Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Related tags

Deep Learning Theano
`MILA will stop developing Theano <>`_.

The PyMC developers are continuing Theano development in a `fork <>`_.

To install the package, see this page:

For the documentation, see the project website:

Related Projects:

It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package.

In order to build the documentation yourself, you will need sphinx. Issue the following command:


   python ./doc/scripts/

Documentation is built into ``html/``

The PDF of the documentation can be found at ``html/theano.pdf``


``Theano`` (current directory) is the distribution directory.

* ``Theano/theano`` contains the package
* ``Theano/theano`` has several submodules:
  * ``gof`` + ``compile`` are the core
  * ``scalar`` depends upon core
  * ``tensor`` depends upon ``scalar``
  * ``sparse`` depends upon ``tensor``
  * ``sandbox`` can depend on everything else

* ``Theano/examples`` are copies of the example found on the wiki
* ``Theano/benchmark`` and ``Theano/examples`` are in the distribution, but not in
  the Python package
* ``Theano/bin`` contains executable scripts that are copied to the bin folder
  when the Python package is installed
* Tests are distributed and are part of the package, i.e. fall in
  the appropriate submodules
* ``Theano/doc`` contains files and scripts used to generate the documentation
* ``Theano/html`` is where the documentation will be generated
  • Faster algorithms and gradients for GpuCorrMM

    Faster algorithms and gradients for GpuCorrMM

    As a follow-up to #2023, this PR adds caffe's backward pass wrt. inputs to GpuCorrMM to implement border_mode="full". It passes all the tests and it's about 2-4x faster than simulating a full convolution with a padded valid convolution. On the way, it cleans up the code and adds some more elaborate documentation.

    There are some caveats, though, all stemming from the fact that the implementation in caffe is meant as a backward pass for a valid correlation:

    • With border_mode="valid", GpuCorrMM doesn't flip the kernels, but with border_mode="full", it does.
    • With border_mode="valid", subsampling is for subsampling the output, but with border_mode="full", it is for upsampling the input.
    • With border_mode="valid", pad is for padding the input, but with border_mode="full", it is for cropping the output.
    • With border_mode="full", it needs a different memory layout for the kernels.

    Currently, GpuCorrMM directly wraps the underlying algorithm, and local_conv_gemm() copes with the different peculiarities, because I wasn't sure whether the GpuCorrMM Op should be inserting dimshuffles, kernel flips and gpu_contiguouses on its own.

    Looking at the end result, although it's quite fast, local_conv_gemm() now basically undoes everything that ConvOp.grad() does. It might be possible to write an optimizer that replaces the gradient of a valid convolution with a properly parameterized GpuCorrMM Op, instead of just replacing the full convolution Op that is part of the gradient (introducing redundant dimshuffles etc. on the way). This way we would leverage the caffe implementation better. The alternative would be to modify the CUDA code to perform a subsampled, padded, full correlation, as would be expected from a Gpu Correlation Op. This would be cleaner, but it would also mean a lot more work and we wouldn't profit as much from caffe's implementation for the case of subsampling != (1,1) or padding != (0,0) (granted, this may be an uninteresting case in practice anyway). /edit: Another alternative would be splitting this into two Ops: GpuCorrMM for valid correlation with padding and subsampling (the caffe forward pass), and GpuConvMM for full convolution with upsampling and cropping (the caffe backward pass). This way it would be obvious how the operations differ, but the memory layout required for the second Op would still be unintuitive. Yet another alternative would be splitting it into GpuCorrMM and GpuCorrMM_gradInput. This way it would be obvious how to use GpuCorrMM_gradInput for computing the gradient of GpuCorrMM. We could even add GpuCorrMM_gradKernel for the gradient wrt. weights; it seems caffe does things slightly different there as well. In both cases, GpuCorrMM should get a grad() method to define its gradient (so it can be used directly in a CNN to avoid kernel flipping and whatnot) and local_conv_gemm() should still be able to replace any GpuConv instances with a gemm-based Op (so it can be used with existing CNN implementations).

    opened by f0k 99
  • caffe conv kernel for theano. tests work, but needs integration and some...

    caffe conv kernel for theano. tests work, but needs integration and some...

