Release 0.3.0

Added

• Use logger for debug/builder output
• Implemented TensorFlow gradients for sparse Variable update Ops, to allow
  models with those elements to be trained
• Added tutorial/examples on using Simulator.train
• Added support for training models when unroll_simulation=False
• Compatibility changes for Nengo 2.4.0
• Added a new graph planner algorithm, which can improve simulation speed at
  the cost of build time

Changed

• Significant improvements to simulation speed

  • Use sparse Variable updates for signals.scatter/gather
  • Improved graph optimizer memory organization
  • Implemented sparse matrix multiplication op, to allow more aggressive
    merging of DotInc operators

• Significant improvements to build speed

  • Added early termination to graph optimization
  • Algorithmic improvements to graph optimization functions

• Reorganized documentation to more clearly direct new users to relevant
  material

Fixed

• Fix bug where passing a built model to the Simulator more than once would
  result in an error
• Cache result of calls to tensor_graph.build_loss/build_optimizer, so that
  we don't unnecessarily create duplicate elements in the graph on repeated
  calls
• Fix support for Variables on GPU when unroll_simulation=False
• SimPyFunc operators will always be assigned to CPU, even when
  device="/gpu:0", since there is no GPU kernel
• Fix bug where Simulator.loss was not being computed correctly for
  models with internal state
• Data/targets passed to Simulator.train will be truncated if not evenly
  divisible by the specified minibatch size
• Fixed bug where in some cases Nodes with side effects would not be run if
  their output was not used in the simulation
• Fixed bug where strided reads that cover a full array would be interpreted as
  non-strided reads of the full array