experiment.ja.json

{
 "<h1>Train a Graph Attention Network v2 (GATv2) on Cora dataset</h1>\n": "<h1>Cora \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af v2 (GATv2) \u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0</h1>\n",
 "<h2>Configurations</h2>\n<p>Since the experiment is same as <a href=\"../gat/experiment.html\">GAT experiment</a> but with <a href=\"index.html\">GATv2 model</a> we extend the same configs and change the model.</p>\n": "<h2>\u30b3\u30f3\u30d5\u30a3\u30ae\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3</h2>\n<p><a href=\"../gat/experiment.html\">\u5b9f\u9a13\u306fGAT\u5b9f\u9a13\u3068\u540c\u3058\u3067\u3059\u304c\u3001<a href=\"index.html\">GATv2\u30e2\u30c7\u30eb\u3067\u306f\u540c\u3058\u69cb\u6210\u3092\u62e1\u5f35\u3057\u3066\u30e2\u30c7\u30eb\u3092\u5909\u66f4\u3057\u307e\u3059</a></a>\u3002</p>\n",
 "<h2>Graph Attention Network v2 (GATv2)</h2>\n<p>This graph attention network has two <a href=\"index.html\">graph attention layers</a>.</p>\n": "<h2>\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af v2 (GATv2)</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",
 "<p> </p>\n": "<p></p>\n",
 "<p> Create GATv2 model</p>\n": "<p>GATv2 \u30e2\u30c7\u30eb\u306e\u4f5c\u6210</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>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>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>Dropout </p>\n": "<p>\u30c9\u30ed\u30c3\u30d7\u30a2\u30a6\u30c8</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>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>Run the training </p>\n": "<p>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3092\u5b9f\u884c</p>\n",
 "<p>Set the model </p>\n": "<p>\u30e2\u30c7\u30eb\u3092\u8a2d\u5b9a\u3059\u308b</p>\n",
 "<p>Start and watch the experiment </p>\n": "<p>\u5b9f\u9a13\u3092\u958b\u59cb\u3057\u3066\u898b\u308b</p>\n",
 "<p>Whether to share weights for source and target nodes of edges </p>\n": "<p>\u30a8\u30c3\u30b8\u306e\u30bd\u30fc\u30b9\u30ce\u30fc\u30c9\u3068\u30bf\u30fc\u30b2\u30c3\u30c8\u30ce\u30fc\u30c9\u306e\u30a6\u30a7\u30a4\u30c8\u3092\u5171\u6709\u3059\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>\n<li><span translate=no>_^_5_^_</span> if set to True, the same matrix will be applied to the source and the target node of every edge</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>\n<li><span translate=no>_^_5_^_</span>True \u306b\u8a2d\u5b9a\u3059\u308b\u3068\u3001\u3059\u3079\u3066\u306e\u30a8\u30c3\u30b8\u306e\u30bd\u30fc\u30b9\u30ce\u30fc\u30c9\u3068\u30bf\u30fc\u30b2\u30c3\u30c8\u30ce\u30fc\u30c9\u306b\u540c\u3058\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u304c\u9069\u7528\u3055\u308c\u307e\u3059</li></ul>\n",
 "This trains is a  Graph Attention Network v2 (GATv2) 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\u30afv2\uff08GATv2\uff09\u3067\u3059\u3002",
 "Train a Graph Attention Network v2 (GATv2) on Cora dataset": "Cora \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306e\u30b0\u30e9\u30d5\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3\u30cd\u30c3\u30c8\u30ef\u30fc\u30af v2 (GATv2) \u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0"
}