Release 0.5.2

Added

• TensorNode outputs can now define a post_build function that will be
  executed after the simulation is initialized (see the TensorNode
  documentation for details).
• Added functionality for outputting summary data during the training process
  that can be viewed in TensorBoard (see the sim.train documentation).
• Added some examples demonstrating how to use Nengo DL in a more complicated
  task using semantic pointers to encode/retrieve information
• Added sim.training_step variable which will track the current training
  iteration (can be used, e.g., for TensorFlow's variable learning rate
  operations).
• Users can manually create tf.summary ops and pass them to sim.train
  summaries
• The Simulator context will now also set the default TensorFlow graph to the
  one associated with the Simulator (so any TensorFlow ops created within the
  Simulator context will automatically be added to the correct graph)
• Users can now specify a different objective for each output probe during
  training/loss calculation (see the sim.train documentation).

Changed

• Resetting the simulator now only rebuilds the necessary components in the
  graph (as opposed to rebuilding the whole graph)
• The default "mse" loss implementation will now automatically convert
  np.nan values in the target to zero error
• If there are multiple target probes given to sim.train/sim.loss the
  total error will now be summed across probes (instead of averaged)

Fixed

• sim.data now implements the full collections.Mapping interface
• Fixed bug where signal order was non-deterministic for Networks containing
  objects with duplicate names (#9)
• Fixed bug where non-slot optimizer variables were not initialized
  (#11)
• Implemented a modified PES builder in order to avoid slicing encoders on
  non-decoded PES connections
• TensorBoard output directory will be automatically created if it doesn't
  exist