--- doc/conf.py.orig 2020-11-23 15:59:25.572772033 -0700 +++ doc/conf.py 2020-11-23 15:59:35.724796795 -0700 @@ -136,7 +136,7 @@ if os.environ.get('READTHEDOCS') != 'Tru html_theme = 'sphinx_rtd_theme' def setup(app): - app.add_stylesheet("fix_rtd.css") + app.add_css_file("fix_rtd.css") # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". --- doc/extending/extending_theano_gpu.txt.orig 2020-11-14 20:22:36.000000000 -0700 +++ doc/extending/extending_theano_gpu.txt 2020-11-23 16:48:56.092972525 -0700 @@ -264,14 +264,14 @@ CGpuKernelBase Python File ~~~~~~~~~~~ -.. literalinclude:: ../../theano/gpuarray/tests/test_cgpukernelbase.py +.. literalinclude:: ../../tests/gpuarray/test_cgpukernelbase.py :language: python :pyobject: GpuEye ``tstgpueye.c`` ~~~~~~~~~~~~~~~ -.. literalinclude:: ../../theano/gpuarray/tests/c_code/tstgpueye.c +.. literalinclude:: ../../tests/gpuarray/c_code/tstgpueye.c :language: C Wrapping Exisiting Libraries --- doc/library/scan.txt.orig 2020-11-14 20:22:36.000000000 -0700 +++ doc/library/scan.txt 2020-11-23 16:23:49.166254031 -0700 @@ -682,4 +682,4 @@ reference .. autofunction:: theano.foldl .. autofunction:: theano.foldr .. autofunction:: theano.scan -.. autofunction:: theano.scan_checkpoints +.. autofunction:: theano.scan.checkpoints.scan_checkpoints --- doc/library/sparse/sandbox.txt.orig 2020-11-14 20:22:36.000000000 -0700 +++ doc/library/sparse/sandbox.txt 2020-11-23 16:25:00.134423267 -0700 @@ -19,5 +19,3 @@ API :members: .. automodule:: theano.sparse.sandbox.sp2 :members: -.. automodule:: theano.sparse.sandbox.truedot - :members: --- doc/library/tests.txt.orig 2020-11-14 20:22:36.000000000 -0700 +++ doc/library/tests.txt 2020-11-23 16:33:08.245587256 -0700 @@ -4,5 +4,5 @@ :mod:`tests` -- Tests ===================== -.. automodule:: tests.breakpoint +.. automodule:: theano.breakpoint :members: --- theano/gof/op.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ theano/gof/op.py 2020-11-23 15:59:35.726796800 -0700 @@ -341,13 +341,13 @@ class CLinkerOp(CLinkerObject): string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that - can be accessed by prepending "py_" to the name in the + can be accessed by prepending ```"py_"``` to the name in the list. outputs : list of strings Each string is the name of a C variable where the Op should store its output. The type depends on the declared type of the output. There is a corresponding python variable that - can be accessed by prepending "py_" to the name in the + can be accessed by prepending ```"py_"``` to the name in the list. In some cases the outputs will be preallocated and the value of the variable may be pre-filled. The value for an unallocated output is type-dependent. @@ -404,13 +404,13 @@ class CLinkerOp(CLinkerObject): string is the name of a C variable pointing to that input. The type of the variable depends on the declared type of the input. There is a corresponding python variable that - can be accessed by prepending "py_" to the name in the + can be accessed by prepending ```"py_"``` to the name in the list. outputs : list of strings Each string is the name of a C variable correspoinding to one of the outputs of the Op. The type depends on the declared type of the output. There is a corresponding - python variable that can be accessed by prepending "py_" to + python variable that can be accessed by prepending ```"py_"``` to the name in the list. sub : dict of strings extra symbols defined in `CLinker` sub symbols (such as 'fail'). --- theano/gof/opt.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ theano/gof/opt.py 2020-11-23 15:59:35.728796804 -0700 @@ -102,9 +102,9 @@ class Optimizer: Add features to the fgraph that are required to apply the optimization. For example: - fgraph.attach_feature(History()) - fgraph.attach_feature(MyFeature()) - etc. + fgraph.attach_feature(History()) + fgraph.attach_feature(MyFeature()) + etc. """ --- theano/scan/__init__.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ theano/scan/__init__.py 2020-11-23 16:21:47.909964873 -0700 @@ -25,9 +25,9 @@ of using ``scan`` over `for` loops in py * it allows the number of iterations to be part of the symbolic graph * it allows computing gradients through the for loop * there exist a bunch of optimizations that help re-write your loop -such that less memory is used and that it runs faster + such that less memory is used and that it runs faster * it ensures that data is not copied from host to gpu and gpu to -host at each step + host at each step The Scan Op should typically be used by calling any of the following functions: ``scan()``, ``map()``, ``reduce()``, ``foldl()``, --- theano/sparse/basic.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ theano/sparse/basic.py 2020-11-23 15:59:35.729796807 -0700 @@ -4342,9 +4342,9 @@ class ConstructSparseFromList(gof.Op): This create a sparse matrix with the same shape as `x`. Its values are the rows of `values` moved. Pseudo-code:: - output = csc_matrix.zeros_like(x, dtype=values.dtype) - for in_idx, out_idx in enumerate(ilist): - output[out_idx] = values[in_idx] + output = csc_matrix.zeros_like(x, dtype=values.dtype) + for in_idx, out_idx in enumerate(ilist): + output[out_idx] = values[in_idx] """ x_ = theano.tensor.as_tensor_variable(x) --- theano/tensor/extra_ops.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ theano/tensor/extra_ops.py 2020-11-23 17:01:03.085630196 -0700 @@ -1464,7 +1464,7 @@ def broadcast_shape(*arrays, **kwargs): Parameters ---------- - *arrays: `TensorVariable`s + *arrays: `TensorVariable` instances The tensor variables, or their shapes (as tuples), for which the broadcast shape is computed. arrays_are_shapes: bool (Optional) --- versioneer.py.orig 2020-11-14 20:22:36.000000000 -0700 +++ versioneer.py 2020-11-23 15:59:35.730796809 -0700 @@ -418,7 +418,7 @@ def run_command(commands, args, cwd=None return stdout, p.returncode -LONG_VERSION_PY['git'] = ''' +LONG_VERSION_PY['git'] = r''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build