{"id":30042,"date":"2024-10-28T03:19:19","date_gmt":"2024-10-28T03:19:19","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30042"},"modified":"2024-11-26T06:50:17","modified_gmt":"2024-11-26T06:50:17","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-%eb%aa%a8%eb%8d%b8-%ed%9b%88%eb%a0%a8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30042\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubaa8\ub378 \ud6c8\ub828"},"content":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \ub370\uc774\ud130 \uacfc\ud559\uacfc \uba38\uc2e0\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub77c\ub294 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc774\ub860\ubfd0\ub9cc \uc544\ub2c8\ub77c \ud30c\uc774\uc36c \ucf54\ub4dc \uc608\uc81c\uc640 \ud568\uaed8 \uc124\uba85\ud558\uc5ec \ub3c5\uc790\ub4e4\uc774 \uc2e4\uc81c\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \ud6c8\ub828\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uaca0\uc2b5\ub2c8\ub2e4. \ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc6cc\ub4dc\ud504\ub808\uc2a4 \ube14\ub85c\uadf8\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 HTML \ud615\uc2dd\uc73c\ub85c \uacb0\uacfc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h2>\n<p>\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(ANN, Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \uc9c4\ub2e8 \ub610\ub294 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc740 \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\uba70, \uc774\ub97c \ud1b5\ud574 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/p>\n<h3>1.1 \uc778\uacf5\uc2e0\uacbd\ub9dd<\/h3>\n<p>\uc778\uacf5\uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub41c \ub370\uc774\ud130 \ucc98\ub9ac \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uac01 \ub178\ub4dc\ub294 \ud2b9\uc815 \uac00\uc911\uce58\ub97c \ud560\ub2f9\ubc1b\uc544 \uc785\ub825 \uc2e0\ud638\ub97c \ucc98\ub9ac\ud558\uace0, \ud65c\uc131\ud654 \ud568\uc218\ub97c \ud1b5\uacfc\uc2dc\ucf1c \ucd9c\ub825\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uc5ec\ub7ec \uce35\uc744 \ud1b5\uacfc\ud558\uba74\uc11c \uc810\uc810 \ub354 \ubcf5\uc7a1\ud558\uace0 \ucd94\uc0c1\uc801\uc778 \ud2b9\uc131\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n<h2>2. \ud30c\uc774\ud1a0\uce58\ub780?<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58(PyTorch)\ub294 \ud398\uc774\uc2a4\ubd81 AI \ub9ac\uc11c\uce58 \uadf8\ub8f9\uc5d0\uc11c \uac1c\ubc1c\ud55c \uc624\ud508 \uc18c\uc2a4 \uba38\uc2e0\ub7ec\ub2dd \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub294 \ud2b9\ud788 \ub525\ub7ec\ub2dd \uc5f0\uad6c\uc640 \ud504\ub85c\ud1a0\ud0c0\uc785 \uac1c\ubc1c\uc5d0 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ud150\uc11c(Tensor) \uc5f0\uc0b0 \ubc0f \uc790\ub3d9 \ubbf8\ubd84 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uc5ec \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uc744 \uc190\uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ud30c\uc774\ud1a0\uce58\uc758 \uc7a5\uc810<\/h3>\n<ul>\n<li>\ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504: \ucf54\ub4dc \uc2e4\ud589 \uc911\uc5d0 \uadf8\ub798\ud504\ub97c \uc0dd\uc131\ud558\uc5ec \ub354 \uc720\uc5f0\ud558\uac8c \ubaa8\ub378\uc744 \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li>\uc5ec\ub7ec GPU \uc9c0\uc6d0: \ud30c\uc774\ud1a0\uce58\ub294 \ub2e4\uc218\uc758 GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0\uc5d0\ub3c4 \ud6a8\uacfc\uc801\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4.