Hello Jim, I'm currently working on the same problem using Theano. Have you implemented the constrastive wake-sleep algorithm on this library and this case, could you tell me some guidances?
Many thanks, François Le jeudi 14 juillet 2016 11:32:38 UTC+2, Jim O' Donoghue a écrit : > > So I'm going to reply to my own question in case it helps anyone else out. > Had another look at the paper there, I had forgotten about the contrastive > wake-sleep algorithm. That's what's used to train the algorithm completely > unsupervised. > > On Tuesday, 12 July 2016 15:40:48 UTC+1, Jim O' Donoghue wrote: >> >> Hi There, >> >> Just wondering how you would fine-tune a DBN for a completely >> unsupervised task i.e. practical implementation of "Fine-tune all the >> parameters of this deep architecture with respect to a proxy for the DBN >> log- likelihood". >> >> Would this be something like, for example, a negative log likelihood >> between the original input and the reconstruction of the data when >> propogated entirely up and down the network? What makes the final layer an >> rbm and the rest just normally directed. Or would the only way you can do >> this be to completely un-roll the network and fine-tune like a deep >> autoencoder (as in reducing the dimensionality of data with neural >> networks)? >> >> Many thanks, >> Jim >> >> >> >> -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
