{"id":30116,"date":"2024-10-28T03:19:40","date_gmt":"2024-10-28T03:19:40","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30116"},"modified":"2024-11-26T06:49:51","modified_gmt":"2024-11-26T06:49:51","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-%ec%ba%90%ea%b8%80%ec%9d%b4%eb%9e%80","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30116\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00\uc774\ub780"},"content":{"rendered":"<p><body><\/p>\n<p>\ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \ub180\ub77c\uc6b4 \uc18d\ub3c4\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc0c1\uc5c5\uc801 \uc751\uc6a9 \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc5f0\uad6c \ubc0f \uad50\uc721\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud750\ub984 \uc18d\uc5d0\uc11c \uc911\uc694\ud55c \ud50c\ub7ab\ud3fc \uc911 \ud558\ub098\uac00 <strong>\uce90\uae00(Kaggle)<\/strong>\uc785\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uce90\uae00\uc758 \uac1c\ub150, \uc5ed\ud560, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uce90\uae00(Kaggle) \uc18c\uac1c<\/h2>\n<p>\uce90\uae00\uc740 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4 \ucee4\ubba4\ub2c8\ud2f0\ub85c, \ub370\uc774\ud130 \ubd84\uc11d, \uba38\uc2e0\ub7ec\ub2dd, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uacbd\uc7c1\ud560 \uc218 \uc788\ub294 \ud50c\ub7ab\ud3fc\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\ub294 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uc744 \ud0d0\uc0c9\ud558\uace0, \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uc5ec \ub2e4\ub978 \uc0ac\uc6a9\uc790\uc640 \uacf5\uc720\ud558\uac70\ub098 \uacbd\uc9c4\ub300\ud68c\uc5d0 \ucc38\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uce90\uae00\uc740 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4\uc640 \uba38\uc2e0\ub7ec\ub2dd \uad00\ub828 \uacbd\ud5d8\uc744 \uc313\uace0, \uc790\uc2e0\uc758 \uae30\uc220\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370\uc5d0 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n<h3>1.1 \uce90\uae00\uc758 \uc8fc\uc694 \uae30\ub2a5<\/h3>\n<ul>\n<li><strong>\ub370\uc774\ud130\uc14b:<\/strong> \uc720\uc800\ub4e4\uc740 \ub2e4\uc591\ud55c \uc8fc\uc81c\uc758 \ub370\uc774\ud130\uc14b\uc744 \ud0d0\uc0c9\ud558\uace0 \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uacbd\uc9c4\ub300\ud68c:<\/strong> \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4 \uacbd\uc9c4\ub300\ud68c\uc5d0 \ucc38\uc5ec\ud558\uc5ec \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uace0 \uc0c1\uae08\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ucf54\ub4dc \uacf5\uc720:<\/strong> \uc720\uc800\ub4e4\uc740 \uc790\uc2e0\uc774 \uc791\uc131\ud55c \ucf54\ub4dc\ub97c \uacf5\uc720\ud558\uace0, \ub2e4\ub978 \uc0ac\ub78c\uc758 \ucf54\ub4dc\ub97c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ucee4\ubba4\ub2c8\ud2f0:<\/strong> \ub370\uc774\ud130 \uacfc\ud559\uc790\ub4e4\uacfc\uc758 \ub124\ud2b8\uc6cc\ud0b9\uc744 \ud1b5\ud574 \ud611\uc5c5\uc774\ub098 \uc9c0\uc2dd \uacf5\uc720\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \ud30c\uc774\ud1a0\uce58(PyTorch)\ub780?<\/h2>\n<p><strong>\ud30c\uc774\ud1a0\uce58<\/strong>\ub294 \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub3d9\uc801 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud558\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \ub370\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \ud2b9\ud788 \uc5f0\uad6c\uc790\ub4e4 \uc0ac\uc774\uc5d0\uc11c \uc778\uae30\uac00 \ub192\uc73c\uba70, \uc720\uc5f0\ud55c \ubaa8\ub378\ub9c1 \uae30\ub2a5\uacfc \uc26c\uc6b4 \ub514\ubc84\uae45 \ud658\uacbd\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub9ce\uc740 \ucd5c\uc2e0 \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc5d0\uc11c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud30c\uc774\ud1a0\uce58\uc758 \ud2b9\uc9d5<\/h3>\n<ul>\n<li><strong>\uc720\uc5f0\uc131:<\/strong> \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc27d\uac8c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>GPU \uc9c0\uc6d0:<\/strong> CUDA\ub97c \ud1b5\ud55c \uace0\uc18d \uc5f0\uc0b0\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uce5c\uc219\ud55c API:<\/strong> NumPy\uc640 \uc720\uc0ac\ud55c API\ub97c \uc81c\uacf5\ud558\uc5ec \uc27d\uac8c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>3. \ud30c\uc774\ud1a0\uce58\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae30\ubcf8\uc801\uc778 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \uc190\uae00\uc528 \uc22b\uc790 \uc778\uc2dd \ubb38\uc81c\ub97c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4. MNIST \ub370\uc774\ud130\uc14b\uc740 0\ubd80\ud130 9\uae4c\uc9c0\uc758 \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n<pre><code>!pip install torch torchvision<\/code><\/pre>\n<h3>3.2 \ub370\uc774\ud130\uc14b \ub85c\ub4dc\ud558\uae30<\/h3>\n<pre><code>import torch\nfrom torchvision import datasets, transforms\n\n# \ub370\uc774\ud130\uc14b \ubcc0\ud658\uae30 \uc815\uc758\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub85c\ub4dc\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\n# \ub370\uc774\ud130 \ub85c\ub354 \uc124\uc815\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)<\/code><\/pre>\n<h3>3.3 \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n<pre><code>import torch.nn as nn\nimport torch.nn.functional as F\n\nclass SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(28 * 28, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = x.view(-1, 28 * 28)\n        x = F.