{"action":"created","issue":{"url":"https://api.github.com/repos/transcranial/keras-js/issues/37","repository_url":"https://api.github.com/repos/transcranial/keras-js","labels_url":"https://api.github.com/repos/transcranial/keras-js/issues/37/labels{/name}","comments_url":"https://api.github.com/repos/transcranial/keras-js/issues/37/comments","events_url":"https://api.github.com/repos/transcranial/keras-js/issues/37/events","html_url":"https://github.com/transcranial/keras-js/issues/37","id":192111351,"number":37,"title":"Multi-dimensional Sequential Inputs  / unflattened float32 arrays","user":{"login":"LRonHubs","id":23349996,"avatar_url":"https://avatars.githubusercontent.com/u/23349996?v=3","gravatar_id":"","url":"https://api.github.com/users/LRonHubs","html_url":"https://github.com/LRonHubs","followers_url":"https://api.github.com/users/LRonHubs/followers","following_url":"https://api.github.com/users/LRonHubs/following{/other_user}","gists_url":"https://api.github.com/users/LRonHubs/gists{/gist_id}","starred_url":"https://api.github.com/users/LRonHubs/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LRonHubs/subscriptions","organizations_url":"https://api.github.com/users/LRonHubs/orgs","repos_url":"https://api.github.com/users/LRonHubs/repos","events_url":"https://api.github.com/users/LRonHubs/events{/privacy}","received_events_url":"https://api.github.com/users/LRonHubs/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2016-11-28T20:39:55Z","updated_at":"2016-11-30T03:02:35Z","closed_at":"2016-11-28T21:40:34Z","body":"This library appears to be focused on the Model structure, which is okay, but most of my trained networks are in Sequential form. Specifically LSTM Sequentials.\r\n\r\nLSTM Sequentials generally expect a three-dimensional input (batchSize, X,Y). However this repo requires there to be only one flattened input for Sequential, named \"input\". Has anyone found a good way to convert Sequentials to a Model or a way around this? \r\n\r\nI would like to just do prediction in javascript. That means the model shouldn't even need to be compiled, so maybe I can just hack something together myself."},"comment":{"url":"https://api.github.com/repos/transcranial/keras-js/issues/comments/263769751","html_url":"https://github.com/transcranial/keras-js/issues/37#issuecomment-263769751","issue_url":"https://api.github.com/repos/transcranial/keras-js/issues/37","id":263769751,"user":{"login":"LRonHubs","id":23349996,"avatar_url":"https://avatars.githubusercontent.com/u/23349996?v=3","gravatar_id":"","url":"https://api.github.com/users/LRonHubs","html_url":"https://github.com/LRonHubs","followers_url":"https://api.github.com/users/LRonHubs/followers","following_url":"https://api.github.com/users/LRonHubs/following{/other_user}","gists_url":"https://api.github.com/users/LRonHubs/gists{/gist_id}","starred_url":"https://api.github.com/users/LRonHubs/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LRonHubs/subscriptions","organizations_url":"https://api.github.com/users/LRonHubs/orgs","repos_url":"https://api.github.com/users/LRonHubs/repos","events_url":"https://api.github.com/users/LRonHubs/events{/privacy}","received_events_url":"https://api.github.com/users/LRonHubs/received_events","type":"User","site_admin":false},"created_at":"2016-11-30T03:02:35Z","updated_at":"2016-11-30T03:02:35Z","body":"So to test this I'm basically putting in a new Float32Array(samplePoints*EEGchannels) and I'm getting the error: \r\n\r\n`Error: Specified shape incompatible with data.`\r\n\r\nusing networks without an embedding layer.\r\n\r\n"}}
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