0.2.0

New Features

• Support custom weight initialization and setting. See #18.

• Add DiscreteVariationalAutoencoder class for training discrete models as priors. See #8

• Add pseudo KL divergence loss function for use with Discrete Variational Autoencoder. See #8.

• Support hidden units. See #7.

Upgrade Notes

• Decouple sampling parameters (beta) from the model. See #14.

• Reduce number of operations in the GRBM objective function. See #14.

• Remove networkx dependency. See #14.

• Simplify GraphRestrictedBoltzmannMachine constructor to require only node and edge lists. See #14.

• Add option to return samples as dimod.Sampleset from GraphRestrictedBoltzmannMachine.sample(). See #23.

Deprecation Notes

• Simplify GraphRestrictedBoltzmannMachine constructor to require only node and edge lists. See #14.

• Remove abstract base class. See #14.

Bug Fixes

• Make GraphRestrictedBoltzmannMachine.quasi_objective's argument kind optional. See #18.

Other Notes

• Add a directory for models and move boltzmann_machine.py from dwave/plugins/torch/ to dwave/plugins/torch/models/. See #24.

0.1.0

Prelude

• Initial release of dwave-pytorch-plugin.