    The caffe convolution works and passes test, however, code needs some cleaning, which are marked with TODO in comments. I created a new file, theano/sandbox/cuda/tests/ that calls GpuConvMM.


    • [x] Add support for the full mode

    Other possible follow up in gh-2015


    • Add faster convolution (Arjun Jain, Frederic B.)
    opened by stencilman 66
  • Baidu CTC wrapper

    Baidu CTC wrapper

    Fix and adapt an existing wrapper for Baidu CTC, then integrate it into tensor.nnet.

    If the warp-ctc library is not on the compiler's default library path, one should set the ctc.root variable to point to a local copy of the aforementioned library.

    Before executing the CTC wrapepr, it is necessary to have the library on the LD path, so one also has to change the LD_LIBRARY_PATH environment variable to include the directory that contains the library.

    For example:

    THEANO_FLAGS=`floatX=float32,ctc.root=/home/user/warp-ctc' python theano/tensor/nnet/tests/


    • [x] Port CPU implementation (done using a COp)
    • [x] Add tests and verification of results for CPU implementation (test done, more test cases required)
    • [x] CPU to GPU optimization
    • [x] Fix and port GPUOp to new backend
    • [x] Add tests and verification of results for GPU implementation
    • [x] Use OpenMP in CPU wrapper
    • [x] Use CUDA stream in GPU wrapper

    fix #3871

    opened by joaovictortr 65
  • Fix theano.gradient.grad

    Fix theano.gradient.grad

    Summary edit to reflect changes from code review on August 30 at 12PM EST.

    -Updates theano.gradient.grad to correctly discriminate between inputs that are not connected to the cost and inputs that have zero gradient -Updates theano.gradient.grad to compute only those gradients requested by the user -Removes the UncomputableOp machinery that was used to hack NaNs into the old grad method -Introduces NullType, used to represent undefined or unimplemented gradients -Adds an optional connection_pattern method to the definition of the op contract. Currently this is only used by the shape op, and will probably only be needed extremely rarely in the future. -Adds more unit tests to make sure gradient.grad works correctly -Changes one tests to KnownFailureTest. The test asserted that a disconnected input error was raised when one shouldn't be. Later this test can be changed to assert that the grad method does the right thing, but we are still arguing on theano-dev about what the right thing is (0 or NaN) in this case. -Introduces the connection_pattern method and DisconnectedType

    I recommend when reading this pull request you look at the final diff rather than each commit individually. I made several changes in the process of finishing this pull request so several lines in the early commits were later removed.

    opened by goodfeli 63
  • Abort Trap when importing Theano

    Abort Trap when importing Theano

    I am getting

    >>> import theano
    WARNING (theano.gof.compilelock): Overriding existing lock by dead process '83772' (I am process '83854')
    Fatal Python error: PyThreadState_Get: no current thread
    Abort trap: 6

