experiment.ja.json

{
 "<h1>Train a Graph Attention Network (GAT) on Cora dataset</h1>\n": "<h1>Cora \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAT) \u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</h1>\n",
 "<h2><a href=\"https://linqs.soe.ucsc.edu/data\">Cora Dataset</a></h2>\n<p>Cora dataset is a dataset of research papers. For each paper we are given a binary feature vector that indicates the presence of words. Each paper is classified into one of 7 classes. The dataset also has the citation network.</p>\n<p>The papers are the nodes of the graph and the edges are the citations.</p>\n<p>The task is to classify the nodes to the 7 classes with feature vectors and citation network as input.</p>\n": "<h2><a href=\"https://linqs.soe.ucsc.edu/data\">\u30b3\u30fc\u30e9\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</a></h2>\n<p>Cora\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u7814\u7a76\u8ad6\u6587\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3059\u3002\u5404\u8ad6\u6587\u306b\u306f\u3001\u5358\u8a9e\u306e\u5b58\u5728\u3092\u793a\u3059\u30d0\u30a4\u30ca\u30ea\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u304c\u4e0e\u3048\u3089\u308c\u307e\u3059\u3002\u5404\u8ad6\u6587\u306f7\u3064\u306e\u30af\u30e9\u30b9\u306e\u3044\u305a\u308c\u304b\u306b\u5206\u985e\u3055\u308c\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u5f15\u7528\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3082\u3042\u308a\u307e\u3059</p>\u3002\n<p>\u8ad6\u6587\u306f\u30b0\u30e9\u30d5\u306e\u7bc0\u70b9\u3067\u3001\u7aef\u306f\u5f15\u7528\u3067\u3059\u3002</p>\n<p>\u30bf\u30b9\u30af\u306f\u3001\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3068\u5f15\u7528\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5165\u529b\u3068\u3057\u3066\u3001\u30ce\u30fc\u30c9\u30927\u3064\u306e\u30af\u30e9\u30b9\u306b\u5206\u985e\u3059\u308b\u3053\u3068\u3067\u3059\u3002</p>\n",
 "<h2>Configurations</h2>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n",
 "<h2>Graph Attention Network (GAT)</h2>\n<p>This graph attention network has two <a href=\"index.html\">graph attention layers</a>.</p>\n": "<h2>\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAT)</h2>\n<p>\u3053\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u306f 2 <a href=\"index.html\">\u3064\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u304c\u3042\u308a\u307e\u3059</a>\u3002</p>\n",
 "<h3>Training loop</h3>\n<p>We do full batch training since the dataset is small. If we were to sample and train we will have to sample a set of nodes for each training step along with the edges that span across those selected nodes.</p>\n": "<h3>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7</h3>\n<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u5c0f\u3055\u3044\u306e\u3067\u3001\u30d5\u30eb\u30d0\u30c3\u30c1\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u5834\u5408\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306b\u4e00\u9023\u306e\u30ce\u30fc\u30c9\u3068\u3001\u9078\u629e\u3057\u305f\u30ce\u30fc\u30c9\u306b\u307e\u305f\u304c\u308b\u30a8\u30c3\u30b8\u3092\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002</p>\n",
 "<p> </p>\n": "<p></p>\n",
 "<p> A simple function to calculate the accuracy</p>\n": "<p>\u7cbe\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u7c21\u5358\u306a\u95a2\u6570</p>\n",
