{"provider_name":"Hatena Blog","height":"190","provider_url":"https://hatena.blog","author_url":"https://blog.hatena.ne.jp/HTN20190109/","type":"rich","title":"WGAN-GP\u306e\u5b9f\u88c5","categories":["DL"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fhtn20190109.hatenablog.com%2Fentry%2F2026%2F10%2F07%2F200557\" title=\"WGAN-GP\u306e\u5b9f\u88c5 - 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_url":"https://htn20190109.hatenablog.com/","image_url":null,"published":"2026-10-07 20:05:57","author_name":"HTN20190109","version":"1.0","description":"https://taviko.com/tavilab/gan/wgan-gp/ import torchimport torch.nn as nnimport torch.optim as optimimport torch.autograd as autogradfrom torch.utils.data import DataLoaderfrom torchvision import datasets, transformsimport numpy as npimport matplotlib.pyplot as plt # \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bfbatch_size = 32latent_d\u2026","blog_title":"HTN20190109\u306e\u65e5\u8a18","url":"https://htn20190109.hatenablog.com/entry/2026/10/07/200557","width":"100%"}