    which then kills Python. The Mac OS X bug report is

    Process:         python2.7 [91560]
    Path:            /sw/*/python2.7
    Identifier:      python2.7
    Version:         ???
    Code Type:       X86-64 (Native)
    Parent Process:  bash [41983]
    User ID:         503
    Date/Time:       2013-04-03 13:18:48.483 -0600
    OS Version:      Mac OS X 10.8.3 (12D78)
    Report Version:  10
    Interval Since Last Report:          276342 sec
    Crashes Since Last Report:           1
    Per-App Crashes Since Last Report:   1
    Anonymous UUID:                      563FC21E-12C7-8C2A-E396-225C427EB6F4
    Crashed Thread:  0  Dispatch queue:
    Exception Type:  EXC_CRASH (SIGABRT)
    Exception Codes: 0x0000000000000000, 0x0000000000000000
    Application Specific Information:
    abort() called
    Thread 0 Crashed:: Dispatch queue:
    0   libsystem_kernel.dylib          0x00007fff82c4fd46 __kill + 10
    1   libsystem_c.dylib               0x00007fff8a54adf0 abort + 177
    2   org.python.python               0x00000001078abeaa Py_FatalError + 49
    3   org.python.python               0x00000001078aa370 PyThreadState_Get + 28
    4   org.python.python               0x00000001078a5f16 Py_InitModule4_64 + 58
    5               0x0000000107852f6c initlazylinker_ext + 124 (mod.cpp:1004)
    6   libpython2.7.dylib              0x0000000105208331 _PyImport_LoadDynamicModule + 177
    7   libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    8   libpython2.7.dylib              0x0000000105207bdc load_next + 268
    9   libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
    10  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    11  libpython2.7.dylib              0x00000001051ed4d7 PyEval_EvalFrameEx + 9911
    12  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    13  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
    14  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
    15  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    16  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
    17  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
    18  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    19  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    20  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    21  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    22  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    23  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    24  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
    25  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    26  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    27  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    28  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    29  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    30  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    31  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    32  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    33  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    34  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    35  libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
    36  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    37  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    38  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    39  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    40  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    41  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    42  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    43  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    44  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    45  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    46  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
    47  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    48  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    49  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    50  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    51  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    52  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    53  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    54  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    55  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    56  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    57  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
    58  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    59  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    60  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    61  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    62  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    63  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    64  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    65  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    66  libpython2.7.dylib              0x0000000105207823 load_package + 371
    67  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    68  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    69  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
    70  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    71  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    72  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    73  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    74  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    75  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    76  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    77  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    78  libpython2.7.dylib              0x0000000105207823 load_package + 371
    79  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    80  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    81  libpython2.7.dylib              0x0000000105205acb PyImport_ImportModuleLevel + 1211
    82  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    83  libpython2.7.dylib              0x00000001051ef91f PyEval_EvalFrameEx + 19199
    84  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    85  libpython2.7.dylib              0x00000001051f23cd fast_function + 285
    86  libpython2.7.dylib              0x00000001051ed578 PyEval_EvalFrameEx + 10072
    87  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    88  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    89  libpython2.7.dylib              0x0000000105204531 PyImport_ExecCodeModuleEx + 241
    90  libpython2.7.dylib              0x00000001052075ef load_source_module + 1231
    91  libpython2.7.dylib              0x0000000105208051 import_submodule + 337
    92  libpython2.7.dylib              0x0000000105207bdc load_next + 268
    93  libpython2.7.dylib              0x0000000105205b12 PyImport_ImportModuleLevel + 1282
    94  libpython2.7.dylib              0x00000001051e5fc8 builtin___import__ + 136
    95  libpython2.7.dylib              0x000000010515d1b1 PyObject_Call + 97
    96  libpython2.7.dylib              0x00000001051f1b78 PyEval_CallObjectWithKeywords + 168
    97  libpython2.7.dylib              0x00000001051ef132 PyEval_EvalFrameEx + 17170
    98  libpython2.7.dylib              0x00000001051eadd6 PyEval_EvalCodeEx + 1990
    99  libpython2.7.dylib              0x00000001051ea606 PyEval_EvalCode + 54
    100 libpython2.7.dylib              0x000000010521184e PyRun_FileExFlags + 174
    101 libpython2.7.dylib              0x00000001052113b9 PyRun_SimpleFileExFlags + 777
    102 libpython2.7.dylib              0x0000000105223458 Py_Main + 2952
    103 libdyld.dylib                   0x00007fff8b7c67e1 start + 1
    Thread 0 crashed with X86 Thread State (64-bit):
      rax: 0x0000000000000000  rbx: 0x00007fff5aaaf1f0  rcx: 0x00007fff5aaaf1d8  rdx: 0x0000000000000000
      rdi: 0x00000000000165a8  rsi: 0x0000000000000006  rbp: 0x00007fff5aaaf200  rsp: 0x00007fff5aaaf1d8
       r8: 0x00000000000003f5   r9: 0x0000000000000012  r10: 0x00007fff82c51342  r11: 0x0000000000000202
      r12: 0x00000001052b6628  r13: 0x0000000107856696  r14: 0x00000000000003f5  r15: 0x0000000000000000
      rip: 0x00007fff82c4fd46  rfl: 0x0000000000000202  cr2: 0x00007fff71683ff0
    Logical CPU: 0
    Binary Images:
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        0x7fff8b6ad000 -     0x7fff8b6b1fff  libpam.2.dylib (20) <C8F45864-5B58-3237-87E1-2C258A1D73B8> /usr/lib/libpam.2.dylib
        0x7fff8b752000 -     0x7fff8b753fff  libsystem_blocks.dylib (59) <D92DCBC3-541C-37BD-AADE-ACC75A0C59C8> /usr/lib/system/libsystem_blocks.dylib
        0x7fff8b762000 -     0x7fff8b774ff7  libz.1.dylib (43) <2A1551E8-A272-3DE5-B692-955974FE1416> /usr/lib/libz.1.dylib
        0x7fff8b7c4000 -     0x7fff8b7c7ff7  libdyld.dylib (210.2.3) <F59367C9-C110-382B-A695-9035A6DD387E> /usr/lib/system/libdyld.dylib
        0x7fff8b7c8000 -     0x7fff8b8c5ff7  libxml2.2.dylib (22.3) <47B09CB2-C636-3024-8B55-6040F7829B4C> /usr/lib/libxml2.2.dylib
        0x7fff8b957000 -     0x7fff8b98dfff  libsystem_info.dylib (406.17) <4FFCA242-7F04-365F-87A6-D4EFB89503C1> /usr/lib/system/libsystem_info.dylib
        0x7fff8b9f6000 -     0x7fff8c3866ff (1.600.0 - 331.0.4) <4953961C-96DC-39D7-ADF5-B767F2A7E4E1> /System/Library/Frameworks/ApplicationServices.framework/Versions/A/Frameworks/CoreGraphics.framework/Versions/A/CoreGraphics
        0x7fff8c387000 -     0x7fff8c3dcff7  libTIFF.dylib (849) <C4D0E196-9319-319B-AF72-8B63FB5AF71B> /System/Library/Frameworks/ImageIO.framework/Versions/A/Resources/libTIFF.dylib
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    This is with Fink Python, as you can see. I have

