Layer-wise training of a DBN
DeepBeliefNetwork
, and it uses different other classes, deriving from RBMLayer
(RBMBinomialLayer
, RBMGaussianLayer
, RBMTruncExpLayer
, and RBMMixedLayer
) and RBMConnection
(RBMMatrixConnection
, RBMMixedConnection
).
args
class at the beginning of the script, or by passing the appropriate command-line arguments (see the scripts for more details).
-- PascalLamblin - 22 Jun 2007I | Attachment | Action | Size | Date | Who | Comment |
---|---|---|---|---|---|---|
![]() | example_mnist.pyplearn | manage | 5.1 K | 21 Jun 2007 - 21:13 | PascalLamblin | Example of .pyplearn script implementing a Deep Belief Net, applied to MNIST digit recognition dataset |
![]() | example_mnist_earlystopping.pyplearn | manage | 6.4 K | 21 Jun 2007 - 21:15 | PascalLamblin | Example of .pyplearn script implementing a Deep Belief Net, applied to MNIST digit recognition dataset, with early-stopping on a validation set during supervised phases |
![]() | DBNs.png | manage | 36.8 K | 21 Jun 2007 - 22:27 | PascalLamblin | Layer-wise training of a DBN |
![]() | RBM.png | manage | 7.2 K | 21 Jun 2007 - 23:10 | PascalLamblin | A Restricted Boltzmann Machine |