 "<p> Create Cora dataset</p>\n": "<p>Cora \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f5c\u6210</p>\n",
 "<p> Create GAT model</p>\n": "<p>GAT \u30e2\u30c7\u30eb\u306e\u4f5c\u6210</p>\n",
 "<p> Create configurable optimizer</p>\n": "<p>\u8a2d\u5b9a\u53ef\u80fd\u306a\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u306e\u4f5c\u6210</p>\n",
 "<p> Download the dataset</p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9</p>\n",
 "<p> Load the dataset</p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f</p>\n",
 "<p>Activation function </p>\n": "<p>\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u6a5f\u80fd</p>\n",
 "<p>Activation function after first graph attention layer </p>\n": "<p>\u6700\u521d\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u5f8c\u306e\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u6a5f\u80fd</p>\n",
 "<p>Adam optimizer </p>\n": "<p>\u30a2\u30c0\u30e0\u30fb\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",
 "<p>Add an empty third dimension for the heads </p>\n": "<p>\u982d\u90e8\u306b\u7a7a\u306e 3 \u756a\u76ee\u306e\u30c7\u30a3\u30e1\u30f3\u30b7\u30e7\u30f3\u3092\u8ffd\u52a0</p>\n",
 "<p>Adjacency matrix with the edge information. <span translate=no>_^_0_^_</span> is <span translate=no>_^_1_^_</span> if there is an edge from <span translate=no>_^_2_^_</span> to <span translate=no>_^_3_^_</span>. </p>\n": "<p>\u30a8\u30c3\u30b8\u60c5\u5831\u3092\u542b\u3080\u96a3\u63a5\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u3002<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span><span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u3082\u3057\u3082\u304b\u3089\u7aef\u304c\u3042\u3063\u305f\u3089\u306d</p>\n",
 "<p>Apply dropout to the input </p>\n": "<p>\u5165\u529b\u306b\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8\u3092\u9069\u7528</p>\n",
 "<p>Calculate configurations. </p>\n": "<p>\u69cb\u6210\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002</p>\n",
 "<p>Calculate gradients </p>\n": "<p>\u52fe\u914d\u306e\u8a08\u7b97</p>\n",
 "<p>Calculate the loss for validation nodes </p>\n": "<p>\u691c\u8a3c\u30ce\u30fc\u30c9\u306e\u640d\u5931\u306e\u8a08\u7b97</p>\n",
 "<p>Create an experiment </p>\n": "<p>\u30c6\u30b9\u30c8\u3092\u4f5c\u6210</p>\n",
 "<p>Create configurations </p>\n": "<p>\u69cb\u6210\u306e\u4f5c\u6210</p>\n",
 "<p>Dataset </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8</p>\n",
 "<p>Device to train on</p>\n<p>This creates configs for device, so that we can change the device by passing a config value </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u30c7\u30d0\u30a4\u30b9</p>\n<p>\u3053\u308c\u306b\u3088\u308a\u30c7\u30d0\u30a4\u30b9\u306e\u8a2d\u5b9a\u304c\u4f5c\u6210\u3055\u308c\u308b\u306e\u3067\u3001\u8a2d\u5b9a\u5024\u3092\u6e21\u3059\u3053\u3068\u3067\u30c7\u30d0\u30a4\u30b9\u3092\u5909\u66f4\u3067\u304d\u307e\u3059</p>\n",
 "<p>Download dataset </p>\n": "<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9</p>\n",
 "<p>Dropout </p>\n": "<p>\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8</p>\n",
 "<p>Dropout probability </p>\n": "<p>\u8131\u843d\u78ba\u7387</p>\n",
 "<p>Empty adjacency matrix - an identity matrix </p>\n": "<p>\u7a7a\u306e\u96a3\u63a5\u884c\u5217-\u5358\u4f4d\u884c\u5217</p>\n",
