ada_belief.zh.json

{
 "<h1>AdaBelief Optimizer</h1>\n<p>This is based from AdaBelief <a href=\"https://github.com/juntang-zhuang/Adabelief-Optimizer\">official implementation</a> of the paper <a href=\"https://arxiv.org/abs/2010.07468\">AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients</a>.</p>\n<p>This is implemented in <a href=\"https://pytorch.org\">PyTorch</a> as an extension to <a href=\"radam.html\">RAdam</a>.</p>\n<p>The main difference between Adam optimizer and AdaBelief is that, how it calculates the adaptive learning rate; instead of dividing by the exponential moving average of square of the gradients, AdaBelief divides by the exponential mean of variance.</p>\n<span translate=no>_^_0_^_</span><p>\ud83e\udd14 The paper calculates variance as <span translate=no>_^_1_^_</span>, but I feel it should use the bias corrected momentum <span translate=no>_^_2_^_</span>. I guess this doesn&#x27;t affect things much because bias correction is <span translate=no>_^_3_^_</span> after the initial training steps.</p>\n": "<h1>adaBelief \u4f18\u5316\u5668</h1>\n<p>\u8fd9\u662f\u57fa\u4e8e AdaBeLief Optimizer \u8bba\u6587<a href=\"https://github.com/juntang-zhuang/Adabelief-Optimizer\">\u300a<a href=\"https://arxiv.org/abs/2010.07468\">AdaBeLief Optimizer\uff1a\u901a\u8fc7\u5bf9\u89c2\u5bdf\u5230\u7684\u68af\u5ea6\u7684\u4fe1\u5ff5\u8c03\u6574\u6b65\u957f\u300b</a>\u7684\u5b98\u65b9\u5b9e\u73b0</a>\u3002</p>\n<p>\u8fd9\u662f\u5728 <a href=\"https://pytorch.org\">PyTorch</a> \u4e2d\u4f5c\u4e3a\u5bf9 <a href=\"radam.html\">RadAM</a> \u7684\u6269\u5c55\u5b9e\u73b0\u7684\u3002</p>\n<p>Adam optimizer \u548c AdaBeLief \u4e4b\u95f4\u7684\u4e3b\u8981\u533a\u522b\u5728\u4e8e\uff0c\u5b83\u5982\u4f55\u8ba1\u7b97\u81ea\u9002\u5e94\u5b66\u4e60\u7387\uff1bAdaBeLief \u4e0d\u662f\u9664\u4ee5\u68af\u5ea6\u5e73\u65b9\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747\u503c\uff0c\u800c\u662f\u9664\u4ee5\u65b9\u5dee\u7684\u6307\u6570\u5747\u503c\u3002</p>\n<span translate=no>_^_0_^_</span><p>\ud83e\udd14 \u672c\u6587\u5c06\u65b9\u5dee\u8ba1\u7b97\u4e3a<span translate=no>_^_1_^_</span>\uff0c\u4f46\u6211\u8ba4\u4e3a\u5b83\u5e94\u8be5\u4f7f\u7528\u504f\u5dee\u6821\u6b63\u7684\u52a8\u91cf<span translate=no>_^_2_^_</span>\u3002\u6211\u60f3\u8fd9\u5bf9\u4e8b\u60c5\u7684\u5f71\u54cd\u4e0d\u5927\uff0c\u56e0\u4e3a\u504f\u5dee\u6821\u6b63\u662f\u5728\u6700\u521d\u7684\u8bad\u7ec3\u6b65\u9aa4<span translate=no>_^_3_^_</span>\u4e4b\u540e\u8fdb\u884c\u7684\u3002</p>\n",
 "<h2>AdaBelief Optimizer</h2>\n<p>This class extends from RAdam optimizer defined in <a href=\"radam.html\"><span translate=no>_^_0_^_</span></a>.</p>\n": "<h2>adaBelief \u4f18\u5316\u5668</h2>\n<p>\u8fd9\u4e2a\u7c7b\u662f\u4ece\u4e2d\u5b9a\u4e49\u7684 RadAM \u4f18\u5316\u5668\u6269\u5c55\u800c\u6765\u7684<a href=\"radam.html\"><span translate=no>_^_0_^_</span></a>\u3002</p>\n",
