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.
