{"id":30144,"date":"2024-10-28T03:19:48","date_gmt":"2024-10-28T03:19:48","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30144"},"modified":"2024-11-26T06:49:43","modified_gmt":"2024-11-26T06:49:43","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-%ed%95%a9%ec%84%b1%ea%b3%b1-%ec%97%ad%ed%95%a9%ec%84%b1%ea%b3%b1-%eb%84%a4%ed%8a%b8%ec%9b%8c%ed%81%ac","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30144\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ud569\uc131\uacf1 & \uc5ed\ud569\uc131\uacf1 \ub124\ud2b8\uc6cc\ud06c"},"content":{"rendered":"

<\/p>\n

\n \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \ucef4\ud4e8\ud130 \ube44\uc804, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubc0f \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uc131\uacfc\ub97c \uc774\ub8e8\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional Neural Network, CNN)\uacfc \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Deconvolutional Neural Network \ub610\ub294 Transpose Convolutional Network)\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uc788\uac8c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

1. \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN) \uc18c\uac1c<\/h2>\n

\n \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubc0f \ucc98\ub9ac\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\ub294 \ub525\ub7ec\ub2dd \uae30\uc220\uc785\ub2c8\ub2e4. CNN\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \ud2b9\uc218\ud654\ub41c \uacc4\uce35\uc778 \ud569\uc131\uacf1 \uacc4\uce35(convolutional layer)\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774 \uacc4\uce35\uc740 \uc774\ubbf8\uc9c0\uc758 \uacf5\uac04\uc801 \uad6c\uc870\ub97c \ud65c\uc6a9\ud558\uc5ec \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud569\ub2c8\ub2e4.\n <\/p>\n

1.1 \ud569\uc131\uacf1 \uacc4\uce35\uc758 \uc791\ub3d9 \uc6d0\ub9ac<\/h3>\n

\n \ud569\uc131\uacf1 \uacc4\uce35\uc740 \ud544\ud130(\ub610\ub294 \ucee4\ub110)\ub97c \uc0ac\uc6a9\ud574 \uc785\ub825 \uc774\ubbf8\uc9c0\uc640\uc758 \ud569\uc131\uacf1 \uc5f0\uc0b0\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ud544\ud130\ub294 \uc774\ubbf8\uc9c0\uc758 \ud2b9\uc815 \ud2b9\uc9d5\uc744 \uac10\uc9c0\ud558\ub294 \uc791\uc740 \ud589\ub82c\ub85c, \uc774\ub7ec\ud55c \ud544\ud130\uac00 \uc5ec\ub7ec \uac1c \uc0ac\uc6a9\ub418\uc5b4 \ub2e4\uc591\ud55c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uac8c \ub429\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ud544\ud130\ub294 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc5c5\ub370\uc774\ud2b8\ub429\ub2c8\ub2e4.\n <\/p>\n

1.2 \ud569\uc131\uacf1 \uc5f0\uc0b0<\/h3>\n

\n \ud569\uc131\uacf1 \uc5f0\uc0b0\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\uc5d0 \ud544\ud130\ub97c \uc2ac\ub77c\uc774\ub529\ud558\uba74\uc11c \uacc4\uc0b0\ub429\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \uc218\uc2dd\uc73c\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4:
\n
\n\"Convolution
\n
\n \uc5ec\uae30\uc11c \\(Y\\)\ub294 \ucd9c\ub825, \\(X\\)\ub294 \uc785\ub825 \uc774\ubbf8\uc9c0, \\(K\\)\ub294 \ud544\ud130, \\(M\\)\uacfc \\(N\\)\uc740 \ud544\ud130\uc758 \ud06c\uae30\uc785\ub2c8\ub2e4.\n <\/p>\n

1.3 \ud65c\uc131\ud654 \ud568\uc218<\/h3>\n

\n \ud569\uc131\uacf1 \uc5f0\uc0b0 \ub4a4\uc5d0\ub294 \ube44\uc120\ud615\uc131\uc744 \ucd94\uac00\ud558\uae30 \uc704\ud574 \ud65c\uc131\ud654 \ud568\uc218\uac00 \uc801\uc6a9\ub429\ub2c8\ub2e4. \uc8fc\ub85c ReLU(Rectified Linear Unit) \ud568\uc218\uac00 \uc0ac\uc6a9\ub429\ub2c8\ub2e4:
\n
\n\"ReLU\n<\/p>\n

2. \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c CNN \uad6c\ud604<\/h2>\n

\n \uc774\uc81c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec CNN\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uae30\ubcf8\uc801\uc778 CNN \uad6c\uc870\uc758 \uc608\uc785\ub2c8\ub2e4.\n <\/p>\n

2.1 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n

\n \uc6b0\ub9ac\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. MNIST\ub294 \uc190\uae00\uc528 \uc22b\uc790 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c \ub370\uc774\ud130\uc14b\uc73c\ub85c, \uae30\ubcf8\uc801\uc778 \uc774\ubbf8\uc9c0 \ucc98\ub9ac \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8\ud558\uae30\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.\n <\/p>\n