    >>> import numpy
    >>> import scipy
    >>> numpy.__version__
    >>> scipy.__version__
    opened by asmeurer 59
  • Newly introduced vector-vector product bug

    Newly introduced vector-vector product bug

    I'm experiencing what appears to be a serious bug. According to git bisect, the recent commit 14af439 is the culprit, merged in gh-1202.

    The following code now gives the wrong result on my machine. It should be 30.0, but after this commit results in 0.0.

    import numpy as np
    import theano
    import theano.tensor as tt
    v = tt.vector()
    f = theano.function([v],, v))
    testval = np.arange(5).astype(theano.config.floatX)
    print f(testval)
    Bug High Priority 
    opened by jlowin 57
  • Shape check input

    Shape check input


    • Compile less C modules.
    • Add Theano flags check_inputs=True. If False, won't check node inputs in c code. It can be used to speed up compilation, reduce overhead (particularly for scalars) and reduce the number of generated C files.


    • Add optional Op.check_inputs attribute, if False, as flag check_inputs, but for this op only and it is always the case.
    • Change Type.c_extract() and Type.c_declare() signature to have new check_inputs parameter.
    opened by Hengjean 52
  • Computation discrepancy between old and new GPU backends

    Computation discrepancy between old and new GPU backends

    I've tested the current master and the libgpuarray with my code on 2 different GPUs and for both backends.

    • On a Quadro K5200 (Kepler, SM=3.5), both backends reproduced equivalent results.
    • On a GTX980 (Maxwell, SM=5.2), gpuarray backend produces completely nonsense batch losses and the training diverges while old backend works without problem.

    How can we debug this?

    GPU - New back-end 
    opened by ozancaglayan 50
  • Unshared convolution

    Unshared convolution

    Related to #5620 .

    Task list :

    • [x] C code for grad inputs and tests

    • [x] All tests need to pass.