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x<\/code><\/pre>\n<h3>3.4 \ubaa8\ub378 \ud6c8\ub828\ud558\uae30<\/h3>\n<pre><code>model = SimpleNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n\n# \ud6c8\ub828 \ub8e8\ud504\nfor epoch in range(5):  # 5 \uc5d0\ud3ed \ub3d9\uc548 \ud6c8\ub828\n    for images, labels in train_loader:\n        optimizer.zero_grad()  # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n        outputs = model(images)  # \ubaa8\ub378 \uc608\uce21\n        loss = criterion(outputs, labels)  # \uc190\uc2e4 \uacc4\uc0b0\n        loss.backward()  # \uc5ed\uc804\ud30c\n        optimizer.step()  # \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n    print(f'Epoch [{epoch + 1}\/5], Loss: {loss.item():.4f}')<\/code><\/pre>\n<h3>3.5 \ubaa8\ub378 \ud3c9\uac00\ud558\uae30<\/h3>\n<pre><code>correct = 0\ntotal = 0\n\nwith torch.no_grad():  # \uae30\uc6b8\uae30 \uacc4\uc0b0 \ube44\ud65c\uc131\ud654\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: {100 * correct \/ total:.2f}%')<\/code><\/pre>\n<h2>4. \uacb0\ub860<\/h2>\n<p>\uce90\uae00\uc740 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4\uc640 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc911\uc694\ud55c \uc790\uc6d0\uc73c\ub85c, \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uacfc \ud559\uc2b5 \uae30\ud68c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \uc774\ub7ec\ud55c \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \uc2e4\ud5d8\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 \uae30\ubcf8\uc801\uc778 \ub370\uc774\ud130 \ub85c\ub529, \ubaa8\ub378\ub9c1, \ud6c8\ub828\uacfc \ud3c9\uac00 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uce90\uae00\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ub2e4\uc591\ud55c \ub3c4\uc804\uacfc\uc81c\ub97c \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \uc5ed\ub7c9\uc744 \ub354\uc6b1 \ud0a4\uc6cc\ubcf4\uc138\uc694!<\/p>\n<h3>5. \ucc38\uace0\uc790\ub8cc<\/h3>\n<ul>\n<li><a href=\"https:\/\/kaggle.com\/\">Kaggle \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n<li><a href=\"https:\/\/pytorch.org\/\">PyTorch \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n<li>Deep Learning with PyTorch: A 60 Minute Blitz<\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \ub180\ub77c\uc6b4 \uc18d\ub3c4\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc0c1\uc5c5\uc801 \uc751\uc6a9 \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc5f0\uad6c \ubc0f \uad50\uc721\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud750\ub984 \uc18d\uc5d0\uc11c \uc911\uc694\ud55c \ud50c\ub7ab\ud3fc \uc911 \ud558\ub098\uac00 \uce90\uae00(Kaggle)\uc785\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uce90\uae00\uc758 \uac1c\ub150, \uc5ed\ud560, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uce90\uae00(Kaggle) \uc18c\uac1c \uce90\uae00\uc740 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4 \ucee4\ubba4\ub2c8\ud2f0\ub85c, \ub370\uc774\ud130 \ubd84\uc11d, \uba38\uc2e0\ub7ec\ub2dd, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uacbd\uc7c1\ud560 \uc218 \uc788\ub294 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/30116\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00\uc774\ub780&#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-30116","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 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\ud50c\ub7ab\ud3fc \uc911 \ud558\ub098\uac00 \uce90\uae00(Kaggle)\uc785\ub2c8\ub2e4. \uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uce90\uae00\uc758 \uac1c\ub150, \uc5ed\ud560, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604 \uc608\uc81c\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uce90\uae00(Kaggle) \uc18c\uac1c \uce90\uae00\uc740 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4 \ucee4\ubba4\ub2c8\ud2f0\ub85c, \ub370\uc774\ud130 \ubd84\uc11d, \uba38\uc2e0\ub7ec\ub2dd, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \uacbd\uc7c1\ud560 \uc218 \uc788\ub294 &hellip; \ub354 \ubcf4\uae30 &quot;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uce90\uae00\uc774\ub780&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30116\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta 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