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Releases: Vivswan/AnalogVNN

v1.0.8

02 May 23:33
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What's Changed

  • Removed redundant code from reduce_precision.
  • Added types argument to PseudoParameter.parametrize_module for better selection for Parameterising the Layers.

Full Changelog: v1.0.7...v1.0.8

v1.0.7

22 Nov 06:52
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What's Changed

  • Fixed GeLU backward function equation.

Full Changelog: v1.0.6...v1.0.7

v1.0.6

06 Jun 02:54
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What's Changed

  • Model is subclass of BackwardModule for additional functionality.
  • Using inspect.isclass to check if backward_class is a class in Linear.set_backward_function.
  • Repr using self.__class__.__name__ in all classes.

Full Changelog: v1.0.5...v1.0.6

v1.0.5

15 May 07:01
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What's Changed (Patches for Pytorch 2.0.1)

  • Patch for Pytorch 2.0.1, add filtering inputs in BackwardGraph._calculate_gradients.
  • Removed unnecessary PseudoParameter.grad property.

Full Changelog: v1.0.4...v1.0.5

v1.0.4

10 May 23:50
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What's Changed

  • Combined PseudoParameter and PseudoParameterModule for better visibility
    • BugFix: fixed save and load of state_dict of PseudoParameter and transformation module
  • Removed redundant class analogvnn.parameter.Parameter

Full Changelog: v1.0.3...v1.0.4

v1.0.3

08 May 04:27
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What's Changed

  • Added support for no loss function in Model class
    • If no loss function is provided, the Model object will use outputs for gradient computation
  • Added support for multiple loss outputs from loss function

Full Changelog: v1.0.2...v1.0.3

v1.0.2

08 May 03:11
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What's Changed

  • Bugfix: removed graph from Layer class
    • graph was causing issues with nested Model objects
    • Now _use_autograd_graph is directly set while compiling the Model object

Full Changelog: v1.0.1...v1.0.2

v1.0.1

27 Mar 02:12
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What's Changed (Patches for Pytorch 2.0.0)

  • added grad.setter to PseudoParameterModule class

Full Changelog: v1.0.0...v1.0.1

v1.0.0

07 Mar 11:03
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Documentation: https://analogvnn.readthedocs.io/

AnalogVNN Paper: https://arxiv.org/abs/2210.10048

Cite:

Vivswan Shah, and Nathan Youngblood. "AnalogVNN: A fully modular framework for modeling and optimizing 
photonic neural networks." *arXiv preprint arXiv:2210.10048 (2022)*.

Installation:

pip install analogvnn

Full Changelog: https://github.com/Vivswan/AnalogVNN/commits/v1.0.0