{"id":29922,"date":"2024-10-28T03:18:42","date_gmt":"2024-10-28T03:18:42","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29922"},"modified":"2024-11-26T06:50:49","modified_gmt":"2024-11-26T06:50:49","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-lenet-5","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29922\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, LeNet-5"},"content":{"rendered":"<p><body><\/p>\n<p>\ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \ub370\uc774\ud130 \uacfc\ud559\uc758 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc5c4\uccad\ub09c \uc778\uae30\ub97c \ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ubd84\uc57c\uc758 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uc720\uba85\ud55c \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98 \uc911 \ud558\ub098\uc778 LeNet-5\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>LeNet-5\ub780?<\/h2>\n<p>LeNet-5\ub294 Yann LeCun\uc744 \ud3ec\ud568\ud55c \uc5f0\uad6c\uc790\ub4e4\uc774 1998\ub144\uc5d0 \uac1c\ubc1c\ud55c \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN) \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. \uc774\ubbf8\uc9c0\ub97c \uc778\uc2dd\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \ubaa8\ub378\ub85c, \uc8fc\ub85c \uc190\uae00\uc528 \uc22b\uc790 \uc778\uc2dd\uc5d0 \uc0ac\uc6a9\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 CNN\uc758 \uae30\ubcf8 \uad6c\uc870\ub97c \ub530\ub974\uba70, \uc5ec\ub7ec \uacc4\uce35\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. LeNet-5\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uacc4\uce35\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc785\ub825\uce35:<\/strong> 32&#215;32 \ud53d\uc140\uc758 \uadf8\ub808\uc774\uc2a4\ucf00\uc77c \uc774\ubbf8\uc9c0.<\/li>\n<li><strong>\ud569\uc131\uacf1\uce35 (C1):<\/strong> 6\uac1c\uc758 \ud544\ud130(5&#215;5)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc131 \ub9f5\uc744 \uc0dd\uc131 (28&#215;28 \ud06c\uae30).<\/li>\n<li><strong>\ud480\ub9c1\uce35 (S2):<\/strong> \ud3c9\uade0 \ud480\ub9c1\uc744 \ud1b5\ud574 6\uac1c\uc758 14&#215;14 \ud2b9\uc131 \ub9f5 \uc0dd\uc131.<\/li>\n<li><strong>\ud569\uc131\uacf1\uce35 (C3):<\/strong> 16\uac1c\uc758 \ud544\ud130\ub97c \uc0ac\uc6a9, 10&#215;10 \ud2b9\uc131 \ub9f5 \uc0dd\uc131.<\/li>\n<li><strong>\ud480\ub9c1\uce35 (S4):<\/strong> \ud3c9\uade0 \ud480\ub9c1\uc744 \ud1b5\ud574 16\uac1c\uc758 5&#215;5 \ud2b9\uc131 \ub9f5 \uc0dd\uc131.<\/li>\n<li><strong>\ud569\uc131\uacf1\uce35 (C5):<\/strong> 120\uac1c\uc758 \ud544\ud130(5&#215;5)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub9c8\uc9c0\ub9c9 \ud2b9\uc131 \ub9f5 \uc0dd\uc131.<\/li>\n<li><strong>\uc644\uc804 \uc5f0\uacb0\uce35 (F6):<\/strong> 84\uac1c\uc758 \ub274\ub7f0\uc73c\ub85c \ucd5c\uc885 \ucd9c\ub825.<\/li>\n<li><strong>\ucd9c\ub825\uce35:<\/strong> 10\uac1c\uc758 \ud074\ub798\uc2a4 (0-9)\ub85c \ubd84\ub958.<\/li>\n<\/ul>\n<h2>LeNet-5\uc758 \uc911\uc694\uc131<\/h2>\n<p>LeNet-5\ub294 CNN\uc758 \uae30\ubcf8 \uc544\ud0a4\ud14d\ucc98 \uc911 \ud558\ub098\ub85c, \ub9ce\uc740 \uae4a\uc740 \ub124\ud2b8\uc6cc\ud06c\uc758 \uae30\ubc18\uc774 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubd84\uc57c\uc5d0\uc11c \ub9ce\uc740 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uace0, \ud604\uc7ac\uc5d0\ub3c4 \ub2e4\uc591\ud55c \ubcc0\ud615 \ubaa8\ub378\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. LeNet-5\uc758 \ub2e8\uc21c\uc131\uacfc \ud6a8\uc728\uc131 \ub355\ubd84\uc5d0, \ub9ce\uc740 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uc88b\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n<h2>LeNet-5 \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc774\uc81c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec LeNet-5\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\ub294 \uc0ac\uc6a9\uc790 \uce5c\ud654\uc801\uc778 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \ub2e4\uc591\ud55c \uc5f0\uad6c\uc640 \uc0b0\uc5c5\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub610\ud55c, PyTorch\ub294 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud558\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\ud658\uacbd \uc124\uc815<\/h3>\n<p>\uba3c\uc800, \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0 \ud658\uacbd\uc744 \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec PyTorch\uc640 torchvision\uc744 \uc124\uce58\ud558\uc138\uc694:<\/p>\n<pre><code>pip install torch torchvision<\/code><\/pre>\n<h3>LeNet-5 \ubaa8\ub378 \uad6c\ud604<\/h3>\n<p>\uc774\uc81c LeNet-5\uc758 \uad6c\uc870\ub97c \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass LeNet5(nn.Module):\n    def __init__(self):\n        super(LeNet5, self).