 "<p>Evaluate the model </p>\n": "<p>\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1</p>\n",
 "<p>Evaluate the model again </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u518d\u5ea6\u8a55\u4fa1\u3057\u3066\u304f\u3060\u3055\u3044</p>\n",
 "<p>Feature vectors for all nodes </p>\n": "<p>\u5168\u30ce\u30fc\u30c9\u306e\u7279\u5fb4\u30d9\u30af\u30c8\u30eb</p>\n",
 "<p>Final graph attention layer where we average the heads </p>\n": "<p>\u30d8\u30c3\u30c9\u3092\u5e73\u5747\u5316\u3059\u308b\u6700\u5f8c\u306e\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30ec\u30a4\u30e4\u30fc</p>\n",
 "<p>First graph attention layer </p>\n": "<p>\u6700\u521d\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc</p>\n",
 "<p>First graph attention layer where we concatenate the heads </p>\n": "<p>\u30d8\u30c3\u30c9\u3092\u9023\u7d50\u3059\u308b\u6700\u521d\u306e\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30ec\u30a4\u30e4\u30fc</p>\n",
 "<p>Get the class names and assign an unique integer to each of them </p>\n": "<p>\u30af\u30e9\u30b9\u540d\u3092\u53d6\u5f97\u3057\u3001\u305d\u308c\u305e\u308c\u306b\u4e00\u610f\u306e\u6574\u6570\u3092\u5272\u308a\u5f53\u3066\u307e\u3059\u3002</p>\n",
 "<p>Get the feature vectors </p>\n": "<p>\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3092\u53d6\u5f97</p>\n",
 "<p>Get the labels as those integers </p>\n": "<p>\u30e9\u30d9\u30eb\u3092\u305d\u308c\u3089\u306e\u6574\u6570\u3068\u3057\u3066\u53d6\u5f97</p>\n",
 "<p>Get the loss for training nodes </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30ce\u30fc\u30c9\u3067\u640d\u5931\u3092\u88ab\u308b</p>\n",
 "<p>Get the number of classes </p>\n": "<p>\u30af\u30e9\u30b9\u6570\u3092\u53d6\u5f97</p>\n",
 "<p>Get the paper ids </p>\n": "<p>\u7d19\u306e ID \u3092\u5165\u624b</p>\n",
 "<p>Labels for each node </p>\n": "<p>\u5404\u30ce\u30fc\u30c9\u306e\u30e9\u30d9\u30eb</p>\n",
 "<p>Load the citations, it&#x27;s a list of pairs of integers. </p>\n": "<p>\u5f15\u7528\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002\u6574\u6570\u306e\u30da\u30a2\u306e\u30ea\u30b9\u30c8\u3067\u3059\u3002</p>\n",
 "<p>Log the accuracy </p>\n": "<p>\u7cbe\u5ea6\u3092\u8a18\u9332\u3059\u308b</p>\n",
 "<p>Log the loss </p>\n": "<p>\u640d\u5931\u3092\u8a18\u9332\u3059\u308b</p>\n",
 "<p>Loss function </p>\n": "<p>\u640d\u5931\u95a2\u6570</p>\n",
 "<p>Make all the gradients zero </p>\n": "<p>\u3059\u3079\u3066\u306e\u30b0\u30e9\u30c7\u30fc\u30b7\u30e7\u30f3\u3092\u30bc\u30ed\u306b\u3059\u308b</p>\n",
 "<p>Map of paper id to index </p>\n": "<p>\u7d19ID\u3068\u7d22\u5f15\u306e\u30de\u30c3\u30d7</p>\n",
 "<p>Mark the citations in the adjacency matrix </p>\n": "<p>\u5f15\u7528\u6587\u732e\u3092\u96a3\u63a5\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u306b\u8a18\u5165</p>\n",
 "<p>Model </p>\n": "<p>\u30e2\u30c7\u30eb</p>\n",
 "<p>Move the adjacency matrix to the device </p>\n": "<p>\u96a3\u63a5\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
 "<p>Move the feature vectors to the device </p>\n": "<p>\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5\u3057\u307e\u3059</p>\n",
 "<p>Move the labels to the device </p>\n": "<p>\u30e9\u30d9\u30eb\u3092\u30c7\u30d0\u30a4\u30b9\u306b\u79fb\u52d5</p>\n",
 "<p>No need to compute gradients </p>\n": "<p>\u52fe\u914d\u3092\u8a08\u7b97\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093</p>\n",
