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Announcing Theano 0.5
Upgrading to Theano 0.5 is recommended for everyone, but you should first make
sure that your code does not raise deprecation warnings with Theano 0.4.1.
Otherwise, in one case the results can change. In other cases, the warnings are
turned into errors (see below for details).
For those using the bleeding edge version in the
git repository, we encourage you to update to the `0.5` tag.
What's New
Important changes:
* Moved to github:
* Old trac ticket moved to assembla ticket:
* Theano vision: (Many people)
* Theano with GPU works in some cases on Windows now. Still experimental. (Sebastian Urban)
* See the Interface changes.
Interface Behavior Change (was deprecated and generated a warning since Theano 0.3 released Nov. 23rd, 2010):
* The current default value of the parameter axis of
theano.{max,min,argmax,argmin,max_and_argmax} is now the same as
numpy: None. i.e. operate on all dimensions of the tensor. (Frédéric Bastien, Olivier Delalleau)
Interface Features Removed (most were deprecated):
* The string modes FAST_RUN_NOGC and STABILIZE are not accepted. They were accepted only by theano.function().
Use Mode(linker='c|py_nogc') or Mode(optimizer='stabilize') instead.
* tensor.grad(cost, wrt) now always returns an object of the "same type" as wrt
(list/tuple/TensorVariable). (Ian Goodfellow, Olivier)
* A few tag.shape and Join.vec_length left have been removed. (Frederic)
* The .value attribute of shared variables is removed, use shared.set_value()
or shared.get_value() instead. (Frederic)
* Theano config option "home" is not used anymore as it was redundant with "base_compiledir".
If you use it, Theano will now raise an error. (Olivier D.)
* scan interface changes: (Razvan Pascanu)
- The use of `return_steps` for specifying how many entries of the output
to return has been removed. Instead, apply a subtensor to the output
returned by scan to select a certain slice.
- The inner function (that scan receives) should return its outputs and
updates following this order:
[outputs], [updates], [condition].
One can skip any of the three if not used, but the order has to stay unchanged.
Interface bug fixes:
* Rop in some case should have returned a list of one Theano variable, but returned the variable itself. (Razvan)
New deprecation (will be removed in Theano 0.6, warning generated if you use them):
* tensor.shared() renamed to tensor._shared(). You probably want to call theano.shared() instead! (Olivier D.)
New features:
* Adding 1D advanced indexing support to inc_subtensor and set_subtensor (James Bergstra)
* tensor.{zeros,ones}_like now support the dtype param as numpy (Frederic)
* Added configuration flag "exception_verbosity" to control the verbosity of exceptions (Ian)
* theano-cache list: list the content of the theano cache (Frederic)
* theano-cache unlock: remove the Theano lock (Olivier)
* tensor.ceil_int_div to compute ceil(a / float(b)) (Frederic)
* MaxAndArgMax.grad now works with any axis (The op supports only 1 axis) (Frederic)
* used by tensor.{max,min,max_and_argmax}
* tensor.{all,any} (Razvan)
* tensor.roll as numpy: (Matthew Rocklin, David Warde-Farley)
* Theano with GPU works in some cases on Windows now. Still experimental. (Sebastian Urban)
* IfElse now allows to have a list/tuple as the result of the if/else branches.
* They must have the same length and corresponding type (Razvan)
* Argmax output dtype is now int64 instead of int32. (Olivier)
* Added the element-wise operation arccos. (Ian)
* Added sparse dot with dense grad output. (Yann Dauphin)
* Optimized to Usmm and UsmmCscDense in some case (Yann)
* Note: and theano.sparse.structured_dot() always had a gradient with the same sparsity pattern as the inputs.
The new has a dense gradient for all inputs.
* GpuAdvancedSubtensor1 supports broadcasted dimensions. (Frederic)
New optimizations:
* AdvancedSubtensor1 reuses preallocated memory if available (scan, c|py_nogc linker) (Frederic)
* tensor_variable.size (as numpy) computes the product of the shape elements. (Olivier)
* sparse_variable.size (as scipy) computes the number of stored values. (Olivier)
* dot22, dot22scalar work with complex. (Frederic)
* Generate Gemv/Gemm more often. (James)
* Remove scan when all computations can be moved outside the loop. (Razvan)
* scan optimization done earlier. This allows other optimizations to be applied. (Frederic, Guillaume, Razvan)
* exp(x) * sigmoid(-x) is now correctly optimized to the more stable form sigmoid(x). (Olivier)
* Added Subtensor(Rebroadcast(x)) => Rebroadcast(Subtensor(x)) optimization. (Guillaume)
* Made the optimization process faster. (James)
* Allow fusion of elemwise when the scalar op needs support code. (James)
* Better opt that lifts transpose around dot. (James)
Bug fixes (the result changed):
* On CPU, if the convolution had received explicit shape information, they where not checked at runtime.
This caused wrong result if the input shape was not the one expected. (Frederic, reported by Sander Dieleman)
* Scan grad when the input of scan has sequences of different lengths. (Razvan, reported by Michael Forbes)
* Scan.infer_shape now works correctly when working with a condition for the number of loops.
In the past, it returned n_steps as the length, which is not always true. (Razvan)
* Theoretical bug: in some case we could have GPUSum return bad value.