\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ntransform = transforms.Compose(\n    [transforms.ToTensor(),\n     transforms.Normalize((0.5,), (0.5,))])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc\ntrainset = torchvision.datasets.MNIST(root='.\/data', train=True,\n                                        download=True, transform=transform)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=64,\n                                          shuffle=True)\ntestset = torchvision.datasets.MNIST(root='.\/data', train=False,\n                                       download=True, transform=transform)\ntestloader = torch.utils.data.DataLoader(testset, batch_size=64,\n                                         shuffle=False)\n    <\/code><\/pre>\n
2.2 CNN \ubaa8\ub378 \uc815\uc758<\/h3>\n
\n        CNN \uad6c\uc870\ub97c \uc815\uc758\ud558\ub294 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ud569\uc131\uacf1 \uacc4\uce35, \uae34\ubc00\ud55c \uc5f0\uacb0 \uacc4\uce35(fully connected layer), \uadf8\ub9ac\uace0 \ud65c\uc131\ud654 \ud568\uc218\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4.\n    <\/p>\n
\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass CNN(nn.Module):\n    def __init__(self):\n        super(CNN, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)  # \uccab \ubc88\uc9f8 \ud569\uc131\uacf1 \ub808\uc774\uc5b4\n        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # \ub9e5\uc2a4 \ud480\ub9c1 \ub808\uc774\uc5b4\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)  # \ub450 \ubc88\uc9f8 \ud569\uc131\uacf1 \ub808\uc774\uc5b4\n        self.fc1 = nn.Linear(64 * 7 * 7, 128)  # \uccab \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0 \ub808\uc774\uc5b4\n        self.fc2 = nn.Linear(128, 10)  # \ub450 \ubc88\uc9f8 \uc644\uc804 \uc5f0\uacb0 \ub808\uc774\uc5b4\n\n    def forward(self, x):\n        x = self.pool(F.relu(self.conv1(x)))  # \ud569\uc131\uacf1 -> \ud65c\uc131\ud654 -> \ud480\ub9c1\n        x = self.pool(F.relu(self.conv2(x)))  # \ud569\uc131\uacf1 -> \ud65c\uc131\ud654 -> \ud480\ub9c1\n        x = x.view(-1, 64 * 7 * 7)  # \ud150\uc11c\uc758 \ud615\ud0dc \ubcc0\ud658\n        x = F.relu(self.fc1(x))  # \uc644\uc804 \uc5f0\uacb0 -> \ud65c\uc131\ud654\n        x = self.fc2(x)  # \ucd9c\ub825\uce35\n        return x\n    <\/code><\/pre>\n
2.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\n        \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uae30 \uc704\ud574 \uc190\uc2e4 \ud568\uc218\uc640 \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nimport torch.optim as optim\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = CNN().to(device)\ncriterion = nn.CrossEntropyLoss()  # \uc190\uc2e4 \ud568\uc218\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)  # SGD \uc635\ud2f0\ub9c8\uc774\uc800\n\n# \ubaa8\ub378 \ud559\uc2b5\nfor epoch in range(10):  # 10 epochs\n    running_loss = 0.0\n    for i, data in enumerate(trainloader, 0):\n        inputs, labels = data[0].to(device), data[1].to(device)\n        \n        # \uae30\uc6b8\uae30 \ucd08\uae30\ud654\n        optimizer.zero_grad()\n        \n        # \uc21c\uc804\ud30c + \uc5ed\uc804\ud30c + \ucd5c\uc801\ud654\n        outputs = model(inputs)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n        \n        running_loss += loss.item()\n        if i % 100 == 99:    # \ub9e4 100 \ubc30\uce58\ub9c8\ub2e4 \ucd9c\ub825\n            print(f'[Epoch {epoch + 1}, Batch {i + 1}] loss: {running_loss \/ 100:.3f}')\n            running_loss = 0.0\n    print(\"Epoch finished\")\n    <\/code><\/pre>\n
2.4 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\n        \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uace0 \uc815\ud655\ub3c4\ub97c \uce21\uc815\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n    for data in testloader:\n        images, labels = data[0].to(device), data[1].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\nprint(f'Accuracy of the network on the 10000 test images: {100 * correct \/ total:.2f}%')\n    <\/code><\/pre>\n
3. \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Deconvolutional Neural Network) \uc18c\uac1c<\/h2>\n
\n        \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd \ub610\ub294 Transpose Convolutional Network\ub294 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc5d0\uc11c \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud55c \ud6c4, \uc774\ub97c \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub97c \uc7ac\uad6c\uc131\ud558\ub294 \uad6c\uc870\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc0dd\uc131 \uc791\uc5c5, \ud2b9\ud788 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(GAN)\uacfc \uac19\uc740 \ubd84\uc57c\uc5d0\uc11c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n    <\/p>\n
