{"width":"100%","provider_url":"https://hatena.blog","title":"\u30bc\u30ed\u3064\u304f 2 (16)","type":"rich","provider_name":"Hatena Blog","image_url":null,"version":"1.0","blog_url":"https://randommemory.hatenablog.com/","published":"2021-11-14 02:44:25","author_name":"derwind","height":"190","url":"https://randommemory.hatenablog.com/entry/2021/11/14/024425","blog_title":"\u3089\u3093\u3060\u3080\u306a\u8a18\u61b6","categories":["machine_learning"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Frandommemory.hatenablog.com%2Fentry%2F2021%2F11%2F14%2F024425\" title=\"\u30bc\u30ed\u3064\u304f 2 (16) - \u3089\u3093\u3060\u3080\u306a\u8a18\u61b6\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_url":"https://blog.hatena.ne.jp/derwind/","description":"TimeSoftmaxWithLoss.forward \u5185\u306e xs = xs.reshape(N * T, V) \u306b\u3064\u3044\u3066\u8003\u3048\u305f\u3044\u3002\u7c21\u5358\u306a\u52d5\u304d\u3092\u898b\u308b\u30b3\u30fc\u30c9\u3092\u8003\u3048\u308b >>> import numpy as np >>> a = np.array(range(24)).reshape(2, 3, 4) >>> print(a) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] >>> print(a.reshape(2*3, 4)) [[ 0 1 2 3] [ 4 5 6 7] [ \u2026"}