 "<h3>Calculate <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> or <span translate=no>_^_2_^_</span></h3>\n<ul><li><span translate=no>_^_3_^_</span> is the optimizer state of the parameter (tensor) </li>\n<li><span translate=no>_^_4_^_</span> stores optimizer attributes of the parameter group </li>\n<li><span translate=no>_^_5_^_</span> is the current gradient tensor <span translate=no>_^_6_^_</span> for the parameter <span translate=no>_^_7_^_</span></li></ul>\n": "<h3>\u8ba1\u7b97<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span>\u6216<span translate=no>_^_2_^_</span></h3>\n<ul><li><span translate=no>_^_3_^_</span>\u662f\u53c2\u6570\uff08\u5f20\u91cf\uff09\u7684\u4f18\u5316\u5668\u72b6\u6001</li>\n<li><span translate=no>_^_4_^_</span>\u5b58\u50a8\u53c2\u6570\u7ec4\u7684\u4f18\u5316\u7a0b\u5e8f\u5c5e\u6027</li>\n<li><span translate=no>_^_5_^_</span>\u662f\u53c2\u6570\u7684\u5f53\u524d\u68af<span translate=no>_^_6_^_</span>\u5ea6\u5f20\u91cf<span translate=no>_^_7_^_</span></li></ul>\n",
 "<h3>Initialize a parameter state</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the optimizer state of the parameter (tensor) </li>\n<li><span translate=no>_^_1_^_</span> stores optimizer attributes of the parameter group </li>\n<li><span translate=no>_^_2_^_</span> is the parameter tensor <span translate=no>_^_3_^_</span></li></ul>\n": "<h3>\u521d\u59cb\u5316\u53c2\u6570\u72b6\u6001</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u53c2\u6570\uff08\u5f20\u91cf\uff09\u7684\u4f18\u5316\u5668\u72b6\u6001</li>\n<li><span translate=no>_^_1_^_</span>\u5b58\u50a8\u53c2\u6570\u7ec4\u7684\u4f18\u5316\u7a0b\u5e8f\u5c5e\u6027</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u53c2\u6570\u5f20\u91cf<span translate=no>_^_3_^_</span></li></ul>\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><span translate=no>_^_13_^_</span> is a flag whether to optimize the bias correction of the second moment  by doing it after adding <span translate=no>_^_14_^_</span> </li>\n<li><span translate=no>_^_15_^_</span> is a flag indicating whether to use AMSGrad or fallback to plain Adam </li>\n<li><span translate=no>_^_16_^_</span> whether to use sgd when the rectification term <span translate=no>_^_17_^_</span> is intractable </li>\n<li><span translate=no>_^_18_^_</span> is whether to use RAdam update </li>\n<li><span translate=no>_^_19_^_</span> is a dictionary of default for group values.  This is useful when you want to extend the class <span translate=no>_^_20_^_</span>.