* implemented CRF
* added test for CRF
* added a chunking example for CRF
* changed to use up2date tensorflow
* added conll2000 data
* minimize package dependency
* using logsumexp in keras instead
* Added improved_wgan.py to examples
Implements the improved WGAN with gradient penalty in Keras.
* Fixed PEP8 issues and one docstring mistake.
* Added a note to the discriminator to avoid BatchNormalization, as per the paper.
* Minor docstring correction to clarify description for wasserstein_loss function.
* Fixed a missing line in the generator
* Minor cleanup to make generating the label vectors a bit more elegant.
* Fixing typo in previous commit.
* Moved the model out of a main() function. Model now correctly respects
keras.backend.image_data_format
* Aaaand another PEP8 cleanup >.>
* Gradient penalty is now correctly based on the gradient w.r.t. the input averaged samples, rather than the weights of the discriminator.
* Gradient is now correctly calculated on the output of the discriminator, not the
K.mean() of the output (i.e. the loss).
Andrew Hundt