adam_warmup.ja.json

{
 "<h1>Adam Optimizer with Warmup</h1>\n<p>This extends <a href=\"amsgrad.html\">AMSGrad optimizer</a> and adds a warmup stage.</p>\n": "<h1>\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u6a5f\u80fd\u4ed8\u304d Adam \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</h1>\n<p>\u3053\u308c\u306b\u3088\u308a <a href=\"amsgrad.html\">AMSgrad \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u304c\u62e1\u5f35\u3055\u308c</a>\u3001\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u30b9\u30c6\u30fc\u30b8\u304c\u8ffd\u52a0\u3055\u308c\u307e\u3059\u3002</p>\n",
 "<h2>Adam Optimizer with Warmup</h2>\n<p>This class extends from AMSGrad optimizer defined in <a href=\"amsgrad.html\"><span translate=no>_^_0_^_</span></a>.</p>\n": "<h2>\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u6a5f\u80fd\u4ed8\u304d Adam \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc</h2>\n<p>\u3053\u306e\u30af\u30e9\u30b9\u306f\u3001\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b AMSGrad \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u3092\u62e1\u5f35\u3057\u305f\u3082\u306e\u3067\u3059\u3002<a href=\"amsgrad.html\"><span translate=no>_^_0_^_</span></a></p>\n",
 "<h3>Get learning-rate</h3>\n<p><span translate=no>_^_0_^_</span> where <span translate=no>_^_1_^_</span> is the number of warmup steps.</p>\n": "<h3>\u5b66\u7fd2\u7387\u3092\u53d6\u5f97</h3>\n<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u306f\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u30b9\u30c6\u30c3\u30d7\u306e\u6570\u3067\u3059\u3002</p>\n",
 "<h3>Initialize the optimizer</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the list of parameters </li>\n<li><span translate=no>_^_1_^_</span> is the learning rate <span translate=no>_^_2_^_</span> </li>\n<li><span translate=no>_^_3_^_</span> is a tuple of (<span translate=no>_^_4_^_</span>, <span translate=no>_^_5_^_</span>) </li>\n<li><span translate=no>_^_6_^_</span> is <span translate=no>_^_7_^_</span> or <span translate=no>_^_8_^_</span> based on <span translate=no>_^_9_^_</span> </li>\n<li><span translate=no>_^_10_^_</span> is an instance of class <span translate=no>_^_11_^_</span> defined in <a href=\"index.html\"><span translate=no>_^_12_^_</span></a> </li>\n<li>&#x27;optimized_update&#x27; is a flag whether to optimize the bias correction of the second moment  by doing it after adding <span translate=no>_^_13_^_</span> </li>\n<li><span translate=no>_^_14_^_</span> is a flag indicating whether to use AMSGrad or fallback to plain Adam </li>\n<li><span translate=no>_^_15_^_</span> number of warmup steps </li>\n<li><span translate=no>_^_16_^_</span> is a dictionary of default for group values.  This is useful when you want to extend the class <span translate=no>_^_17_^_</span>.</li></ul>\n": "<h3>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u3092\u521d\u671f\u5316</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u306f\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u30ea\u30b9\u30c8\u3067\u3059</li>\n<li><span translate=no>_^_1_^_</span>\u306f\u5b66\u7fd2\u7387 <span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>(,) <span translate=no>_^_4_^_</span> \u306e\u30bf\u30d7\u30eb\u3067\u3059 <span translate=no>_^_5_^_</span></li>\n<li><span translate=no>_^_6_^_</span><span translate=no>_^_7_^_</span><span translate=no>_^_8_^_</span>\u307e\u305f\u306f\u305d\u308c\u306b\u57fa\u3065\u3044\u3066\u3044\u308b <span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span><span translate=no>_^_11_^_</span>\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u30af\u30e9\u30b9\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3067\u3059 <a href=\"index.html\"><span translate=no>_^_12_^_</span></a></li>\n<li>'optimized_update'\u306f\u8ffd\u52a0\u5f8c\u306b\u884c\u3046\u3053\u3068\u3067\u30bb\u30ab\u30f3\u30c9\u30e2\u30fc\u30e1\u30f3\u30c8\u306e\u30d0\u30a4\u30a2\u30b9\u88dc\u6b63\u3092\u6700\u9069\u5316\u3059\u308b\u304b\u3069\u3046\u304b\u306e\u30d5\u30e9\u30b0\u3067\u3059 <span translate=no>_^_13_^_</span></li>\n<li><span translate=no>_^_14_^_</span>amsGrad\u3092\u4f7f\u7528\u3059\u308b\u304b\u3001\u30d7\u30ec\u30fc\u30f3\u306aAdam\u306b\u30d5\u30a9\u30fc\u30eb\u30d0\u30c3\u30af\u3059\u308b\u304b\u3092\u793a\u3059\u30d5\u30e9\u30b0\u3067\u3059</li>\n<li><span translate=no>_^_15_^_</span>\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u30b9\u30c6\u30c3\u30d7\u6570</li>\n<li><span translate=no>_^_16_^_</span>\u30b0\u30eb\u30fc\u30d7\u5024\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u8f9e\u66f8\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30af\u30e9\u30b9\u3092\u62e1\u5f35\u3059\u308b\u5834\u5408\u306b\u4fbf\u5229\u3067\u3059<span translate=no>_^_17_^_</span>\u3002</li></ul>\n",
 "<p>A linearly increasing learning rate from <span translate=no>_^_0_^_</span> to <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5b66\u7fd2\u7387\u304c 1 \u304b\u3089 <span translate=no>_^_0_^_</span> 1 \u306b\u76f4\u7dda\u7684\u306b\u5897\u52a0\u3057\u3066\u3044\u308b <span translate=no>_^_1_^_</span></p>\n",
 "<p>Constant learning rate <span translate=no>_^_0_^_</span> </p>\n": "<p>\u4e00\u5b9a\u306e\u5b66\u7fd2\u7387 <span translate=no>_^_0_^_</span></p>\n",
 "<p>If we are in warmup stage </p>\n": "<p>\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u6bb5\u968e\u306e\u5834\u5408</p>\n",
 "A simple PyTorch implementation/tutorial of Adam optimizer with warm-up.": "\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u6a5f\u80fd\u4ed8\u304d\u306e Adam \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u306e\u7c21\u5358\u306a PyTorch \u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3002",
 "Adam optimizer with warm-up": "\u30a6\u30a9\u30fc\u30e0\u30a2\u30c3\u30d7\u6a5f\u80fd\u4ed8\u304d Adam \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc"
}