* cuda.root inferred if nvcc is on the path, otherwise defaults to
/usr/local/cuda
* Better graph printing for graphs involving a scan subgraph
* Casting behavior can be controlled through config.cast_policy,
new (experimental) mode.
* Smarter C module cache, avoiding erroneous usage of the wrong C
implementation when some options change, and avoiding recompiling the
same module multiple times in some situations.
* The "theano-cache clear" command now clears the cache more thoroughly.
* More extensive linear algebra ops (CPU only) that wrap scipy.linalg
now available in the sandbox.
* CUDA devices 4 - 16 should now be available if present.
* infer_shape support for the View op, better infer_shape support in Scan
* infer_shape supported in all case of subtensor
* tensor.grad now gives an error by default when computing the gradient
wrt a node that is disconnected from the cost (not in the graph, or
no continuous path from that op to the cost).
* New tensor.isnan and isinf functions.
* MRG random generator now implements the same casting behavior as the regular random generator.
Sandbox New features(not enabled by default):
* New Linkers (theano flags linker={vm,cvm})
* The new linker allows lazy evaluation of the new ifelse op, meaning we compute only the true or false branch depending of the condition. This can speed up some types of computation.
* Uses a new profiling system (that currently tracks less stuff)
* The cvm is implemented in C, so it lowers Theano's overhead.
* The vm is implemented in python. So it can help debugging in some cases.
* In the future, the default will be the cvm.
* Some new not yet well tested sparse ops: theano.sparse.sandbox.{SpSum, Diag, SquareDiagonal, ColScaleCSC, RowScaleCSC, Remove0, EnsureSortedIndices, ConvolutionIndices}
Documentation:
* Better commenting of cuda_ndarray.cu
* Fixes in the scan documentation: add missing declarations/print statements
* Better error message on failed __getitem__
* Updated documentation on profile mode
* Better documentation of testing on Windows
* Better documentation of the 'run_individual_tests' script
Unit tests:
* More strict float comparaison by default
* Reuse test for subtensor of tensor for gpu tensor(more gpu test)
* Tests that check for aliased function inputs and assure appropriate copying
(#374)
* Better test of copies in CudaNdarray
* New tests relating to the new base pointer requirements
* Better scripts to run tests individually or in batches
* Some tests are now run whenever cuda is available and not just when it has
been enabled before
* Tests display less pointless warnings.
Other:
* Correctly put the broadcast flag to True in the output var of
a Reshape op when we receive an int 1 in the new shape.
* pydotprint: high contrast mode is now the default, option to print
more compact node names.
* pydotprint: How trunk label that are too long.
* More compact printing (ignore leading "Composite" in op names)
* How to compute the `Jacobian, Hessian, Jacobian times a vector, Hessian times a vector <http://deeplearning.net/software/theano/tutorial/gradients.html>`_.
* Slide for a 3 hours class with exercises that was done at the HPCS2011 Conference in Montreal.
Others:
* Logger name renamed to be consistent.
* Logger function simplified and made more consistent.
* Fixed transformation of error by other not related error with the compute_test_value Theano flag.
* Compilation cache enhancements.
* Made compatible with NumPy 1.6 and SciPy 0.9
* Fix tests when there was new dtype in NumPy that is not supported by Theano.
* Fixed some tests when SciPy is not available.
* Don't compile anything when Theano is imported. Compile support code when we compile the first C code.
* Python 2.4 fix:
* Fix the file theano/misc/check_blas.py
* For python 2.4.4 on Windows, replaced float("inf") with numpy.inf.
* Removes useless inputs to a scan node
* Beautification mostly, making the graph more visible. Such inputs would appear as a consequence of other optimizations
Core:
* there is a new mechanism that lets an Op permit that one of its
inputs to be aliased to another destroyed input. This will generally
result in incorrect calculation, so it should be used with care! The
right way to use it is when the caller can guarantee that even if
these two inputs look aliased, they actually will never overlap. This
mechanism can be used, for example, by a new alternative approach to
implementing Scan. If an op has an attribute called
"destroyhandler_tolerate_aliased" then this is what's going on.
IncSubtensor is thus far the only Op to use this mechanism.Mechanism
"If 'None', we warn about all Theano bugs found by default. If 'all', we don't warn about Theano bugs found by default. If a version, we print only the warnings relative to Theano bugs found after that version. Warning for specific bugs can be configured with specific [warn] flags.",
print'Try to run this script a few times. Experience show that the first time is not as fast as followings call. The difference is not big, but consistent.'
returnt1-t0
...
...
@@ -103,7 +105,7 @@ if __name__ == "__main__":
* manually compiled numpy and ATLAS with 2 threads
* goto 1.26 with 1, 2, 4 and 8 threads.
* goto2 1.13 compiled with multiple thread enabled.
