\n pip install torch torchvision\n <\/code>\n <\/pre>\n4.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
MNIST \ub370\uc774\ud130\uc14b\uc740 \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c \ub370\uc774\ud130\uc14b\uc785\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n \n import torch\n from torchvision import datasets, transforms\n\n transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5,), (0.5,))\n ])\n\n trainset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\n trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n <\/code>\n <\/pre>\n4.3 \ubaa8\ub378 \uc815\uc758<\/h3>\n
\ub2e4\uc74c\uc73c\ub85c \uac04\ub2e8\ud55c \uc778\uacf5\uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
\n \n import torch.nn as nn\n import torch.nn.functional as F\n\n class 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, 64)\n self.fc3 = nn.Linear(64, 10)\n\n def forward(self, x):\n x = x.view(-1, 28 * 28) # Flatten the input\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\n model = SimpleNN()\n <\/code>\n <\/pre>\n4.4 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800 \uc815\uc758<\/h3>\n
\ubaa8\ub378\uc758 \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0 \uc5c5\ub370\uc774\ud2b8\ud558\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
\n \n criterion = nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n <\/code>\n <\/pre>\n4.5 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c \ud559\uc2b5 \ub8e8\ud504\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
\n \n epochs = 5\n for epoch in range(epochs):\n for images, labels in trainloader:\n optimizer.zero_grad() # Zero the gradients\n output = model(images) # Forward pass\n loss = criterion(output, labels) # Calculate loss\n loss.backward() # Backward pass\n optimizer.step() # Update weights\n\n print(f'Epoch {epoch+1}\/{epochs}, Loss: {loss.item()}')\n <\/code>\n <\/pre>\n4.6 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n \n testset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\n testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)\n\n correct = 0\n total = 0\n with torch.no_grad():\n for images, labels in testloader:\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: {100 * correct \/ total}%')\n <\/code>\n <\/pre>\n5. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uc740 \ub9ce\uc740 \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uc77c\uc73c\ud0a4\uace0 \uc788\uc73c\uba70, \ud30c\uc774\ud1a0\uce58\ub294 \uadf8 \uad6c\ud604\uc5d0 \uc788\uc5b4 \ub9e4\uc6b0 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud568\uaed8 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub2e4\uc591\ud55c \ud504\ub85c\uc81d\ud2b8\ub97c \ud1b5\ud574 \uc2e4\ub825\uc744 \uc313\uc544\uac00\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd(ML)\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uac04\uc758 \ub1cc \uad6c\uc870\ub97c \ubaa8\ubc29\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud559\uc2b5 \uacfc\uc815\uc744 \ud1b5\ud574 \ucef4\ud4e8\ud130\uac00 \uc0ac\ub78c\uacfc \ube44\uc2b7\ud558\uac8c \uc778\uc2dd\ud558\uace0 \ud310\ub2e8\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc758 \uc5ed\uc0ac \ub525\ub7ec\ub2dd\uc758 \uac1c\ub150\uc740 1940\ub144\ub300\uc640 1950\ub144\ub300\uc5d0 \uae30\uc6d0\ud569\ub2c8\ub2e4. \uc774 \uc2dc\uae30\uc5d0 \ud37c\uc149\ud2b8\ub860(Perceptron)\uc774\ub77c\ub294 \uc2e0\uacbd\ub9dd \uae30\ubc95\uc774 \uc81c\uc548\ub418\uc5c8\uace0, \uc774\ub294 \uae30\uacc4\uac00 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uac04\ub2e8\ud55c \ubaa8\ub378 \uc911 \ud558\ub098\uc600\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ucd08\uae30\uc758 \ud55c\uacc4\ub85c … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd\uc774\ub780”<\/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-30030","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd\uc774\ub780 - \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\/30030\/\" \/>\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, \ub525\ub7ec\ub2dd\uc774\ub780 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd(ML)\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uac04\uc758 \ub1cc \uad6c\uc870\ub97c \ubaa8\ubc29\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud559\uc2b5 \uacfc\uc815\uc744 \ud1b5\ud574 \ucef4\ud4e8\ud130\uac00 \uc0ac\ub78c\uacfc \ube44\uc2b7\ud558\uac8c \uc778\uc2dd\ud558\uace0 \ud310\ub2e8\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc758 \uc5ed\uc0ac \ub525\ub7ec\ub2dd\uc758 \uac1c\ub150\uc740 1940\ub144\ub300\uc640 1950\ub144\ub300\uc5d0 \uae30\uc6d0\ud569\ub2c8\ub2e4. \uc774 \uc2dc\uae30\uc5d0 \ud37c\uc149\ud2b8\ub860(Perceptron)\uc774\ub77c\ub294 \uc2e0\uacbd\ub9dd \uae30\ubc95\uc774 \uc81c\uc548\ub418\uc5c8\uace0, \uc774\ub294 \uae30\uacc4\uac00 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \uac04\ub2e8\ud55c \ubaa8\ub378 \uc911 \ud558\ub098\uc600\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ucd08\uae30\uc758 \ud55c\uacc4\ub85c … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd\uc774\ub780"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/30030\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:19:14+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:21+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/30030\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30030\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd\uc774\ub780\",\"datePublished\":\"2024-10-28T03:19:14+00:00\",\"dateModified\":\"2024-11-26T06:50:21+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/30030\/\"},\"wordCount\":28,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/30030\/\",\"url\":\"https:\/\/atmokpo.com\/w\/30030\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ub525\ub7ec\ub2dd\uc774\ub780 - 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