</li></ul>\n": "<h3>\u521d\u59cb\u5316\u4f18\u5316\u5668</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u53c2\u6570\u5217\u8868</li>\n<li><span translate=no>_^_1_^_</span>\u662f\u5b66\u4e60\u7387<span translate=no>_^_2_^_</span></li>\n<li><span translate=no>_^_3_^_</span>\u662f (<span translate=no>_^_4_^_</span>,<span translate=no>_^_5_^_</span>) \u7684\u5143\u7ec4</li>\n<li><span translate=no>_^_6_^_</span>\u662f<span translate=no>_^_7_^_</span>\u6216<span translate=no>_^_8_^_</span>\u57fa\u4e8e<span translate=no>_^_9_^_</span></li>\n<li><span translate=no>_^_10_^_</span>\u662f\u5728\u4e2d<span translate=no>_^_11_^_</span>\u5b9a\u4e49\u7684\u7c7b\u7684\u5b9e\u4f8b <a href=\"index.html\"><span translate=no>_^_12_^_</span></a></li>\n<li><span translate=no>_^_13_^_</span>\u662f\u4e00\u4e2a\u6807\u5fd7\uff0c\u662f\u5426\u5728\u6dfb\u52a0\u540e\u901a\u8fc7\u8fd9\u6837\u505a\u6765\u4f18\u5316\u7b2c\u4e8c\u4e2a\u65f6\u523b\u7684\u504f\u5dee\u6821\u6b63<span translate=no>_^_14_^_</span></li>\n<li><span translate=no>_^_15_^_</span>\u662f\u4e00\u4e2a\u6807\u5fd7\uff0c\u6307\u793a\u662f\u4f7f\u7528 AmsGrad \u8fd8\u662f\u56de\u9000\u5230\u666e\u901a\u7684 Adam</li>\n<li><span translate=no>_^_16_^_</span>\u7ea0\u6b63\u6761\u6b3e<span translate=no>_^_17_^_</span>\u96be\u4ee5\u5904\u7406\u65f6\u662f\u5426\u4f7f\u7528 sgd</li>\n<li><span translate=no>_^_18_^_</span>\u662f\u5426\u4f7f\u7528 raDAM \u66f4\u65b0</li>\n<li><span translate=no>_^_19_^_</span>\u662f\u7ec4\u503c\u7684\u9ed8\u8ba4\u5b57\u5178\u3002\u5f53\u4f60\u60f3\u6269\u5c55\u7c7b\u65f6\uff0c\u8fd9\u5f88\u6709\u7528<span translate=no>_^_20_^_</span>\u3002</li></ul>\n",
 "<h3>Take an update step for a given parameter tensor</h3>\n<ul><li><span translate=no>_^_0_^_</span> is the optimizer state of the parameter (tensor) </li>\n<li><span translate=no>_^_1_^_</span> stores optimizer attributes of the parameter group </li>\n<li><span translate=no>_^_2_^_</span> is the current gradient tensor <span translate=no>_^_3_^_</span> for the parameter <span translate=no>_^_4_^_</span> </li>\n<li><span translate=no>_^_5_^_</span> is the parameter tensor <span translate=no>_^_6_^_</span></li></ul>\n": "<h3>\u5bf9\u7ed9\u5b9a\u53c2\u6570\u5f20\u91cf\u6267\u884c\u66f4\u65b0\u6b65\u9aa4</h3>\n<ul><li><span translate=no>_^_0_^_</span>\u662f\u53c2\u6570\uff08\u5f20\u91cf\uff09\u7684\u4f18\u5316\u5668\u72b6\u6001</li>\n<li><span translate=no>_^_1_^_</span>\u5b58\u50a8\u53c2\u6570\u7ec4\u7684\u4f18\u5316\u7a0b\u5e8f\u5c5e\u6027</li>\n<li><span translate=no>_^_2_^_</span>\u662f\u53c2\u6570\u7684\u5f53\u524d\u68af<span translate=no>_^_3_^_</span>\u5ea6\u5f20\u91cf<span translate=no>_^_4_^_</span></li>\n<li><span translate=no>_^_5_^_</span>\u662f\u53c2\u6570\u5f20\u91cf<span translate=no>_^_6_^_</span></li></ul>\n",
 "<p><span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> otherwise </p>\n": "<p><span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span>\u5426\u5219</p>\n",
 "<p>Calculate <span translate=no>_^_0_^_</span>. </p>\n": "<p>\u8ba1\u7b97<span translate=no>_^_0_^_</span>\u3002</p>\n",
 "<p>Calculate weight decay </p>\n": "<p>\u8ba1\u7b97\u4f53\u91cd\u8870\u51cf</p>\n",
 "<p>Difference between gradient and momentum </p>\n": "<p>\u68af\u5ea6\u548c\u52a8\u91cf\u4e4b\u95f4\u7684\u533a\u522b</p>\n",
