\ubcf5\uc7a1\ud55c \ubaa8\ub378: \uacc4\uce35 \uad6c\uc870\ub97c \ud1b5\ud574 \ube44\uc120\ud615\uc131\uc744 \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n2. \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870<\/h2>\n
\n \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uce35(input layer), \uc5ec\ub7ec \uac1c\uc758 \uc740\ub2c9\uce35(hidden layers), \uadf8\ub9ac\uace0 \ucd9c\ub825\uce35(output layer)\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uac01 \uce35\uc740 \uc5ec\ub7ec \uac1c\uc758 \ub178\ub4dc(node)\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc73c\uba70, \uac01 \ub178\ub4dc\ub294 \ud574\ub2f9 \uce35\uc758 \ucd9c\ub825\uac12\uc744 \uacc4\uc0b0\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.\n<\/p>\n
2.1. \uad6c\uc131 \uc694\uc18c<\/h3>\n2.1.1. \ub178\ub4dc(Node)<\/h4>\n
\ub178\ub4dc\ub294 \uc785\ub825\uc744 \ubc1b\uace0, \uac00\uc911\uce58(weight)\uc640 \ud3b8\ud5a5(bias)\uc744 \uc801\uc6a9\ud558\uc5ec \ud65c\uc131\ud654 \ud568\uc218(activation function)\ub97c \ud1b5\uacfc\ud55c \ud6c4 \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
2.1.2. \ud65c\uc131\ud654 \ud568\uc218(Activation Function)<\/h4>\n
\ud65c\uc131\ud654 \ud568\uc218\ub294 \ub178\ub4dc\uc758 \ucd9c\ub825\uc744 \ube44\uc120\ud615\uc801\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ud568\uc218\ub85c, \ub300\ud45c\uc801\uc73c\ub85c Sigmoid, Tanh, ReLU(Rectified Linear Unit) \ud568\uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1.3. \uc804\ud30c(Forward Propagation)<\/h4>\n
\uc804\ud30c \uacfc\uc815\uc740 \uc785\ub825 \ub370\uc774\ud130\ub97c \ub124\ud2b8\uc6cc\ud06c\ub97c \ud1b5\uacfc\uc2dc\ucf1c \ucd9c\ub825\uc744 \uacc4\uc0b0\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ubaa8\ub4e0 \uc740\ub2c9\uce35\uc758 \ub178\ub4dc\ub4e4\uc740 \uc785\ub825\uac12\uc744 \ubc1b\uc544 \uac00\uc911\uce58 \ubc0f \ud3b8\ud5a5\uc744 \uc801\uc6a9\ud558\uace0, \ud65c\uc131\ud654 \ud568\uc218\ub97c \ud1b5\ud574 \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
2.1.4. \uc5ed\uc804\ud30c(Backward Propagation)<\/h4>\n
\uc5ed\uc804\ud30c \uacfc\uc815\uc740 \ub124\ud2b8\uc6cc\ud06c\uc758 \ucd9c\ub825\uacfc \uc2e4\uc81c \ubaa9\ud45c\uac12 \uac04\uc758 \uc624\ucc28\ub97c \uc904\uc774\uae30 \uc704\ud574 \uac00\uc911\uce58\uc640 \ud3b8\ud5a5\uc744 \uc870\uc815\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uacbd\ub7c9\ud558\uac15\ubc95(Gradient Descent)\uc744 \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n
2.2. \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \uc218\uc2dd<\/h3>\n
\uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \ucd9c\ub825\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
y = f(W * x + b)<\/code><\/pre>\n\uc5ec\uae30\uc11c y<\/code>\ub294 \ucd9c\ub825, f<\/code>\ub294 \ud65c\uc131\ud654 \ud568\uc218, W<\/code>\ub294 \uac00\uc911\uce58, x<\/code>\ub294 \uc785\ub825, b<\/code>\ub294 \ud3b8\ud5a5\uc785\ub2c8\ub2e4.<\/p>\n3. \ud30c\uc774\ud1a0\uce58 \uae30\ucd08<\/h2>\n
\n \ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \ud398\uc774\uc2a4\ubd81(\ud604\uc7ac \uba54\ud0c0)\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uac04\ud3b8\ud55c \uc0ac\uc6a9\ubc95\uacfc \ub3d9\uc801\uc778 \uacc4\uc0b0 \uadf8\ub798\ud504(Define-by-Run)\uac00 \ud2b9\uc9d5\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud1b5\ud574 \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
