{"action":"created","issue":{"url":"https://api.github.com/repos/Microsoft/CNTK/issues/425","repository_url":"https://api.github.com/repos/Microsoft/CNTK","labels_url":"https://api.github.com/repos/Microsoft/CNTK/issues/425/labels{/name}","comments_url":"https://api.github.com/repos/Microsoft/CNTK/issues/425/comments","events_url":"https://api.github.com/repos/Microsoft/CNTK/issues/425/events","html_url":"https://github.com/Microsoft/CNTK/issues/425","id":152598278,"number":425,"title":"Example/process for iterative deep training","user":{"login":"simra","id":2946735,"avatar_url":"https://avatars.githubusercontent.com/u/2946735?v=3","gravatar_id":"","url":"https://api.github.com/users/simra","html_url":"https://github.com/simra","followers_url":"https://api.github.com/users/simra/followers","following_url":"https://api.github.com/users/simra/following{/other_user}","gists_url":"https://api.github.com/users/simra/gists{/gist_id}","starred_url":"https://api.github.com/users/simra/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/simra/subscriptions","organizations_url":"https://api.github.com/users/simra/orgs","repos_url":"https://api.github.com/users/simra/repos","events_url":"https://api.github.com/users/simra/events{/privacy}","received_events_url":"https://api.github.com/users/simra/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":8,"created_at":"2016-05-02T17:14:47Z","updated_at":"2016-05-03T21:09:40Z","closed_at":null,"body":"Hi all,\r\nI'm interested to train a deep network for binary classification. My instances are (sparse) Boolean features from a very large feature space, and I've had some success training a simple network with up to four hidden layers using hashed input features.\r\nI would like to experiment with training even deeper networks along the lines of Hinton's deep RBMs- training layer by layer.  It seems to me this is possible with some extra work and some network editing. For example - I can train a two layer auto-encoder, strip off the output layer, add a new one, and continue training.\r\nThe devil's in the details, though, and I wonder if I could get some guidance on how to best approach this. Are there any existing examples?\r\nThanks in advance."},"comment":{"url":"https://api.github.com/repos/Microsoft/CNTK/issues/comments/216665488","html_url":"https://github.com/Microsoft/CNTK/issues/425#issuecomment-216665488","issue_url":"https://api.github.com/repos/Microsoft/CNTK/issues/425","id":216665488,"user":{"login":"simra","id":2946735,"avatar_url":"https://avatars.githubusercontent.com/u/2946735?v=3","gravatar_id":"","url":"https://api.github.com/users/simra","html_url":"https://github.com/simra","followers_url":"https://api.github.com/users/simra/followers","following_url":"https://api.github.com/users/simra/following{/other_user}","gists_url":"https://api.github.com/users/simra/gists{/gist_id}","starred_url":"https://api.github.com/users/simra/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/simra/subscriptions","organizations_url":"https://api.github.com/users/simra/orgs","repos_url":"https://api.github.com/users/simra/repos","events_url":"https://api.github.com/users/simra/events{/privacy}","received_events_url":"https://api.github.com/users/simra/received_events","type":"User","site_admin":false},"created_at":"2016-05-03T21:09:40Z","updated_at":"2016-05-03T21:09:40Z","body":"Quick follow-up: the latest repo examples are unchanged. I don't have a current build yet because I'm not set up with VS2013, so I'm stuck with the latest binary release.\r\n\r\nThis is the reader I'm trying to convert to UCIFastReader, using my own input samples.\r\n```\r\nreader=[\r\n        # reader to use\r\n        readerType=HTKMLFReader\r\n        readMethod=blockRandomize\r\n\r\n        miniBatchMode=Partial\r\n        randomize=Auto\r\n        verbosity=0\r\n\r\n        featIn=[\r\n            dim=792\r\n            scpFile=$ScpDir$/TIMIT.train.scp.fbank.fullpath.rnn\r\n        ]\r\n\r\n        featOut=[\r\n            scpFile=$ScpDir$/TIMIT.train.scp.fbank.fullpath.rnn\r\n            dim=792\r\n        ]\r\n    ]\r\n```"}}