 "<p>Nodes for training </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u30ce\u30fc\u30c9</p>\n",
 "<p>Nodes for validation </p>\n": "<p>\u691c\u8a3c\u7528\u30ce\u30fc\u30c9</p>\n",
 "<p>Normalize the feature vectors </p>\n": "<p>\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3092\u6b63\u898f\u5316</p>\n",
 "<p>Number of classes for classification </p>\n": "<p>\u5206\u985e\u3059\u308b\u30af\u30e9\u30b9\u6570</p>\n",
 "<p>Number of features in the first graph attention layer </p>\n": "<p>\u6700\u521d\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u306b\u542b\u307e\u308c\u308b\u30d5\u30a3\u30fc\u30c1\u30e3\u306e\u6570</p>\n",
 "<p>Number of features in the input </p>\n": "<p>\u5165\u529b\u5185\u306e\u30d5\u30a3\u30fc\u30c1\u30e3\u306e\u6570</p>\n",
 "<p>Number of features per node in the input </p>\n": "<p>\u5165\u529b\u5185\u306e\u30ce\u30fc\u30c9\u3042\u305f\u308a\u306e\u30d5\u30a3\u30fc\u30c1\u30e3\u6570</p>\n",
 "<p>Number of heads </p>\n": "<p>\u30d8\u30c3\u30c9\u6570</p>\n",
 "<p>Number of nodes to train on </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u30ce\u30fc\u30c9\u6570</p>\n",
 "<p>Number of training iterations </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u53cd\u5fa9\u56de\u6570</p>\n",
 "<p>Optimizer </p>\n": "<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</p>\n",
 "<p>Output layer (without activation) for logits </p>\n": "<p>\u30ed\u30b8\u30c3\u30c8\u306e\u51fa\u529b\u30ec\u30a4\u30e4\u30fc (\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30b7\u30e7\u30f3\u306a\u3057)</p>\n",
 "<p>Random indexes </p>\n": "<p>\u30e9\u30f3\u30c0\u30e0\u30a4\u30f3\u30c7\u30c3\u30af\u30b9</p>\n",
 "<p>Read the paper ids, feature vectors, and labels </p>\n": "<p>\u8ad6\u6587ID\u3001\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3001\u30e9\u30d9\u30eb\u3092\u8aad\u3080</p>\n",
 "<p>Run the training </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u5b9f\u884c</p>\n",
 "<p>Save logs </p>\n": "<p>\u30ed\u30b0\u3092\u4fdd\u5b58</p>\n",
 "<p>Set mode to evaluation mode for validation </p>\n": "<p>\u691c\u8a3c\u7528\u306b\u30e2\u30fc\u30c9\u3092\u8a55\u4fa1\u30e2\u30fc\u30c9\u306b\u8a2d\u5b9a</p>\n",
 "<p>Set of class names and an unique integer index </p>\n": "<p>\u30af\u30e9\u30b9\u540d\u3068\u4e00\u610f\u306e\u6574\u6570\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306e\u30bb\u30c3\u30c8</p>\n",
 "<p>Set the model to training mode </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30e2\u30fc\u30c9\u306b\u8a2d\u5b9a</p>\n",
 "<p>Start and watch the experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u958b\u59cb\u3057\u3066\u898b\u308b</p>\n",
 "<p>Take optimization step </p>\n": "<p>\u6700\u9069\u5316\u306e\u4e00\u6b69\u3092\u8e0f\u307f\u51fa\u3059</p>\n",
 "<p>The pair of paper indexes </p>\n": "<p>\u4e00\u5bfe\u306e\u30da\u30fc\u30d1\u30fc\u30a4\u30f3\u30c7\u30c3\u30af\u30b9</p>\n",
 "<p>Training loop </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30eb\u30fc\u30d7</p>\n",
