{"provider_name":"Hatena Blog","width":"100%","image_url":"https://lh3.googleusercontent.com/-9iE6nO0O9fA/W-cAHl6hN9I/AAAAAAAABV8/38rO8L4-Zvsv-mH6wyI2NQLJG_0IonUdwCE0YBhgL/s1024/keras-vgg16-model_03.png","height":"190","url":"https://pynote.hatenablog.com/entry/keras-vgg16-mode","blog_url":"https://pynote.hatenablog.com/","description":"\u6982\u8981 Keras \u3067\u306f VGG\u3001GoogLeNet\u3001ResNet \u306a\u3069\u306e\u6709\u540d\u306a CNN \u30e2\u30c7\u30eb\u306e\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u304c\u7c21\u5358\u306b\u5229\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u308b\u3002 \u4eca\u56de\u306f ImageNet \u3067\u5b66\u7fd2\u6e08\u307f\u306e VGG16 \u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u305f\u753b\u50cf\u5206\u985e\u3092\u884c\u3046\u65b9\u6cd5\u3092\u7d39\u4ecb\u3059\u308b\u3002 \u6982\u8981 \u624b\u9806 \u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3002 \u753b\u50cf\u3092\u8aad\u307f\u8fbc\u3080\u3002 \u63a8\u8ad6\u3059\u308b\u3002 \u65e5\u672c\u8a9e\u306e\u30e9\u30d9\u30eb\u540d\u3067\u8868\u793a\u3059\u308b\u3002 \u3044\u308d\u3093\u306a\u753b\u50cf\u3092\u63a8\u8ad6\u3057\u3066\u307f\u308b\u3002","categories":["Deep Learning"],"blog_title":"pynote","type":"rich","author_url":"https://blog.hatena.ne.jp/nekobean/","author_name":"nekobean","provider_url":"https://hatena.blog","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fpynote.hatenablog.com%2Fentry%2Fkeras-vgg16-mode\" title=\"Keras - ImageNet \u306e\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3092\u5229\u7528\u3057\u3066\u753b\u50cf\u5206\u985e\u3092\u884c\u3046\u3002 - pynote\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","published":"2018-11-11 01:07:04","version":"1.0","title":"Keras - ImageNet \u306e\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3092\u5229\u7528\u3057\u3066\u753b\u50cf\u5206\u985e\u3092\u884c\u3046\u3002"}