3.1. \uc124\uce58<\/h3>\n
\ud30c\uc774\ud1a0\uce58\ub294 pip\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc27d\uac8c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
pip install torch torchvision torchaudio<\/code><\/pre>\n3.2. \uae30\ubcf8 \ub370\uc774\ud130 \uad6c\uc870<\/h3>\n
\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ud150\uc11c(Tensor)\ub294 numpy \ubc30\uc5f4\uacfc \uc720\uc0ac\ud558\uc9c0\ub9cc GPU \uc5f0\uc0b0\uc744 \uc9c0\uc6d0\ud558\uc5ec \ub525\ub7ec\ub2dd\uc5d0 \ucd5c\uc801\ud654\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ud150\uc11c\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport torch\n\n# 1\ucc28\uc6d0 \ud150\uc11c\nx = torch.tensor([1.0, 2.0, 3.0])\nprint(x)\n\n# 2\ucc28\uc6d0 \ud150\uc11c\ny = torch.tensor([[1.0, 2.0], [3.0, 4.0]])\nprint(y)\n<\/code><\/pre>\n4. \uc2ec\uce35 \uc2e0\uacbd\ub9dd \uad6c\ud604\ud558\uae30<\/h3>\n4.1. \ub370\uc774\ud130\uc14b \uc900\ube44<\/h4>\n
\ub525\ub7ec\ub2dd\uc744 \uc2e4\uc2b5\ud558\uae30 \uc704\ud574 \ube44\uc5b4 \uc788\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. MNIST\ub294 \uc190\uc73c\ub85c \uc4f4 \uc22b\uc790 \ub370\uc774\ud130\uc14b\uc73c\ub85c, 0\ubd80\ud130 9\uae4c\uc9c0\uc758 \uc22b\uc790\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom torchvision import datasets, transforms\n\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\ntrain_dataset = datasets.MNIST(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\n<\/code><\/pre>\n4.2. \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h4>\n
\ub2e4\uc74c\uc73c\ub85c \uc2ec\uce35 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. nn.Module \ud074\ub798\uc2a4\ub97c \ud1b5\ud574 \uc0ac\uc6a9\uc790 \uc815\uc758 \uc2e0\uacbd\ub9dd \ud074\ub798\uc2a4\ub97c \uc27d\uac8c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport 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, 64)\n self.fc3 = nn.Linear(64, 10)\n\n def forward(self, x):\n x = x.view(-1, 28 * 28) # 2D \ud150\uc11c\ub97c 1D\ub85c \ud3bc\uce69\ub2c8\ub2e4.\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n x = self.fc3(x) # \ucd9c\ub825\uce35\uc740 \ud65c\uc131\ud654 \ud568\uc218\uac00 \uc5c6\uc74c\n return x\n\nmodel = SimpleNN()\n<\/code><\/pre>\n4.3. \uc190\uc2e4 \ud568\uc218 \ubc0f \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815<\/h4>\n
\ubaa8\ub378\uc758 \ud559\uc2b5\uc744 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc124\uc815\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uad50\ucc28 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4(Cross Entropy Loss)\uacfc SGD(Stochastic Gradient Descent) \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\nimport torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n<\/code><\/pre>\n4.4. \ud559\uc2b5 \ub8e8\ud504<\/h4>\n
\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud55c \ub8e8\ud504\ub97c \uc791\uc131\ud569\ub2c8\ub2e4. \uac01 \ubc30\uce58\uc5d0 \ub300\ud574 \uc804\ud30c, \uc190\uc2e4 \uacc4\uc0b0, \uc5ed\uc804\ud30c \uacfc\uc815\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\nnum_epochs = 5\n\nfor epoch in range(num_epochs):\n for images, labels in train_loader:\n # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n optimizer.zero_grad()\n \n # \uc804\ubc29\ud5a5 \uc804\ud30c\n outputs = model(images)\n \n # \uc190\uc2e4 \uacc4\uc0b0\n loss = criterion(outputs, labels)\n \n # \uc5ed\ubc29\ud5a5 \uc804\ud30c\n loss.backward()\n \n # \uac00\uc911\uce58 \uc5c5\ub370\uc774\ud2b8\n optimizer.step()\n \n print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n5. \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\n \ubaa8\ub378 \ud559\uc2b5\uc774 \uc644\ub8cc\ub418\uba74, \ud559\uc2b5\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc815\ud655\ub3c4\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \uc798 \ud559\uc2b5\ub418\uc5c8\ub294\uc9c0\ub97c \uac80\uc99d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\n# \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b \uc900\ube44\ntest_dataset = datasets.MNIST(root='.\/data', train=False, download=True, transform=transform)\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n\nmodel.eval() # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\ncorrect = 0\ntotal = 0\n\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: {100 * correct \/ total:.2f}%')\n<\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud558\uace0 \ubaa8\ub378\uc744 \uc815\uc758\ud55c \ud6c4, \uc2e4\uc81c\ub85c \ud559\uc2b5 \ubc0f \ud3c9\uac00 \uacfc\uc815\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uacfc \ud30c\uc774\ud1a0\uce58\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ubc14\ud0d5\uc73c\ub85c, \ub354 \ubcf5\uc7a1\ud55c \ub124\ud2b8\uc6cc\ud06c\ub098 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \uc2dc\ub3c4\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n<\/p>\n
\n \uc55e\uc73c\ub85c\ub3c4 \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc5d0 \ub300\ud55c \uc5f0\uad6c\uc640 \uc801\uc6a9\uc774 \uc9c0\uc18d\uc801\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c8 \uac83\uc774\uba70, \uc774\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\ub294 \ub354\uc6b1 \ubc1c\uc804\ud560 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
\n \ucd94\uac00 \uc790\ub8cc\uc640 \ucc38\uace0 \ubb38\ud5cc\uc744 \ud1b5\ud574 \ub354\uc6b1 \uae4a\uc774 \uc788\ub294 \ud559\uc2b5\uc744 \uc774\uc5b4\uac00\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \ubaa8\ub4e0 \ub3c5\uc790\ubd84\ub4e4\uc758 \uc131\uacf5\uc801\uc778 \ub525\ub7ec\ub2dd \uc5ec\uc815\uc744 \uae30\uc6d0\ud569\ub2c8\ub2e4!\n<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud55c \uc2ec\uce35 \uc2e0\uacbd\ub9dd(Deep Neural Network, DNN)\uc758 \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc740 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ub2e4\uc591\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc911\uc694\ud55c \uc694\uc18c\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc758 \uad6c\uc870, \ud559\uc2b5 \ubc29\ubc95, \uadf8\ub9ac\uace0 \ud30c\uc774\ud1a0\uce58\uc758 \uae30\ucd08\uc801\uc778 \uc0ac\uc6a9\ubc95\uc744 \ubc30\uc6b0\uac8c \ub420 \uac83\uc785\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc774\ub780? \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uc778\uacf5\uc9c0\ub2a5(Artificial Intelligence, AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc2ec\uce35 \uc2e0\uacbd\ub9dd”<\/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-30066","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uc2ec\uce35 \uc2e0\uacbd\ub9dd - \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\/30066\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" 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