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Release 3.1.0

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@drasmuss drasmuss released this 05 Mar 13:56

Compatible with Nengo 3.0.0

Compatible with TensorFlow 2.0.0 - 2.1.0

Added

  • Added inference_only=True option to the Converter, which will allow some Layers/parameters that cannot be fully converted to native Nengo objects to be converted in a way that only matches the inference behaviour of the source Keras model (not the training behaviour). (#119)

Changed

  • Improved build time of networks containing lots of TensorNodes. (#119)
  • Improved memory usage of build process. (#119)
  • Saved simulation state may now be placed on GPU (this should improve the speed of state updates, but may slightly increase GPU memory usage). (#119)
  • Changed Converter freeze_batchnorm=True option to inference_only=True (effect of the parameter is the same on BatchNormalization layers, but also has broader effects). (#119)
  • The precision of the Nengo core build process will now be set based on the nengo_dl.configure_settings(dtype=...) config option. Note that this will override the default precision set in nengo.rc. (#119)
  • Minimum Numpy version is now 1.16.0 (required by TensorFlow). (#119)
  • Added support for the new transform=None default in Nengo connections (see Nengo#1591). Note that this may change the number of trainable parameters in a network as the scalar default transform=1 weights on non-Ensemble connections will no longer be present. (#128)

Fixed

  • Provide a more informative error message if Layer shape_in/shape_out contains undefined (None) elements. (#119)
  • Fixed bug in Converter when source model contains duplicate nodes. (#119)
  • Fixed bug in Converter for Concatenate layers with axis != 1. (#119)
  • Fixed bug in Converter for models containing passthrough Input layers inside submodels. (#119)
  • Keras Layers inside TensorNodes will be called with the training argument set correctly (previously it was always set to the default value). (#119)
  • Fixed compatibility with progressbar2 version 3.50.0. (#136)