This is based from AdaBelief official implementation of the paper AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients.
\nThis is implemented in PyTorch as an extension to RAdam.
\nThe 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.
\n_^_0_^_\ud83e\udd14 The paper calculates variance as _^_1_^_, but I feel it should use the bias corrected momentum _^_2_^_. I guess this doesn't affect things much because bias correction is _^_3_^_ after the initial training steps.
\n": "\u3053\u308c\u306f\u3001\u300cAdabelief\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\uff1a\u89b3\u6e2c\u3055\u308c\u305f\u52fe\u914d\u3092\u4fe1\u3058\u3066\u30b9\u30c6\u30c3\u30d7\u30b5\u30a4\u30ba\u3092\u8abf\u6574\u3059\u308b\u300d\u3068\u3044\u3046\u8ad6\u6587\u306eAdableLief\u516c\u5f0f\u5b9f\u88c5\u306b\u57fa\u3065\u3044\u3066\u3044\u307e\u3059\u3002
\n\u3053\u308c\u306f RadAM \u306e\u62e1\u5f35\u6a5f\u80fd\u3068\u3057\u3066 PyTorch \u306b\u5b9f\u88c5\u3055\u308c\u3066\u3044\u307e\u3059\u3002
\nAdam \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u3068 Adabelief \u306e\u4e3b\u306a\u9055\u3044\u306f\u3001\u9069\u5fdc\u578b\u5b66\u7fd2\u7387\u306e\u8a08\u7b97\u65b9\u6cd5\u306b\u3042\u308a\u307e\u3059\u3002Adabelief \u3067\u306f\u3001\u52fe\u914d\u306e 2 \u4e57\u306e\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3067\u5272\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u6307\u6570\u95a2\u6570\u7684\u5206\u6563\u5e73\u5747\u3067\u9664\u7b97\u3055\u308c\u307e\u3059\u3002
\n_^_0_^_\ud83e\udd14 \u8ad6\u6587\u3067\u306f\u5206\u6563\u3092\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3057\u3066\u3044\u307e\u3059\u304c_^_1_^_\u3001\u30d0\u30a4\u30a2\u30b9\u88dc\u6b63\u3055\u308c\u305f\u30e2\u30e1\u30f3\u30bf\u30e0\u3092\u4f7f\u7528\u3059\u3079\u304d\u3060\u3068\u601d\u3044\u307e\u3059\u3002_^_2_^__^_3_^_\u30d0\u30a4\u30a2\u30b9\u88dc\u6b63\u306f\u6700\u521d\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30b9\u30c6\u30c3\u30d7\u306e\u5f8c\u306b\u884c\u308f\u308c\u308b\u306e\u3067\u3001\u3053\u308c\u306f\u3042\u307e\u308a\u5f71\u97ff\u3057\u306a\u3044\u3068\u601d\u3044\u307e\u3059\u3002
\n", "This class extends from RAdam optimizer defined in _^_0_^_.
\n": "\u3053\u306e\u30af\u30e9\u30b9\u306f\u3001\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b RadAM \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u3092\u62e1\u5f35\u3057\u305f\u3082\u306e\u3067\u3059\u3002_^_0_^_
\n", "_^_0_^_ and _^_1_^_ otherwise
\n": "_^_0_^__^_1_^_\u305d\u308c\u4ee5\u5916\u306f
\n", "Calculate _^_0_^_.
\n": "\u8a08\u7b97_^_0_^_\u3002
\n", "Calculate weight decay
\n": "\u4f53\u91cd\u6e1b\u5c11\u306e\u8a08\u7b97
\n", "Difference between gradient and momentum
\n": "\u52fe\u914d\u3068\u904b\u52d5\u91cf\u306e\u9055\u3044
\n", "Exponential moving average of gradient values
\n": "\u52fe\u914d\u5024\u306e\u6307\u6570\u79fb\u52d5\u5e73\u5747
\n", "Exponential moving average of variance
\n": "\u6307\u6570\u79fb\u52d5\u5e73\u5747\u504f\u5dee
\n", "Get _^_0_^_ and _^_1_^_
\n": "_^_0_^_\u53d6\u5f97\u3057\u3066 _^_1_^_
\n", "Get _^_0_^_.
\n": "\u53d6\u5f97_^_0_^_\u3002
\n", "If _^_0_^_ flag is _^_1_^_ for this parameter group, we maintain the maximum of exponential moving average of variance
\n": "_^_0_^__^_1_^_\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30b0\u30eb\u30fc\u30d7\u306b\u30d5\u30e9\u30b0\u3092\u6307\u5b9a\u3059\u308b\u3068\u3001\u6307\u6570\u79fb\u52d5\u5e73\u5747\u306e\u6700\u5927\u5206\u6563\u5024\u304c\u7dad\u6301\u3055\u308c\u307e\u3059\u3002
\n", "If this parameter group is using _^_0_^_
\n": "\u3053\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30b0\u30eb\u30fc\u30d7\u304c\u4f7f\u7528\u3057\u3066\u3044\u308b\u5834\u5408 _^_0_^_
\n", "In-place calculation of _^_0_^_ _^_1_^_
\n": "\u306e\u30a4\u30f3\u30d7\u30ec\u30fc\u30b9\u8a08\u7b97 _^_0_^_ _^_1_^_
\n", "Increment _^_0_^_ the number of optimizer steps
\n": "_^_0_^_\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u306e\u30b9\u30c6\u30c3\u30d7\u6570\u3092\u5897\u3084\u3059
\n", "Maintains max of all exp. moving avg. of sq. grad. values
\n": "\u3059\u3079\u3066\u306e\u8a31\u5bb9\u504f\u5dee\u79fb\u52d5\u5e73\u5747\u5024\u306e\u6700\u5927\u5024\u3092\u7dad\u6301
\n", "Perform Adam update, defined in _^_0_^_, with _^_1_^_ in place of _^_2_^_.
\n": "\u306e\u4ee3\u308f\u308a\u306b_^_0_^_\u3001_^_1_^_\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b Adam \u66f4\u65b0\u3092\u5b9f\u884c\u3057\u307e\u3059_^_2_^_\u3002
\n", "Perform Rectified Adam update defined in _^_0_^_, with _^_1_^_ in place of _^_2_^_.
\n": "\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u4fee\u6b63\u6e08\u307f\u306e Adam \u66f4\u65b0\u3092_^_0_^_\u3001_^_1_^_\u306e\u4ee3\u308f\u308a\u306b\u3067\u5b9f\u884c\u3057\u307e\u3059\u3002_^_2_^_
\n", "A simple PyTorch implementation/tutorial of AdaBelief optimizer.": "Adabelief \u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc\u306e\u7c21\u5358\u306a PyTorch \u5b9f\u88c5/\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u3059\u3002", "AdaBelief optimizer": "\u30a2\u30c0\u30d6\u30ea\u30ea\u30fc\u30d5\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u30fc" }