<\/li>\n<li>\ud65c\ubc1c\ud55c \ucee4\ubba4\ub2c8\ud2f0: \ud48d\ubd80\ud55c \ubb38\uc11c\uc640 \ub2e4\uc591\ud55c \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc774 \uc788\uc5b4 \ud559\uc2b5\uc774 \uc6a9\uc774\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>3. \ubaa8\ub378 \ud6c8\ub828\uc758 \uac1c\uc694<\/h2>\n<p>\ubaa8\ub378 \ud6c8\ub828 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n<ol>\n<li><strong>\ub370\uc774\ud130 \uc900\ube44:<\/strong> \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ubaa8\ub378 \uc815\uc758:<\/strong> \uc0ac\uc6a9\ud560 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc758 \uad6c\uc870\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815:<\/strong> \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12 \uac04\uc758 \ucc28\uc774\ub97c \uacc4\uc0b0\ud560 \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud558\uace0, \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud560 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ud6c8\ub828 \ub8e8\ud504:<\/strong> \uc804\uccb4 \ub370\uc774\ud130\uc14b\uc744 \ubc18\ubcf5\ud558\uba74\uc11c \ubaa8\ub378\uc744 \ud6c8\ub828\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ubaa8\ub378 \ud3c9\uac00:<\/strong> \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h2>4. \uc2e4\uc2b5: \uac04\ub2e8\ud55c \ubd84\ub958 \ubaa8\ub378 \ud6c8\ub828<\/h2>\n<p>\uc774\uc81c \uc2e4\uc81c\ub85c \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc774\ubbf8\uc9c0 \ubd84\ub958 \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ucf1c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b(\uc190\uc73c\ub85c \uc4f4 \uc22b\uc790 \ub370\uc774\ud130\uc14b)\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n<p>\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud558\uc138\uc694:<\/p>\n<pre><code>pip install torch torchvision<\/code><\/pre>\n<h3>4.2 \ub370\uc774\ud130\uc14b \ubd88\ub7ec\uc624\uae30<\/h3>\n<p>PyTorch\uc758 torchvision \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec MNIST \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uba3c\uc800\ub294 \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>import torch\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import MNIST\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ntransform = transforms.Compose([\n    transforms.ToTensor(),  # \uc774\ubbf8\uc9c0\ub97c \ud150\uc11c\ub85c \ubcc0\ud658\n    transforms.Normalize((0.5,), (0.5,))  # \uc815\uaddc\ud654\n])\n\n# MNIST \ub370\uc774\ud130\uc14b \ub2e4\uc6b4\ub85c\ub4dc \ubc0f \ub85c\ub4dc\ntrain_dataset = MNIST(root='.\/data', train=True, download=True, transform=transform)\ntest_dataset = MNIST(root='.\/data', train=False, download=True, transform=transform)\n\n# \ub370\uc774\ud130 \ub85c\ub354 \uc124\uc815\ntrain_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)\ntest_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)\n<\/code><\/pre>\n<h3>4.3 \ubaa8\ub378 \uc815\uc758<\/h3>\n<p>\ub2e4\uc74c\uc73c\ub85c, \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55cFully Connected Neural Network(FCNN)\ub97c \uad6c\ucd95\ud569\ub2c8\ub2e4.<\/p>\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)  # \uc785\ub825\uce35\n        self.fc2 = nn.Linear(128, 64)        # \uc740\ub2c9\uce35\n        self.fc3 = nn.Linear(64, 10)         # \ucd9c\ub825\uce35\n\n    def forward(self, x):\n        x = x.view(-1, 28 * 28)  # \uc774\ubbf8\uc9c0\ub97c 1\ucc28\uc6d0\uc73c\ub85c \ubcc0\ud658\n        x = F.relu(self.fc1(x))  # ReLU \ud65c\uc131\ud654 \ud568\uc218\n        x = F.relu(self.fc2(x))  # ReLU \ud65c\uc131\ud654 \ud568\uc218\n        x = self.fc3(x)          # \ucd9c\ub825\n        return x\n<\/code><\/pre>\n<h3>4.4 \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998 \uc124\uc815<\/h3>\n<p>\uc190\uc2e4 \ud568\uc218\ub85c\ub294 Cross Entropy Loss\ub97c \uc0ac\uc6a9\ud558\uace0, \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 Stochastic Gradient Descent(SGD)\ub97c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>model = SimpleNN()  # \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4 \uc0dd\uc131\ncriterion = nn.CrossEntropyLoss()  # \uc190\uc2e4 \ud568\uc218\noptimizer = torch.optim.SGD(model.parameters(), lr=0.01)  # \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\n<\/code><\/pre>\n<h3>4.5 \ud6c8\ub828 \ub8e8\ud504 \uad6c\ud604<\/h3>\n<p>\ud6c8\ub828 \ub8e8\ud504\ub97c \uad6c\ud604\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud569\ub2c8\ub2e4. \uc5ec\ub7ec \uc5d0\ud3ed(epoch)\uc5d0 \uac78\uccd0 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>num_epochs = 5  # \uc5d0\ud3ed \uc218\n\nfor epoch in range(num_epochs):\n    for i, (images, labels) in enumerate(train_loader):\n        # \ubaa8\ub378\uc744 0\uc73c\ub85c \ucd08\uae30\ud654\n        optimizer.zero_grad()\n\n        # \uc21c\uc804\ud30c\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n\n        # \uc5ed\uc804\ud30c \ubc0f \ucd5c\uc801\ud654\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>4.6 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>model.eval()  # \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc804\ud658\nwith torch.no_grad():  # \uadf8\ub798\ub514\uc5b8\ud2b8 \uacc4\uc0b0\uc744 \ud558\uc9c0 \uc54a\uc74c\n    correct = 0\n    total = 0\n    for images, labels in test_loader:\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)  # \uc608\uce21\uac12\n        total += labels.size(0)\n        correct += (predicted == labels).sum().item()\n\nprint(f'Accuracy of the model on the test images: {100 * correct \/ total:.2f}%')\n<\/code><\/pre>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\uc774 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828 \uacfc\uc815\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uc600\uace0, \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \ubd84\ub958 \ubaa8\ub378\uc744 \uc2e4\uc81c\ub85c \ud6c8\ub828\uc2dc\ud0a4\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\uc81c \uc5ec\ub7ec\ubd84\uc740 \ud30c\uc774\ud1a0\uce58\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc5b4\ub5bb\uac8c \uad6c\uc131\ud558\uace0 \ud6c8\ub828\ud558\ub294\uc9c0 \uc774\ud574\ud558\uac8c \ub418\uc5c8\uc744 \uac83\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc774\ub098 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8e8\uba74\uc11c \ub525\ub7ec\ub2dd\uc758 \uae4a\uc774\ub97c \ub354\ud574\uac00\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<p>\uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc774 \ub525\ub7ec\ub2dd\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ud655\uc7a5\ud558\uace0, \uc2e4\uc9c8\uc801\uc778 \uacbd\ud5d8\uc744 \uc313\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc9c8\ubb38\uc774\ub098 \uc758\uacac\uc774 \uc788\uc73c\uc2dc\uba74 \ub313\uae00\ub85c \ub0a8\uaca8\uc8fc\uc138\uc694!<\/p>\n<h2>6. \ucc38\uace0\uc790\ub8cc<\/h2>\n<ul>\n<li><a href=\"https:\/\/pytorch.org\/docs\/stable\/index.html\">PyTorch Documentation<\/a><\/li>\n<li><a href=\"http:\/\/yann.lecun.com\/exdb\/mnist\/\">MNIST Dataset<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \ub370\uc774\ud130 \uacfc\ud559\uacfc \uba38\uc2e0\ub7ec\ub2dd \ubd84\uc57c\uc5d0\uc11c \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub77c\ub294 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc774\ub860\ubfd0\ub9cc \uc544\ub2c8\ub77c \ud30c\uc774\uc36c \ucf54\ub4dc \uc608\uc81c\uc640 \ud568\uaed8 \uc124\uba85\ud558\uc5ec \ub3c5\uc790\ub4e4\uc774 \uc2e4\uc81c\ub85c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \ud6c8\ub828\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uaca0\uc2b5\ub2c8\ub2e4. \ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc6cc\ub4dc\ud504\ub808\uc2a4 \ube14\ub85c\uadf8\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 HTML \ud615\uc2dd\uc73c\ub85c \uacb0\uacfc\ub97c \uc81c\uacf5\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(ANN, &hellip; <a 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