{"id":1899,"date":"2018-01-29T23:02:54","date_gmt":"2018-01-29T15:02:54","guid":{"rendered":"http:\/\/www.ticoneva.com\/journal\/?p=1899"},"modified":"2018-01-29T23:02:54","modified_gmt":"2018-01-29T15:02:54","slug":"1899","status":"publish","type":"post","link":"https:\/\/www.ticoneva.com\/journal\/?p=1899","title":{"rendered":""},"content":{"rendered":"<p><a class=\"_58cn\" href=\"https:\/\/www.facebook.com\/hashtag\/keras?source=feed_text&amp;story_id=10107753478603533\" data-ft=\"{&quot;tn&quot;:&quot;*N&quot;,&quot;type&quot;:104}\"><span class=\"_5afx\"><span class=\"_58cl _5afz\" aria-label=\"hashtag\">#<\/span><span class=\"_58cm\">Keras<\/span><\/span><\/a>\u00a0tip: you actually need to specify batch size again when you run model.predict() after model.fit(), otherwise it defaults to a batch size of 32. Depending on your model this could be too small, leading to incredibly slow inference. Common online examples do not seem to emphasize this at all.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>#Keras\u00a0tip: you actually need to specify batch size again when you run model.predict() after model.fit(), otherwise it defaults to a batch size of 32. Depending on your model this could be too small, leading to incredibly slow inference. Common online examples do not seem to emphasize this at all.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1899","post","type-post","status-publish","format-standard","hentry","category-tech-zone"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/posts\/1899","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1899"}],"version-history":[{"count":1,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/posts\/1899\/revisions"}],"predecessor-version":[{"id":1900,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=\/wp\/v2\/posts\/1899\/revisions\/1900"}],"wp:attachment":[{"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ticoneva.com\/journal\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}