
Layer-wise training of a DBN
The probabilities of the states that a Boltzmann Machine can take is defined by its energy function:
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 2007| I | 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 |