    • [x] Change weight tensor dimension ordering instead of dimshuffle

    • [x] Integrate unshared tests with AbstractConv

    • [x] GPU code

    opened by vikramnitin9 49
  • New destroy handler

    New destroy handler

    Issue #5976. Supervisor class is kept but we added a has_destroyers attribute to fgraph, but some tests regarding dumping the pickle failed because a new attribute was added to the fgraph. I changed it like this :

    fgraph.destroyers = [get_destroyers_of, has_destroyers]

    @nouiz what do you suggest?


    opened by ReyhaneAskari 44
  • RuntimeError: Failed to import pydot.

    RuntimeError: Failed to import pydot.

    I just tried running this example for printing theano graphs from ipython, and I got this error. Anyone possibly know why? If I try accessing theano.printing.pydotprint, it has no trouble finding the attribute.

    RuntimeError Traceback (most recent call last) in () 70 theano.printing.pydotprint(predict, 71 outfile="pics/logreg_pydotprint_predic.png", ---> 72 var_with_name_simple=True) 73 # before compilation 74 theano.printing.pydotprint_variables(prediction, /usr/local/lib/python2.7/dist-packages/theano/printing.pyc in pydotprint(fct, outfile, compact, format, with_ids, high_contrast, cond_highlight, colorCodes, max_label_size, scan_graphs, var_with_name_simple, print_output_file, assert_nb_all_strings) 566 567 if not pydot_imported: --> 568 raise RuntimeError("Failed to import pydot. You must install pydot" 569 " for pydotprint to work.") 570 return

    opened by mattdornfeld 43
  • TypeError: ufunc 'sin' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

    TypeError: ufunc 'sin' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

    Screenshot 2022-11-01 at 16 34 36

    Anyone know what this error means and how I can fix it. Any other tips on the code would be great too, I'm a first year mechanical engineer at uni with very little coding experience.

    opened by MatthewCouplan 1
  • theano error cannot convert 'cudnnConvolutionFwdAlgo_t*' to 'cudnnConvolutionFwdAlgoPerf_t

    theano error cannot convert 'cudnnConvolutionFwdAlgo_t*' to 'cudnnConvolutionFwdAlgoPerf_t

    My installed packages are: CUDA 11.7, cudnn 8.5, theano 1.0.5 When I run the convolution network in the test file of Michael Nielsen's book on Deep Learning:

        import network3
        from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer
        training_data, validation_data, test_data = network3.load_data_shared()
        mini_batch_size = 10
        net = Network([
                ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),filter_shape=(20, 1, 5, 5), poolsize=(2, 2)),FullyConnectedLayer(n_in=20*12*12, n_out=100),SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
        net.SGD(training_data, 60, mini_batch_size, 0.1, 
                    validation_data, test_data)   

    I bump into the following error:

        Problem occurred during compilation with the command line below:
        /MYHOME/anaconda3/envs/behi/bin/x86_64-conda-linux-gnu-c++ -shared -g -O3 -fno-math-errno -Wno-unused-label -Wno-unused-variable -Wno-write-strings -Wl,-rpath,/usr/local/cuda-11.7/lib64 -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -m64 -fPIC -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/pygpu -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/numpy/core/include -I/MYHOME/anaconda3/envs/behi/include -I/usr/local/cuda-11.7/include -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/theano/gpuarray/c_code -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/numpy/core/include -I/MYHOME/anaconda3/envs/behi/include/python3.10 -I/MYHOME/anaconda3/envs/behi/lib/python3.10/site-packages/theano/gof/c_code -L/MYHOME/anaconda3/envs/behi/lib -L/usr/local/cuda-11.7/lib64 -L/MYHOME/anaconda3/envs/behi/lib -fvisibility=hidden -o /MYHOME/.theano/compiledir_Linux-5.15--generic-x86_64-with-glibc2.35-x86_64-3.10.4-64/tmpf3qvese9/ /MYHOME/.theano/compiledir_Linux-5.15--generic-x86_64-with-glibc2.35-x86_64-3.10.4-64/tmpf3qvese9/mod.cpp -lgpuarray -lcudnn -lpython3.10dnn_fwd.c: In member function 'int {anonymous}::__struct_compiled_op_mc1cd627e616bc8abc33fbfac83f3f655fef0d5f1dc7018ab317b39356ccb88a5::conv_fwd_node_mc1cd627e616bc8abc33fbfac83f3f655fef0d5f1dc7018ab317b39356ccb88a5_0(PyGpuArrayObject*, PyGpuArrayObject*, PyGpuArrayObject*, cudnnConvolutionDescriptor_t, double, double, PyGpuArrayObject**, _Params_c1c8b4ff173698da406f46c90c8211df3674b0478655765dc1ebaa6eb2c3f1af_1348bc2b76f74f3ee4c82e363ec6eacd0522bb7d4a29dd246904b25b733edb4a*)':
        dnn_fwd.c:326:60: error: invalid conversion from 'size_t {aka long unsigned int}' to 'int*' [-fpermissive]
        dnn_fwd.c:326:60: error: cannot convert 'cudnnConvolutionFwdAlgo_t*' to 'cudnnConvolutionFwdAlgoPerf_t* {aka cudnnConvolutionFwdAlgoPerfStruct*}' for argument '8' to 'cudnnStatus_t cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t, cudnnTensorDescriptor_t, cudnnFilterDescriptor_t, cudnnConvolutionDescriptor_t, cudnnTensorDescriptor_t, int, int*, cudnnConvolutionFwdAlgoPerf_t*)'

