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Implement Default bmm Functionality for Base Tensor Type #28

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iamtrask opened this issue Aug 9, 2017 · 1 comment
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Implement Default bmm Functionality for Base Tensor Type #28

iamtrask opened this issue Aug 9, 2017 · 1 comment
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Type: Improvement 📈 Minor improvements not introducing a new feature or requiring a major refactor

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@iamtrask
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iamtrask commented Aug 9, 2017

User Story A: As a Data Scientist using Syft's Base Tensor type, we want to implement a default method for computing operations on a Tensor of arbitrary type. For this ticket to be complete, bmm() should return a new tensor. For a reference on the operation this performs check out PyTorch's documentation.

Acceptance Criteria:

  • If the Base Tensor type's attribute "encrypted" is set to True, it should return a NotImplemented error.
  • a unit test demonstrating the correct operation on the Base Tensor type implemented over int and float Tensors.
  • inline documentation in the python code. For inspiration on inline documentation, please check out PyTorch's documentation for this operator.
@iamtrask iamtrask modified the milestone: Helium Aug 10, 2017
@iamtrask iamtrask added d. Beginner Type: Improvement 📈 Minor improvements not introducing a new feature or requiring a major refactor labels Aug 10, 2017
@AkashGutha AkashGutha mentioned this issue Oct 2, 2017
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bharathgs pushed a commit that referenced this issue Oct 2, 2017
@AkashGutha @bharathgs
BMM functionality - issue #28 (#264)
4e2aa9e 
* added basic bmm functionaltiy

* added bmm functionality

* typo fix

* added bmm to the init

* doc fix

* added assertions to tests

* change to shape() from size

* fn name fix

* added  bmm tests

* fix for spaces

* reverting line change issues

* inline documentation according to guide
@AkashGutha
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we can close this issue now

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@iamtrask @bharathgs @AkashGutha