[{"data":1,"prerenderedAt":184},["ShallowReactive",2],{"project-12947830":3},{"id":4,"name":5,"line":6,"line_source":7,"title":8,"url":9,"domain":10,"maker":11,"posted_at":12,"year":13,"points":14,"comments":15,"rank_in_year":16,"shown_in_year":17,"top":18,"tier":19,"category":20,"subcategory":21,"format":22,"keywords":23,"reception":28,"sentiment":29,"stance_mix":30,"scored_comments":34,"aspects":35,"strip":39,"post_text":42,"what":43,"said":61,"maker_in_thread":108,"shape":110,"grid":114,"attempts":145,"same_week":146,"maker_shows":32,"maker_hidden":176,"reader_version":177,"card":178},"12947830","Quiver","Interactive deep convnet visualization for Keras and Tensorflow","title","Show HN: Interactive deep convnet visualization for Keras and Tensorflow","https:\u002F\u002Fjakebian.github.io\u002Fquiver\u002F","jakebian.github.io","dbranes",1479103110,2016,124,11,289,2264,13,"notable","ai","neural network visualization","library",[24,25,26,27],"convnet visualization","keras","tensorflow","neural network","curious",0.063,{"positive":31,"neutral":32,"mixed":33,"critical":31},1,4,0,6,{"idea":36,"design":37,"docs":38},{"positive":33,"critical":31},{"positive":33,"critical":33},{"positive":33,"critical":33},[40,40,31,33,33,33,33,33,40,41,40],3,5,null,{"problem":44,"audience":42,"pricing":42,"motivation":42,"time_to_build":42,"stack":50,"openness":59,"stage":60,"team_size":60},{"value":45,"source":46,"evidence":47},"Visualising activations of convolutional neural network layers","maker",[48],{"id":49},"post",[51,55],{"value":52,"source":46,"evidence":53},"Keras",[54],{"id":49},{"value":56,"source":46,"evidence":57},"TensorFlow",[58],{"id":49},"open_source","unknown",{"praise":62,"objections":75,"requests":83,"questions":98,"alternatives":103},[63],{"value":64,"source":65,"evidence":66},"Useful for gaining insight into neural network behaviour","commenter",[67,71],{"id":68,"by":69,"text":70},"12948100","visarga","That looks useful for getting insights into what is happening in a NN. What is it visualizing in a convolutional layer, does it average all the channels, or select just one?",{"id":72,"by":73,"text":74},"12954224","SynapticSelf","I created an account just to say that this looks really fantastic. Thanks for creating this! I'm looking forward to steady improvements in neural network tooling, and it's people…",[76],{"value":77,"source":65,"evidence":78},"Raw per-filter image views offer limited insight; abstract analysis matters more",[79],{"id":80,"by":81,"text":82},"12950020","taliesinb","As slick as this kind of thing looks, I don't think it does help much to see raw filters on an image-by-image basis. The kind of insights that I think are more important are the…",[84,91],{"value":85,"source":65,"evidence":86},"Add visualization of filters",[87],{"id":88,"by":89,"text":90},"12949060","joelthelion","It would be nice to also have a way to visualize filters (eg. https:\u002F\u002Fblog.keras.io\u002Fhow-convolutional-neural-networks-see-the-world.html)",{"value":92,"source":65,"evidence":93},"Include a license file",[94],{"id":95,"by":96,"text":97},"12949213","Radim","License file missing, but setup.py seems to imply MIT.",[99],{"value":100,"source":65,"evidence":101},"What does each convolutional layer visualization show: all channels averaged or one selected",[102],{"id":68,"by":69,"text":70},[104],{"value":105,"source":65,"evidence":106},"Keras blog filter visualization examples",[107],{"id":88,"by":89,"text":90},{"comments":33,"answered":33,"key":109},[],{"commenters":111,"max_depth":34,"maker_replies":33,"first_comment_mins":112,"lifespan_hours":113},10,90,43,[115,117,121,123,127,129,132,136,139,140,143],[68,33,40,116,69,33],"project\u002Fneutral\u002Fidea",[118,33,40,119,120,33],"12948904","project\u002Fneutral\u002Fdesign","stephanheijl",[80,33,31,122,81,33],"project\u002Fcritical\u002Fidea",[124,33,33,125,126,33],"12950330","other\u002Fneutral\u002Fnone","TFortunato",[128,33,33,125,81,33],"12950730",[130,33,33,125,131,33],"12963712","d_fortunato",[133,33,33,134,135,33],"12963750","commenter\u002Fneutral\u002Fnone","realD_fortunato",[137,33,33,134,138,33],"12963760","ReelDFortunato",[88,31,40,116,89,33],[72,141,41,142,73,33],2,"maker\u002Fpositive\u002Fnone",[95,40,40,144,96,33],"project\u002Fneutral\u002Fdocs",[],[147,152,157,162,167,171],{"id":148,"name":149,"points":150,"category":151},"12958445","Primitive",798,"creative",{"id":153,"name":154,"points":155,"category":156},"12956243","Whosfiring",256,"web_saas",{"id":158,"name":159,"points":160,"category":161},"12940615","jamesbvaughan.com",238,"languages",{"id":163,"name":164,"points":165,"category":166},"12932592","Blink Shell for iOS",219,"dev_tools",{"id":168,"name":169,"points":170,"category":166},"12921389","CloudRail",191,{"id":172,"name":173,"points":174,"category":175},"12938636","Gridmaster",188,"learning_science",false,"v2",{"id":179,"name":5,"line":6,"ai":176,"cat":20,"year":13,"points":14,"comments":15,"rec":28,"s":180,"top":18,"kw":181,"maker":11,"domain":10,"tier":19,"attempts":33,"strip":182,"url":9,"mix":183,"praise":64,"objection":77,"sub":21},12947830,0.06,[24,25,26,27],"33100000353",[31,32,33,31],1794036549505]