{"version":"1.0","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fhtn20190109.hatenablog.com%2Fentry%2F2026%2F09%2F21%2F223539\" title=\"DNNClassifier\u30af\u30e9\u30b9 - HTN20190109\u306e\u65e5\u8a18\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","blog_title":"HTN20190109\u306e\u65e5\u8a18","provider_name":"Hatena Blog","type":"rich","title":"DNNClassifier\u30af\u30e9\u30b9","author_url":"https://blog.hatena.ne.jp/HTN20190109/","url":"https://htn20190109.hatenablog.com/entry/2026/09/21/223539","published":"2026-09-21 22:35:39","width":"100%","description":"import numpy as np class DNNClassifier(object): def __init__(self, input_size, num_class, hidden_units=[128, 64]): self.num_hidden = len(hidden_units) num_units = [input_size] + hidden_units + [num_class] self.weights = [ np.random.randn(prev_size, size) * 0.01 for prev_size, size in zip(num_units[:\u2026","image_url":null,"provider_url":"https://hatena.blog","height":"190","blog_url":"https://htn20190109.hatenablog.com/","author_name":"HTN20190109","categories":["DL"]}