    I understand that incompatibility of packages should be the reason, however I was wondering if there is any way to resolve this by modify the module or dnn_fwd.c or the theano's config file without downgrading the packages?

    opened by drb3hn4m 0
  • unexpected behaviour in dimension expansion

    unexpected behaviour in dimension expansion

    When working with pymc3 (3.11.2) and theano (1.1.2) I found that dimension expansion of theano tensors shows some unexpected behaviour, compared to numpy style advanced indexing.

    This code works:

    import theano.tensor as T
    A = T.zeros((3,4))
    B = A[None,:,:]

    While leaving out the last axis indexer raises a ValueError:

    import theano.tensor as T
    A = T.zeros((3,4))
    B = A[None,:]

    ValueError: ('You cannot drop a non-broadcastable dimension.', ([False, False], ['x', 0]))

    In numpy you can usually leave out trailing ",:" axis indexers. In theano it seems they are necessary so the dimshuffle functions works correctly.

    I don't know if this is fixed in any new version or if it is necessary to fix it at all. I just want to drop this somewhere on the internet for other people to find it, because that error annoyed me for a long time.

    opened by AlexanderWinterl 0
  • Error while using pymc3 and Theano-PyMC package

    Error while using pymc3 and Theano-PyMC package

    I am trying to use AutomatedRecommendationTool - A machine learning Automated Recommendation Tool for guiding synthetic biology. It uses a package named pymc3. But there are some issues regarding the compiler. Following is the Error:

    Exception: ('Compilation failed (return status=1): C:\\Users\\vaibh\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19044-SP0-AMD64_Family_23_Model_96_Stepping_1_AuthenticAMD-3.9.12-64\\tmpqepy79wz\\mod.cpp:1:0: sorry, unimplemented: 64-bit mode not compiled in\r.  #include <Python.h>\r.  \r. ', 'FunctionGraph(Elemwise{mul,no_inplace}(TensorConstant{3.141592653589793}, TensorConstant{0.01}))')

    Following is the code cell that I am trying to run -

    if run_art:
        art = RecommendationEngine(df, **art_params)
        with open(os.path.join(art_params['output_directory'], 'art.pkl'), 'rb') as output:
            art = pickle.load(output)

    I am using a jupyter notebook for executing the code. Following are the system specifications - Processor: AMD Ryzen 5 4600H with Radeon Graphics 3.00 GHz System type: 64-bit Operating System, x64-based processor Operating System: Windows 10 I have also installed a c++ compiler (MingW) and have added its bin path to system variables:

    C:\Users\vaibh>g++ --version
    g++ ( GCC-6.3.0-1) 6.3.0
    Copyright (C) 2016 Free Software Foundation, Inc.
    This is free software; see the source for copying conditions.  There is NO

    Following are the packages that I have installed -

    pipenv: 2022.5.2 depinfo: 1.7.0 python-libsbml(or just libsbml): 5.19.5 rfc3986: 2.0.0 h11: 0.13.0 rich: 12.4.4 pydantic: 1.9.1 diskcache: 5.4.0 importlib_resources: 5.7.1 Semver: 2.13.0 Pathvalidate: 2.5.0 pydoe: 0.3.8 tpot: 0.11.7 edd-utils: 0.0.12 pytorch: 1.11.0 mpi4py: 3.1.3 pymc3: 3.11.4 blas: 1.0

    Anybody who has been able to solve this issue, please help.

