{"title":"L1\u6b63\u5247\u5316","published":"2025-09-28 21:34:54","categories":["DL"],"provider_url":"https://hatena.blog","blog_url":"https://htn20190109.hatenablog.com/","provider_name":"Hatena Blog","blog_title":"HTN20190109\u306e\u65e5\u8a18","width":"100%","image_url":null,"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fhtn20190109.hatenablog.com%2Fentry%2F2025%2F09%2F28%2F213454\" title=\"L1\u6b63\u5247\u5316 - 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>","author_url":"https://blog.hatena.ne.jp/HTN20190109/","description":"pip3.11 install torchvizpip3.11 install torchinfo python3.11 import numpy as npimport torchimport torch.nn as nnimport torch.optim as optim device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")print(device) # \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u6e96\u50991 \u30e9\u30f3\u30c0\u30e0\u30c7\u30fc\u30bf\u306e\u5834\u5408##### rng = np.random.default_rng()image_train = rng.\u2026","version":"1.0","author_name":"HTN20190109","height":"190","type":"rich","url":"https://htn20190109.hatenablog.com/entry/2025/09/28/213454"}