Transcription of A Practical Guide to Training Restricted Boltzmann Machines
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A Practical Guide to Training Restricted Boltzmann Machines
Department of Computer Science6 King s College Rd, TorontoUniversity of TorontoM5S 3G4, : +1 416 978 1455 Copyrightc Geoffrey Hinton 2, 2010 UTML TR 2010 003A Practical Guide to TrainingRestricted Boltzmann MachinesVersion 1 Geoffrey HintonDepartment of Computer Science, University of TorontoA Practical Guide to Training Restricted BoltzmannMachinesVersion 1 Geoffrey HintonDepartment of Computer Science, University of TorontoContents1 Introduction32 An overview of Restricted Boltzmann Machines and Contrastive Divergence33 How to collect statistics when using Contrastive Updating the hidden states .. Updating the visible states .. Collecting the statistics needed for learning .. A recipe for getting the learning signal for CD1..64 The size of a A recipe for dividing the Training set into mini-batches.
ascent in the log probability of the training data: w ij = (hv ih ji data h v ih ji model) (6) where is a learning rate. Because there are no direct connections between hidden units in an RBM, it is very easy to get an unbiased sample of hv ih ji data. Given a randomly selected training image, v, the binary state, h j,
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