    Update: Theano-PyMC:1.1.2 was also installed as a dependency

    opened by Vaibhav-22-dm 0
  • configparser.NoSectionError: No section: 'blas' (Theano does not run probably on Python 3.9 and Numpy 1.22.2)

    configparser.NoSectionError: No section: 'blas' (Theano does not run probably on Python 3.9 and Numpy 1.22.2)

    Theano does not run properly on Python 3.9 and Numpy 1.22.2 for me.

    I get this error:

    Traceback (most recent call last):
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 168, in fetch_val_for_key
        return theano_cfg.get(section, option)
      File "/usr/lib/python3.9/", line 781, in get
        d = self._unify_values(section, vars)
      File "/usr/lib/python3.9/", line 1152, in _unify_values
        raise NoSectionError(section) from None
    configparser.NoSectionError: No section: 'blas'
    During handling of the above exception, another exception occurred:
    Traceback (most recent call last):
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 327, in __get__
        val_str = fetch_val_for_key(self.fullname,
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 172, in fetch_val_for_key
        raise KeyError(key)
    KeyError: 'blas.ldflags'
    During handling of the above exception, another exception occurred:
    Traceback (most recent call last):
      File "/home/nutzer/projects-2022-04-06/subtitle2go/punctuator2/", line 4, in <module>
        import models
      File "/home/nutzer/projects-2022-04-06/subtitle2go/punctuator2/", line 4, in <module>
        import theano
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 124, in <module>
        from theano.scan_module import (scan, map, reduce, foldl, foldr, clone,
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/scan_module/", line 41, in <module>
        from theano.scan_module import scan_opt
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/scan_module/", line 60, in <module>
        from theano import tensor, scalar
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/", line 17, in <module>
        from theano.tensor import blas
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/", line 155, in <module>
        from theano.tensor.blas_headers import blas_header_text
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/tensor/", line 987, in <module>
        if not config.blas.ldflags:
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 332, in __get__
        val_str = self.default()
      File "/home/nutzer/projects-2022-04-06/subtitle2go/subtitle2go_env/lib/python3.9/site-packages/theano/", line 1284, in default_blas_ldflags
        blas_info = np.distutils.__config__.blas_opt_info
    AttributeError: module 'numpy.distutils.__config__' has no attribute 'blas_opt_info'

    As the error says, there is no blas_opt_info in my numpy config, but replacing line 1284 with:

             blas_info = np.distutils.__config__.blas_opt_info
            blas_info = np.distutils.__config__.blas_ilp64_opt_info

    works and then Theano works as expected! If someone else confirms this error I can do a PR where I'd use a try/except to catch the attribute error on line 1284, with another try to look for blas_ilp64_opt_info if blas_opt_info isn't available. That should work on older and newer numpy versions alike.

    opened by bmilde 3
  • ImportError: cannot import name  'is_same_graph'

    ImportError: cannot import name 'is_same_graph'

    When I try to import theano (or pymc3) I get:

    from theano.gof.toolbox import is_same_graph

    ImportError: cannot import name 'is_same_graph'

    Using python 3.6, Theano 1.0.5 and pymc3 3.10.0 installed via pip without errors.

    opened by bryguypgh 2
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null 136 Dec 28, 2022
ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

Xtra Computing Group 1.4k Dec 22, 2022
MacroTools provides a library of tools for working with Julia code and expressions.

MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an

FluxML 278 Dec 11, 2022
An end-to-end machine learning library to directly optimize AUC loss

LibAUC An end-to-end machine learning library for AUC optimization. Why LibAUC? Deep AUC Maximization (DAM) is a paradigm for learning a deep neural n

Andrew 75 Dec 12, 2022