* The random number generator in theano/sandbox/rng_mrg.py did not always return the same sequence of number on the CPU and GPU.
* In some cases, there was a small fraction of garbage in the returned sequence,
but that garbage looked random. So if your usage did not depend too much on the random properties, you might be OK.
* In python mode (not the default mode) when input of elemwise operation was an empty ndarray, we were not returning an empty ndarray.
* Some segfault at exit with GPU code.
* Some bugs in Scan:
* Scan was incorrectly caching the number of steps to execute
This affect you only if you change the number of step of a compiled scan op. Constant number of step were ok.
* others: Razvan?
* In GpuConv, errors in conv_patch_stack_reduce when the entire kernel doesn't fit into shared memory.
The error was not found before as the impact was less then the relative tolerance of 1e-3. Now the relative tolerance is 1e-5.
Crash fixed:
* Add a feature to not have an exception that makes Theano crash when taking the gradient on DimShuffle in some particular case.
* Compilation crash for GpuElemwise with tensor with high number of dimensions(~6 or more).
* Disabled C code generator that make gcc crash on complex type.
* Crash in optimization when an Op has no input.
* output shape is now computed correctly for matrix-vector multiplication on GPU.
* In Scan, when using numbers as inputs, not symbolic variables
* In GpuSum, bug in calculation of n_blocks for the 10 pattern
(Sum on the row of a matrix)
Optimization:
* New SpecifyShape op that allow to pass more shape info in the graph.
* Speed up gemv by a work around scipy gemv slowness when the matrix is in C order (the default).
* Remove join of only 1 element
* During optimization, consider one more case in get_constant_value.
GPU:
* cuda_shared.value = X now works inplace!
* cuda_shared_var.set_value(new_ndarray) will overwrite the old value inplace in the most common case.
* Allow to create a CudaNdarraySharedVariable from a CudaNdarray.
* new init_gpu_device theano flags.
* Fuse GpuElemwise more often (in the case where there are so many inputs that fusing them all would bust the 256 bytes limit of parameter to gpu function).
* Cpu join of only 1 element that was not moved to the gpu.
New features:
* Tensor.reshape now makes dimensions of length broadcastable (fixes #434).
* Tensor.prod now implements the gradient
* DebugMode now warns if an Op declared itself as returning a view of the input but did not do so.
* This behaviour is a problem, because it can block other Ops from being inplace on the same inputs. This could lower the reuse of memory.
* Sparse.structured_dot now works when both matrices are sparse
* Sparse type is now supported by the shape op, and the ShapeFeature optimizer works correctly with them.
* New 3D convolution ops, with CPU and GPU implementations.
* New colors in pydotprint.
Documentation:
* Documented lib.amdlibm and (new) init_gpu_device config variables.
* A new page (was done for 0.3 but an error was hiding it on the web page) on the memory aliasing contract of Theano.
* Revision to the Windows installation instructions.
* The cuda documentation is now generated on the web server.
* Better documentation of .theanorc and its sections.
Unit tests:
* Stop usage of deprecated functions or syntax in the unit tests.
* Better testing of GPU convolution nets.
* Make more tests able to use different random seeds.
* Tests of sparse now use default mode, not a hard-coded one.
* Remove some tests of unimplemented features.
Other:
* The name of compiledir now includes the Python version to make it easier for people with many Python versions
* Added theano.tensor.std as a shortcut to sqrt(var(input=input, axis=axis)).
* Whitespace, tabulation and indentation clean-up in the code.
* Better detection of memory sharing between variables.