3.1 \uc5ed\ud569\uc131\uacf1 \uacc4\uce35\uc758 \uc791\ub3d9 \uc6d0\ub9ac<\/h3>\n
\n        \uc5ed\ud569\uc131\uacf1 \uacc4\uce35\uc740 CNN\uc758 \uc77c\ubc18\uc801\uc778 \ud569\uc131\uacf1 \uae30\ub2a5\uc744 \ubc18\ub300\ub85c \uc218\ud589\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub2c8\uae4c \uc800\ud574\uc0c1\ub3c4 \uc774\ubbf8\uc9c0\ub97c \ub354 \ub192\uc740 \ud574\uc0c1\ub3c4\ub85c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacc4\uce35\uc740 “Transpose Convolution” \ub610\ub294 “Deconvolution”\uc73c\ub85c\ub3c4 \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud544\ud130\uc758 \uacf5\uac04\uc801 \uc120\ud615 \ubcc0\ud658\uc744 \uc801\uc6a9\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.\n    <\/p>\n
3.2 \uc5ed\ud569\uc131\uacf1 \uc608\uc81c<\/h3>\n
\n        \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\ub294 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\nclass DeconvNetwork(nn.Module):\n    def __init__(self):\n        super(DeconvNetwork, self).__init__()\n        self.deconv1 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1)  # \uccab \ubc88\uc9f8 \uc5ed\ud569\uc131\uacf1 \ub808\uc774\uc5b4\n        self.deconv2 = nn.ConvTranspose2d(32, 1, kernel_size=3, stride=2, padding=1)  # \ub450 \ubc88\uc9f8 \uc5ed\ud569\uc131\uacf1 \ub808\uc774\uc5b4\n\n    def forward(self, x):\n        x = F.relu(self.deconv1(x))  # \ud65c\uc131\ud654\n        x = torch.sigmoid(self.deconv2(x))  # \ucd9c\ub825\uce35\n        return x\n    <\/code><\/pre>\n
3.3 \uc5ed\ud569\uc131\uacf1 \ub124\ud2b8\uc6cc\ud06c\ub97c \ud1b5\ud55c \uc774\ubbf8\uc9c0 \uc7ac\uad6c\uc131<\/h3>\n
\n        \uc774\ub807\uac8c \uc815\uc758\ud55c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud574 \uc774\ubbf8\uc9c0 \uc7ac\uad6c\uc131\uc758 \uae30\ucd08\uc801\uc778 \uad6c\uc870\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 GAN \ub610\ub294 Autoencoder\uc640 \uac19\uc740 \uc194\ub8e8\uc158\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\ndeconv_model = DeconvNetwork().to(device)\n\n# \uc774\ubbf8\uc9c0\ub97c \ubc30\uc5f4\uc5d0 \ucd94\uac00\nimage = torch.randn(1, 64, 7, 7).to(device)  # \uc784\uc758\uc758 \ud150\uc11c\nreconstructed_image = deconv_model(image)\nprint(reconstructed_image.shape)  # (1, 1, 28, 28)\ub85c \uc7ac\uad6c\uc131\uc774 \uac00\ub2a5\ud568\n    <\/code><\/pre>\n
4. \uacb0\ub860<\/h2>\n
\n        \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \ub450 \ud575\uc2ec \uae30\uc220\uc778 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uacfc \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Deconvolutional Network)\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58 \ud504\ub808\uc784\uc6cc\ud06c\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ub860\uacfc \uc2e4\uc2b5\uc744 \ud1b5\ud574 CNN \uad6c\uc870\ub97c \uad6c\ucd95\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uc5ed\ud569\uc131\uacf1 \ub124\ud2b8\uc6cc\ud06c\uc758 \uae30\ucd08\uc801\uc778 \uc791\ub3d9 \uc6d0\ub9ac\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\uc220\ub4e4\uc740 \ud604\uc7ac \ub9ce\uc740 \ucd5c\uc2e0 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uae30\ucd08\uac00 \ub418\uace0 \uc788\uc73c\uba70, \uc9c0\uc18d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
\n        \uc5ec\ub7ec\ubd84\uc758 \ub525\ub7ec\ub2dd \uc5ec\uc815\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub77c\uba70, \uc5b8\uc81c\ub4e0\uc9c0 \ub354 \uae4a\uc774 \uc788\ub294 \uc5f0\uad6c\uc640 \ud0d0\uad6c\ub97c \ud1b5\ud574 \uc790\uc2e0\uc758 \ubaa8\ub378\uc744 \ubc1c\uc804\uc2dc\ucf1c \ub098\uac00\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4!\n    <\/p>\n
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\ub525\ub7ec\ub2dd \uae30\uc220\uc740 \ucef4\ud4e8\ud130 \ube44\uc804, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubc0f \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uc131\uacfc\ub97c \uc774\ub8e8\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional Neural Network, CNN)\uacfc \uc5ed\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Deconvolutional Neural Network \ub610\ub294 Transpose Convolutional Network)\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uc788\uac8c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN) \uc18c\uac1c \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc740 \uc8fc\ub85c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubc0f \ucc98\ub9ac\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubcf4\uc774\ub294 \ub525\ub7ec\ub2dd \uae30\uc220\uc785\ub2c8\ub2e4. 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