{"provider_name":"Hatena Blog","type":"rich","author_url":"https://blog.hatena.ne.jp/derwind/","url":"https://randommemory.hatenablog.com/entry/2020/06/30/224554","published":"2020-06-30 22:45:54","version":"1.0","blog_url":"https://randommemory.hatenablog.com/","provider_url":"https://hatena.blog","description":"\u753b\u50cf\u3092\u8868\u793a\u3057\u305f\u3044\u6642\u306b\u3044\u3064\u3082\u5168\u7136\u5206\u304b\u3089\u306a\u3044\u3084\u3064\u3002matplotlib\u57fa\u790e | figure\u3084axes\u3067\u306e\u30b0\u30e9\u30d5\u306e\u30ec\u30a4\u30a2\u30a6\u30c8 - Qiita\u3092\u53c2\u8003\u306b\u3059\u308b\u3002 x, t = next(iter(trainloader)) row = 5 col = 6 # 15inch x 10inch plt.figure(figsize=(15,10)) for i, im in enumerate(x.view(-1, 28, 28).cpu().detach().numpy()[:18]): plt.subplot(row, col, i+1) plt.imshow(im, cmap=\"gray\") plt.\u2026","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Frandommemory.hatenablog.com%2Fentry%2F2020%2F06%2F30%2F224554\" title=\"matplotlib - \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>","width":"100%","author_name":"derwind","categories":["machine_learning"],"blog_title":"\u3089\u3093\u3060\u3080\u306a\u8a18\u61b6","title":"matplotlib","height":"190","image_url":null}