This is the alpha pre-release of the stable v1.0.0 release. Below is a summary of changes and updates from the previous release, v0.4.

New Features

Exhaustive documentation now available!
Now compatible with both TF1 and TF2 (still uses tensorflow graphs).
Add fixed weights feature-- can choose not to train certain weights in W_rec, W_in, W_out.
Revamps Simulation to make it relevant and functional.
Adds new MatchToCategory task.
Add support for custom loss and regularization functions.

Other New Features

Added options for constant uniform, constant gaussian, glorot gaussian, glorot uniform initializations.
Automatically build and initialize in get_weights, save, test, train, train_curric. No model.build() calls needed from now on.
Added train_curric function.

No long supports TF < 1.13.1
Refactors RDM Task--> PerceptualDiscrimination & changes some functionality.
Refactors Romo Task--> DelayedDiscrimination & changes some functionality.
Refactors Curriculum's self.stopTraining --> self. stop_training. This change should not affect most users since it is a variable used internally only by curriculum.py and rnn.py
Removes sussillo regularizer because it was specific to the transfer-function used. Can be defined and used via the custom regularizer.
Remove get_initial_weights function. Use get_weights instead.

Default random initialization changed to glorot gaussian from constant uniform. This may affect results.
Unexpected behavior in Dale's fixed and Dale's setup moved into WeightInitializer. This should not affect most users but may affect results.
Default training iters changed from 10000 to 50000

BasicScan internal implementation slightly change. Should not affect users unless users inherited BasicScan to make a custom Model.
Use get_effective to get the weight matrices to regularize. Should not affect users.