{"blog_title":"\u6280\u8853\u30e1\u30e2\u96c6","author_name":"t_nkb","url":"https://www.robotech-note.com/entry/2016/09/30/092659","categories":["DeepLearning","chainer"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.robotech-note.com%2Fentry%2F2016%2F09%2F30%2F092659\" title=\"GPU\u306e\u30ab\u30bf\u30ed\u30b0\u30b9\u30da\u30c3\u30af\uff08TFLOPS\uff09\u3068\u5b66\u7fd2\u6642\u9593\u306e\u95a2\u4fc2\uff08chainer\u306eMNIST\u30b5\u30f3\u30d7\u30eb\u304b\u3089\u306e\u8003\u5bdf\uff09 - \u6280\u8853\u30e1\u30e2\u96c6\" 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%","published":"2016-09-30 09:26:59","provider_url":"https://hatena.blog","blog_url":"https://www.robotech-note.com/","version":"1.0","author_url":"https://blog.hatena.ne.jp/t_nkb/","type":"rich","title":"GPU\u306e\u30ab\u30bf\u30ed\u30b0\u30b9\u30da\u30c3\u30af\uff08TFLOPS\uff09\u3068\u5b66\u7fd2\u6642\u9593\u306e\u95a2\u4fc2\uff08chainer\u306eMNIST\u30b5\u30f3\u30d7\u30eb\u304b\u3089\u306e\u8003\u5bdf\uff09","provider_name":"Hatena Blog","height":"190","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/t/t_nkb/20160930/20160930092112.png","description":"\u8da3\u65e8 \u5b66\u7fd2\u7d50\u679c\uff08\u6642\u9593\uff09 \u8003\u5bdf \u7d50\u679c\u304b\u3089\u306e\u77e5\u898b \u9ad8\u901f\u306aGPU\u304b\u3089\u4f4e\u901f\u306aGPU\u306b\u79fb\u884c\u3059\u308b\u5834\u5408\uff1a \u4f4e\u901f\u306aGPU\u304b\u3089\u9ad8\u901f\u306aGPU\u306b\u79fb\u884c\u3059\u308b\u5834\u5408\uff1a AI \u8da3\u65e8 GPU\u3092\u4ea4\u63db\u3057\u305f\u5834\u5408\u306e\u5b66\u7fd2\u6642\u9593\u306e\u5909\u5316\u3092\u8a66\u7b97\u3057\u305f\u3044\u3002 \u4eca\u56de\u306f\u5b66\u7fd2\u6642\u9593\u306e\u8a66\u7b97\u3092\u884c\u3046\u6307\u6a19\u3068\u3059\u308b\u305f\u3081\u3001chainer\u306eMNIST\u5b66\u7fd2\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u4f7f\u7528\u3057\u3066\u5b66\u7fd2\u6642\u9593\u3092\u6c42\u3081\u305f\u3002\u4ee5\u4e0b\u30d7\u30ed\u30b0\u30e9\u30e0\u306etrain_mnist.py\u3092\u4f7f\u7528\u3002 chainer/examples/mnist at master \u00b7 pfnet/chainer \u00b7 GitHub \u5b66\u7fd2\u7d50\u679c\uff08\u6642\u9593\uff09 \u624b\u6301\u3061\u306eCPU,GPU\u3092\u4f7f\u3063\u3066\u5b66\u7fd2\u3055\u305b\u305f\u7d50\u679c\u3092\u4ee5\u4e0b\u306b\u793a\u3059\u3002 GPU(CPU) \u7406\u8ad6\u4e0a\u306eFLO\u2026"}