{"height":"190","author_url":"https://blog.hatena.ne.jp/end0tknr/","categories":[],"description":"github.com \u300c\u30bc\u30ed\u304b\u3089\u4f5c\u308bDeep Learning \u2460 (Python\u3067\u5b66\u3076\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u7406\u8ad6\u3068\u5b9f\u88c5)\u300d p.147\uff5e154\u306e\u5199\u7d4c\u3068\u3057\u3066\u3001\u4ee5\u4e0b\u306e\u6731\u66f8\u304d\u90e8\u5206\u3092\u8a08\u7b97\u30b0\u30e9\u30d5\u5316\u3057\u307e\u3059\u3002 \u76ee\u6b21 Affine\u30ec\u30a4\u30e4 Softmax-with-Loss\u30ec\u30a4\u30e4 Affine ReLU \u30fb \u30fb \u30fb \u30fb Affine Softmax input \u30fb \u30fb \u30fb CrossEntropyError L Affine\u30ec\u30a4\u30e4 Affine\u5c64\u3068\u306f\u300cX\u30fbW + B = O\u300d\u3089\u3057\u304f\u3001\u3053\u306e\u8a08\u7b97\u30b0\u30e9\u30d5\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002 \u6731\u66f8\u304d\u90e8\u5206\u306f\u3001\u5fae\u5206\u306b\u3088\u308b\u8aa4\u5dee\u9006\u4f1d\u64ad\u3067\u3059\u3002 W = ( ) w w w 11 12 13 w w \u2026","provider_name":"Hatena Blog","published":"2022-09-03 06:17:52","url":"https://end0tknr.hateblo.jp/entry/20220903/1662153472","blog_url":"https://end0tknr.hateblo.jp/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fend0tknr.hateblo.jp%2Fentry%2F20220903%2F1662153472\" title=\"Affine\u5c64\u3068Softmax-with-Loss\u5c64\u306e\u8a08\u7b97\u30b0\u30e9\u30d5\u3068numpy for python\u5b9f\u88c5 - end0tknr&#39;s kipple - web\u5199\u7d4c\u958b\u767a\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","width":"100%","blog_title":"end0tknr's kipple - web\u5199\u7d4c\u958b\u767a","type":"rich","author_name":"end0tknr","version":"1.0","title":"Affine\u5c64\u3068Softmax-with-Loss\u5c64\u306e\u8a08\u7b97\u30b0\u30e9\u30d5\u3068numpy for python\u5b9f\u88c5","image_url":null,"provider_url":"https://hatena.blog"}