__init__()\n        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)\n        self.conv2 = nn.Conv2d(6, 16, kernel_size=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 = F.relu(self.conv1(x))\n        x = F.avg_pool2d(x, kernel_size=2, stride=2)\n        x = F.relu(self.conv2(x))\n        x = F.avg_pool2d(x, kernel_size=2, stride=2)\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\n<\/code><\/pre>\n<h3>\ubaa8\ub378 \ud6c8\ub828 \uc704\ud55c \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n<p>LeNet-5\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud6c8\ub828\ud560 \uac83\uc785\ub2c8\ub2e4. torchvision\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc27d\uac8c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec MNIST \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\uc138\uc694:<\/p>\n<pre><code>from torchvision import datasets, transforms\n\ntransform = transforms.Compose([\n    transforms.Grayscale(num_output_channels=1),\n    transforms.Resize((32, 32)),\n    transforms.ToTensor(),\n    transforms.Normalize((0.5,), (0.5,))\n])\n\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntest_dataset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\n\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n<\/code><\/pre>\n<h3>\ubaa8\ub378 \ud6c8\ub828<\/h3>\n<p>\ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 Cross Entropy Loss\uc640 Adam \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel = LeNet5().to(device)\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n\nnum_epochs = 5\nfor epoch in range(num_epochs):\n    for i, (images, labels) in enumerate(train_loader):\n        images = images.to(device)\n        labels = labels.to(device)\n\n        # Forward pass\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n\n        # Backward and optimize\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        if (i + 1) % 100 == 0:\n            print(f'Epoch [{epoch + 1}\/{num_epochs}], Step [{i + 1}\/{len(train_loader)}], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n<h3>\ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc815\ud655\ub3c4\ub97c \ud655\uc778\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>model.eval()\nwith torch.no_grad():\n    correct = 0\n    total = 0\n    for images, labels in test_loader:\n        images = images.to(device)\n        labels = 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\n    print(f'Accuracy of the model on the test images: {100 * correct \/ total:.2f}%')\n<\/code><\/pre>\n<h3>\uacb0\ub860<\/h3>\n<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 LeNet-5 \uc544\ud0a4\ud14d\ucc98\ub97c PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud558\uace0 \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. LeNet-5\ub294 CNN\uc758 \uae30\ucd08\ub97c \uc774\ud574\ud558\uace0 \uc2e4\uc2b5\ud560 \uc218 \uc788\ub294 \uc88b\uc740 \uc608\uc81c\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ub354 \ubcf5\uc7a1\ud55c \ub124\ud2b8\uc6cc\ud06c \uc544\ud0a4\ud14d\ucc98\ub098 \ub2e4\uc591\ud55c \uc751\uc6a9\uc73c\ub85c \ubc1c\uc804\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\ub85c\ub294 \ub354 \uae4a\uc740 \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870\ub098 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud574\ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4.<\/p>\n<h3>\ucc38\uace0 \uc790\ub8cc<\/h3>\n<ul>\n<li><a href=\"http:\/\/yann.lecun.com\/exdb\/publis\/pdf\/lecun-98.pdf\">LeNet-5 \ub17c\ubb38<\/a><\/li>\n<li><a href=\"https:\/\/pytorch.org\/\">PyTorch \uacf5\uc2dd \ud648\ud398\uc774\uc9c0<\/a><\/li>\n<li><a href=\"http:\/\/yann.lecun.com\/exdb\/mnist\/\">MNIST \ub370\uc774\ud130\uc14b<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \ub370\uc774\ud130 \uacfc\ud559\uc758 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc5c4\uccad\ub09c \uc778\uae30\ub97c \ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ubd84\uc57c\uc758 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uc720\uba85\ud55c \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98 \uc911 \ud558\ub098\uc778 LeNet-5\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. LeNet-5\ub780? LeNet-5\ub294 Yann LeCun\uc744 \ud3ec\ud568\ud55c \uc5f0\uad6c\uc790\ub4e4\uc774 1998\ub144\uc5d0 \uac1c\ubc1c\ud55c \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN) \uc544\ud0a4\ud14d\ucc98\uc785\ub2c8\ub2e4. \uc774\ubbf8\uc9c0\ub97c \uc778\uc2dd\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \ubaa8\ub378\ub85c, \uc8fc\ub85c \uc190\uae00\uc528 \uc22b\uc790 \uc778\uc2dd\uc5d0 \uc0ac\uc6a9\ub418\uc5c8\uc2b5\ub2c8\ub2e4. &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29922\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, LeNet-5&#8221;<\/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-29922","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, LeNet-5 - \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\/29922\/\" \/>\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, LeNet-5 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \ub370\uc774\ud130 \uacfc\ud559\uc758 \uc5ec\ub7ec \ubd84\uc57c\uc5d0\uc11c \uc5c4\uccad\ub09c \uc778\uae30\ub97c \ub04c\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ubd84\uc57c\uc758 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uc720\uba85\ud55c \ub525\ub7ec\ub2dd \uc544\ud0a4\ud14d\ucc98 \uc911 \ud558\ub098\uc778 LeNet-5\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 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