 "<p>We build a symmetrical graph, where if paper <span translate=no>_^_0_^_</span> referenced paper <span translate=no>_^_1_^_</span> we place an adge from <span translate=no>_^_2_^_</span> to <span translate=no>_^_3_^_</span> as well as an edge from <span translate=no>_^_4_^_</span> to <span translate=no>_^_5_^_</span>. </p>\n": "<p>\u5bfe\u79f0\u7684\u306a\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<span translate=no>_^_0_^_</span>\u7d19\u304c\u53c2\u7167\u3057\u3066\u3044\u308b\u7d19\u306e\u5834\u5408\u306f\u3001<span translate=no>_^_1_^_</span>\u7aef\u3092\u7aef\u304b\u3089\u7aef\u306b\u3001<span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u7aef\u3092\u7aef\u3068\u3057\u3066\u914d\u7f6e\u3057\u307e\u3059\u3002<span translate=no>_^_4_^_</span> <span translate=no>_^_5_^_</span></p>\n",
 "<p>Whether to include edges. This is test how much accuracy is lost if we ignore the citation network. </p>\n": "<p>\u30a8\u30c3\u30b8\u3092\u542b\u3081\u308b\u304b\u3069\u3046\u304b\u3002\u3053\u308c\u306f\u3001\u5f15\u7528\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7121\u8996\u3059\u308b\u3068\u7cbe\u5ea6\u304c\u3069\u308c\u3060\u3051\u5931\u308f\u308c\u308b\u304b\u3092\u30c6\u30b9\u30c8\u3059\u308b\u3082\u306e\u3067\u3059</p>\u3002\n",
 "<p>Whether to include the citation network </p>\n": "<p>\u5f15\u7528\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u542b\u3081\u308b\u304b\u3069\u3046\u304b</p>\n",
 "<ul><li><span translate=no>_^_0_^_</span> is the features vectors of shape <span translate=no>_^_1_^_</span> </li>\n<li><span translate=no>_^_2_^_</span> is the adjacency matrix of the form  <span translate=no>_^_3_^_</span> or <span translate=no>_^_4_^_</span></li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u5f62\u72b6\u306e\u7279\u5fb4\u30d9\u30af\u30c8\u30eb\u3067\u3059 <span translate=no>_^_1_^_</span></li>\n<li><span translate=no>_^_2_^_</span><span translate=no>_^_3_^_</span>\u306f\u6b21\u306e\u5f62\u5f0f\u306e\u96a3\u63a5\u884c\u5217\u3067\u3059 <span translate=no>_^_4_^_</span></li></ul>\n",
 "<ul><li><span translate=no>_^_0_^_</span> is the number of features per node </li>\n<li><span translate=no>_^_1_^_</span> is the number of features in the first graph attention layer </li>\n<li><span translate=no>_^_2_^_</span> is the number of classes </li>\n<li><span translate=no>_^_3_^_</span> is the number of heads in the graph attention layers </li>\n<li><span translate=no>_^_4_^_</span> is the dropout probability</li></ul>\n": "<ul><li><span translate=no>_^_0_^_</span>\u306f\u30ce\u30fc\u30c9\u3042\u305f\u308a\u306e\u30d5\u30a3\u30fc\u30c1\u30e3\u6570</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u6700\u521d\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30ec\u30a4\u30e4\u30fc\u306b\u542b\u307e\u308c\u308b\u30d5\u30a3\u30fc\u30c1\u30e3\u306e\u6570\u3067\u3059</li>\n<li><span translate=no>_^_2_^_</span>\u306f\u30af\u30e9\u30b9\u306e\u6570</li>\n<li><span translate=no>_^_3_^_</span>\u30b0\u30e9\u30d5\u30fb\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30fb\u30ec\u30a4\u30e4\u30fc\u306e\u30d8\u30c3\u30c9\u6570\u3067\u3059</li>\n<li><span translate=no>_^_4_^_</span>\u306f\u8131\u843d\u78ba\u7387\u3067\u3059</li></ul>\n",
 "This trains is a  Graph Attention Network (GAT) on Cora dataset": "\u3053\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306f\u3001Cora\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08GAT\uff09\u3067\u3059",
 "Train a Graph Attention Network (GAT) on Cora dataset": "Cora \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af (GAT) \u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0"
}