 "<p>Exponential moving average of gradient values </p>\n": "<p>\u68af\u5ea6\u503c\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf</p>\n",
 "<p>Exponential moving average of variance </p>\n": "<p>\u65b9\u5dee\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf</p>\n",
 "<p>Get <span translate=no>_^_0_^_</span> and <span translate=no>_^_1_^_</span> </p>\n": "<p>\u83b7\u53d6<span translate=no>_^_0_^_</span>\u548c<span translate=no>_^_1_^_</span></p>\n",
 "<p>Get <span translate=no>_^_0_^_</span>. </p>\n": "<p>\u5f97\u5230<span translate=no>_^_0_^_</span>\u3002</p>\n",
 "<p>If <span translate=no>_^_0_^_</span> flag is <span translate=no>_^_1_^_</span> for this parameter group, we maintain the maximum of exponential moving average of variance </p>\n": "<p>\u5982\u679c f<span translate=no>_^_0_^_</span> lag<span translate=no>_^_1_^_</span> \u7528\u4e8e\u6b64\u53c2\u6570\u7ec4\uff0c\u5219\u6211\u4eec\u7ef4\u6301\u65b9\u5dee\u7684\u6307\u6570\u79fb\u52a8\u5e73\u5747\u7ebf\u7684\u6700\u5927\u503c</p>\n",
 "<p>If this parameter group is using <span translate=no>_^_0_^_</span> </p>\n": "<p>\u5982\u679c\u6b64\u53c2\u6570\u7ec4\u6b63\u5728\u4f7f\u7528<span translate=no>_^_0_^_</span></p>\n",
 "<p>In-place calculation of <span translate=no>_^_0_^_</span> <span translate=no>_^_1_^_</span> </p>\n": "<p>\u5c31\u5730\u8ba1\u7b97<span translate=no>_^_0_^_</span><span translate=no>_^_1_^_</span></p>\n",
 "<p>Increment <span translate=no>_^_0_^_</span> the number of optimizer steps </p>\n": "<p><span translate=no>_^_0_^_</span>\u589e\u52a0\u4f18\u5316\u5668\u6b65\u6570</p>\n",
 "<p>Maintains max of all exp. moving avg. of sq. grad. values </p>\n": "<p>\u4fdd\u6301\u6240\u6709 exp. \u79fb\u52a8\u5e73\u5747 sq. grad. \u503c\u7684\u6700\u5927\u503c</p>\n",
 "<p>Perform <em>Adam</em> update, defined in <a href=\"adam.html\"><span translate=no>_^_0_^_</span></a>, with <span translate=no>_^_1_^_</span> in place of <span translate=no>_^_2_^_</span>. </p>\n": "<p>\u6267\u884c <em>Adam</em> \u66f4\u65b0\uff0c\u5728\u4e2d\u5b9a\u4e49 <a href=\"adam.html\"><span translate=no>_^_0_^_</span></a>\uff0c\u7528<span translate=no>_^_1_^_</span>\u4ee3\u66ff<span translate=no>_^_2_^_</span>\u3002</p>\n",
 "<p>Perform <em>Rectified Adam</em> update defined in <a href=\"radam.html\"><span translate=no>_^_0_^_</span></a>, with <span translate=no>_^_1_^_</span> in place of <span translate=no>_^_2_^_</span>. </p>\n": "<p>\u6267\u884c\u4e2d\u5b9a\u4e49\u7684\u5df2<em>\u6821\u6b63\u7684 Adam</em> \u66f4\u65b0 <a href=\"radam.html\"><span translate=no>_^_0_^_</span></a><span translate=no>_^_1_^_</span>\uff0c\u7528\u4ee3\u66ff<span translate=no>_^_2_^_</span>\u3002</p>\n",
 "A simple PyTorch implementation/tutorial of AdaBelief optimizer.": "AdaBeLief \u4f18\u5316\u5668\u7684\u7b80\u5355\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u3002",
 "AdaBelief optimizer": "adaBeLief \u4f18\u5316\u5668"
}