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        "author": "Stephan Seitz",
        "author_email": "stephan.seitz@fau.de",
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            "Development Status :: 4 - Beta",
            "Programming Language :: Python"
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        "description": ".. image:: https://badge.fury.io/py/pystencils-autodiff.svg\n   :target: https://badge.fury.io/py/pystencils-autodiff\n   :alt: PyPI version\n\n.. image:: https://readthedocs.org/projects/pystencils-autodiff/badge/?version=latest\n    :target: https://pystencils-autodiff.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status===================\n\n.. image:: https://i10git.cs.fau.de/pycodegen/pystencils_autodiff/badges/master/pipeline.svg \n    :target: https://i10git.cs.fau.de/pycodegen/pystencils_autodiff\n    :alt: Gitlab CI\n\n.. image:: https://travis-ci.org/theHamsta/pystencils_autodiff.svg?branch=master\n    :target: https://travis-ci.org/theHamsta/pystencils_autodiff\n\n.. image:: https://codecov.io/gh/theHamsta/pystencils_autodiff/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/theHamsta/pystencils_autodiff\n\npystencils_autodiff\n===================\n\nThis repo adds automatic differentiation to `pystencils <https://i10git.cs.fau.de/pycodegen/pystencils>`_.\n\nInstallation\n------------\n\nInstall via pip:\n\n.. code-block:: bash\n\n   pip install pystencils-autodiff\n\nor if you downloaded this `repository <https://github.com/pycodegen/pystencils_autodiff>`_ using:\n\n.. code-block:: bash\n\n   pip install -e .\n\nThen, you can access the submodule `pystencils.autodiff`.\n\n.. code-block:: python\n\n    import pystencils.autodiff\n\nUsage\n-----\n\nCreate a `pystencils.AssignmentCollection` with pystencils:\n\n.. code-block:: python\n\n    import sympy\n    import pystencils\n\n    z, y, x = pystencils.fields(\"z, y, x: [20,30]\")\n\n    forward_assignments = pystencils.AssignmentCollection({\n        z[0, 0]: x[0, 0] * sympy.log(x[0, 0] * y[0, 0])\n    })\n\n    print(forward_assignments)\n\n\n.. code-block:: python\n\n    Subexpressions:\n    Main Assignments:\n         z[0,0] \u2190 x_C*log(x_C*y_C)\n   \nYou can then obtain the corresponding backward assignments:\n\n.. code-block:: python\n\n    from pystencils.autodiff import AutoDiffOp, create_backward_assignments\n    backward_assignments = create_backward_assignments(forward_assignments)\n\n    print(backward_assignments)\n\nYou can see the derivatives with respective to the two inputs multiplied by the gradient `diffz_C` of the output `z_C`.\n\n.. code-block:: python\n\n    Subexpressions:\n    Main Assignments:\n        \\hat{x}[0,0] \u2190 diffz_C*(log(x_C*y_C) + 1)\n        \\hat{y}[0,0] \u2190 diffz_C*x_C/y_C\n\nYou can also use the class `AutoDiffOp` to obtain both the assignments (if you are curious) and auto-differentiable operations for Tensorflow...\n\n.. code-block:: python\n\n    op = AutoDiffOp(forward_assignments)\n    backward_assignments = op.backward_assignments   \n\n    x_tensor = pystencils.autodiff.tf_variable_from_field(x)\n    y_tensor = pystencils.autodiff.tf_variable_from_field(y)\n    tensorflow_op = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='tensorflow')\n\n... or Torch:\n\n.. code-block:: python\n\n    x_tensor = pystencils.autodiff.torch_tensor_from_field(x, cuda=False, requires_grad=True)\n    y_tensor = pystencils.autodiff.torch_tensor_from_field(y, cuda=False, requires_grad=True)\n\n    z_tensor = op.create_tensorflow_op({x: x_tensor, y: y_tensor}, backend='torch')",
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