\uc131 \ub9f5\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654\ub294 \ubaa8\ub378\uc758 \ub0b4\ubd80 \uc791\ub3d9 \ubc29\uc2dd\uc744
\n \uc774\ud574\ud558\uace0, \uc0dd\uc131\ub418\ub294 \ud328\ud134\uc5d0 \ub300\ud55c \uc778\uc0ac\uc774\ud2b8\ub97c \uc81c\uacf5\ud558\ub294 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \uacc4\uc18d\ud574\uc11c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc774\ub7ec\ud55c \uc2dc\uac01\ud654 \uae30\ubc95\ub4e4\uc744 \ud1b5\ud574
\n \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc124\uba85\ud558\uace0 \uac1c\uc120\ud558\ub294\ub370 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc55e\uc73c\ub85c \uc774\uc640 \uc720\uc0ac\ud55c \uc5ec\ub7ec \uc8fc\uc81c\ub97c \ub2e4\ub8e8\uba70 \uc9c0\uc18d\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uac83\uc744 \uad8c\uc7a5\ub4dc\ub9bd\ub2c8\ub2e4.\n <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc744 \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ud2b9\ud788, \ud2b9\uc131 \ub9f5(feature map)\uc740 \uc2e0\uacbd\ub9dd\uc758 \uc911\uac04 \uce35\uc5d0\uc11c \uc0dd\uc131\ub41c \uc800\ucc28\uc6d0 \ub370\uc774\ud130\ub85c\uc11c, \ubaa8\ub378\uc774 \uc785\ub825 \ub370\uc774\ud130\uc5d0\uc11c \uc5b4\ub5a4 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0 \uc788\ub294\uc9c0\ub97c \ubcf4\uc5ec\uc8fc\ub294 \uc911\uc694\ud55c \uc9c0\ud45c\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc131 \ub9f5\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uace0, \uc2e4\uc2b5\uc744 \ud1b5\ud574 \uadf8 \uacfc\uc815\uc744 \uc774\ud574\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud2b9\uc131 \ub9f5\uc774\ub780? \ud2b9\uc131 \ub9f5\uc740 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc5d0\uc11c \uc0dd\uc131\ub41c \ub2e4\uc591\ud55c \ud544\ud130\ub97c \ud1b5\ud574 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654”<\/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-30130","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud2b9\uc131 \ub9f5 \uc2dc\uac01\ud654 - 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