* change irfft signal_sizes arg to be the last
* add docs for fft, ifft, rfft, irfft; update doc for stft
* fix typo in window function docs
* improve gradcheck error message
* implement backward of fft, ifft, rfft, irfft
* add grad tests for fft, ifft, rfft, irfft
* fix nits and typos from #6118
* address comments
* Implemented log2 and log10
* Re-add incorrectly removed files
* Fix minor bugs
* Fix log1p docs
* Add a try-except for python2 math module in log2 test
* Revert changes made to aten/doc/*
* Fix docstring errors
* Fix windows build
* 1. Add logdet and slogdet in ATen side
2. Previously, det can return result with incorrect sign upon seeing symmetric
matrices. This is caused by the wrong assumption I had on SVD (when input is
symmetric U=V^T). This fixes it.
3. Moreover, after fixing 2 now QR is always needed for det forward. So I moved
SVD to backward call. Since this is a specific variant of SVD, it is named as
_svd_with_positive_UV_det, with derivative.yaml entry being svd_backward.
4. Updated/added backward functions for det, logdet and slogdet, which uses
_svd_with_positive_UV_det and svd_backward inside.
5. Optimized svd_backward:
a. Avoid unnecessary kernels when only sigma has gradient (this is the usual
case, and also true with *det backward functions).
b. Fix SVD double backward by avoiding a nan.
* 1. Add/update grad checks for det, logdet, and slogdet.
2. Fix an incorrect check for dim_args_idx in test_autograd.py
3. Add option to only test a subset of output values, specified by
test_output_indices, for cases like slogdet where only the
second output is differentiable.
4. Add better doc for the test generating list.
* Add/improve output tests for det, logdet and slogdet
Add a scaling to random matrices so closeness checks are more robust
* Remove unnecessaery Variable wrappers in some test files
* Add logdet slogdet docs
* Improve an err msg in THTensorLapack.c
* add inverse-based backward for invertible matrices
use svd only for non-invertible case, so don't need the special variant anymore
* use LU rather than QR
* Fix some minor errors in existing docs.
* Fix Convolution and Pooling docs in torch.nn.functional
* Cleaned up torch.nn.functional docs
* Address @SsnL 's comments
* Add multiplication sign missing in docs
* Fix more typos, and clear some warnings
* Change infinity symbol in LPPool2d
* Revert some changes in torch.nn.functional
* Few more minor changes
* Implement torch.reshape and Tensor.reshape
This implements reshape which has similar semantics to numpy.reshape. It
will return a view of the source tensor if possible. Otherwise, it
returns a copy.
* Remove in-place reshape_ that was an alias for resize_
* Update documentation
* Improvize documentation
1. Add formula for erf, erfinv
2. Make exp, expm1 similar to log, log1p
3. Symbol change in ge, le, ne, isnan
* Fix minor nit in the docstring
* More doc improvements
1. Added some formulae
2. Complete scanning till "Other Operations" in Tensor docs
* Add more changes
1. Modify all torch.Tensor wherever required
* Fix Conv docs
1. Fix minor nits in the references for LAPACK routines
* Improve Pooling docs
1. Fix lint error
* Improve docs for RNN, Normalization and Padding
1. Fix flake8 error for pooling
* Final fixes for torch.nn.* docs.
1. Improve Loss Function documentation
2. Improve Vision Layers documentation
* Fix lint error
* Improve docstrings in torch.nn.init
* Fix lint error
* Fix minor error in torch.nn.init.sparse
* Fix Activation and Utils Docs
1. Fix Math Errors
2. Add explicit clean to Makefile in docs to prevent running graph generation script
while cleaning
3. Fix utils docs
* Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py
* Fix batch norm doc error
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
* Fix catArray in THTensor
Asserts that the inputs have the same size except in the
cat dimension or are empty (or a mix of both).
* Fix catArray for THCTensor
* Document torch.cat shape checks
* Fix types
* Add torch.take and Tensor.put_
These are similar to numpy.take and numpy.put. The take function allows
you to linearly index into a tensor without viewing it as a 1D tensor
first. The output has the same shape as the indices. The put function
copies value into a tensor also using linear indices.