We were not able to reproduce this problem
* patterns affected ({0,1}*nb dim, 0 no reduction on this dim, 1 reduction on this dim):
01, 011, 0111, 010, 10, 001, 0011, 0101 (Frederic)
* div by zero in verify_grad. This hid a bug in the grad of Images2Neibs. (James)
* theano.sandbox.neighbors.Images2Neibs grad was returning a wrong value.
The grad is now disabled and returns an error. (Frederic)
Crashes fixed:
* T.mean crash at graph building time. (Ian)
* "Interactive debugger" crash fix. (Ian, Frederic)
* Do not call gemm with strides 0, some blas refuse it. (Pascal Lamblin)
* Optimization crash with gemm and complex. (Frederic)
* GPU crash with elemwise. (Frederic)
* Compilation crash with amdlibm and the GPU. (Frederic)
* IfElse crash. (Frederic)
* Execution crash fix in AdvancedSubtensor1 on 32 bit computers. (Pascal)
* GPU compilation crash on MacOS X. (Olivier)
* Support for OSX Enthought Python Distribution 7.x. (Graham Taylor, Olivier)
* When the subtensor inputs had 0 dimensions and the outputs 0 dimensions. (Frederic)
* Crash when the step to subtensor was not 1 in conjunction with some optimization. (Frederic, reported by Olivier Chapelle)
* fix dot22scalar cast of integer scalars (Justin Bayer, Frédéric, Olivier)
Known bugs:
* CAReduce with nan in inputs don't return the good output (`Ticket <>`_).
* This is used in tensor.{max,mean,prod,sum} and in the grad of PermuteRowElements.
* If you do grad of grad of scan you can have wrong results in some cases.
* cvm interface more consistent with current linker. (James)
* vm linker has a callback parameter. (James)
* review/finish/doc: diag/extract_diag. (Arnaud Bergeron, Frederic, Olivier)
* review/finish/doc: AllocDiag/diag. (Arnaud, Frederic, Guillaume)
* review/finish/doc: MatrixInverse, matrix_inverse. (Razvan)
* review/finish/doc: matrix_dot. (Razvan)
* review/finish/doc: det (determinent) op. (Philippe Hamel)
* review/finish/doc: Cholesky determinent op. (David)
* review/finish/doc: ensure_sorted_indices. (Li Yao)
* review/finish/doc: spectral_radius_boud. (Xavier Glorot)
* review/finish/doc: sparse sum. (Valentin Bisson)
Sandbox New features (not enabled by default):
* CURAND_RandomStreams for uniform and normal (not picklable, GPU only) (James)
* Many updates. (Many people)
* Updates to install doc on MacOS. (Olivier)
* Updates to install doc on Windows. (David, Olivier)
* Added how to use scan to loop with a condition as the number of iteration. (Razvan)
* Added how to wrap in Theano an existing python function (in numpy, scipy, ...). (Frederic)
* Refactored GPU installation of Theano. (Olivier)
* Better error messages in many places. (David, Ian, Frederic, Olivier)
* PEP8 fixes. (Many people)
* New min_informative_str() function to print graph. (Ian)
* Fix catching of exception. (Sometimes we catched interupt) (Frederic, David, Ian, Olivier)
* Better support for uft string. (David)
* Fix pydotprint with a function compiled with a ProfileMode (Frederic)
* Was broken with change to the profiler.
* Warning when people have old cache entries. (Olivier)
* More tests for join on the GPU and CPU. (Frederic)
* Don't request to load the GPU module by default in scan module. (Razvan)
* Fixed some import problems.
* Filtering update. (James)
* The buidbot now raises optimization errors instead of just printing a warning. (Frederic)
* On Windows, the default compiledir changed to be local to the computer/user and not transferred with roaming profile. (Sebastian Urban)
Reviewers (alphabetical order):
* David, Frederic, Ian, James, Olivier, Razvan
This is a major release, with lots of new features, bug fixes, and some
interface changes (deprecated or potentially misleading features were
removed). The upgrade is recommended for everybody, unless you rely on
deprecated features that have been removed.
You can download Theano from
Theano is a Python library that allows you to define, optimize, and
efficiently evaluate mathematical expressions involving
multi-dimensional arrays. It is built on top of NumPy. Theano
* tight integration with NumPy: a similar interface to NumPy's.
numpy.ndarrays are also used internally in Theano-compiled functions.
* transparent use of a GPU: perform data-intensive computations up to
140x faster than on a CPU (support for float32 only).
* efficient symbolic differentiation: Theano can compute derivatives
for functions of one or many inputs.
* speed and stability optimizations: avoid nasty bugs when computing
expressions such as log(1+ exp(x)) for large values of x.
* dynamic C code generation: evaluate expressions faster.
* extensive unit-testing and self-verification: includes tools for
detecting and diagnosing bugs and/or potential problems.
Theano has been powering large-scale computationally intensive
scientific research since 2007, but it is also approachable
enough to be used in the classroom (IFT6266 at the University of Montreal).
About Theano:
Theano-related projects:
About NumPy:
About SciPy:
Machine Learning Tutorial with Theano on Deep Architectures:
I would like to thank all contributors of Theano. For this particular
release, people names have been added next to what they did.
Also, thank you to all NumPy and Scipy developers as Theano builds on
their strengths.
All questions/comments are always welcome on the Theano
mailing-lists ( )