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-rw-r--r--src/python/doc/differentiation.rst18
-rw-r--r--src/python/doc/differentiation_sum.inc14
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+:orphan:
+
+.. To get rid of WARNING: document isn't included in any toctree
+
+======================
+Differentiation manual
+======================
+
+.. include:: differentiation_sum.inc
+
+In this module, we provide neural network models for computing persistent homology. In particular, we provide TensorFlow 2 models that allow to compute persistence diagrams from complexes available in the Gudhi library, including simplex trees, cubical complexes and Vietoris-Rips complexes. These models can be incorporated at each step of a given neural network architecture, and can be used in addition to `PersLay <https://github.com/MathieuCarriere/gudhi/blob/perslay/src/python/gudhi/representations/perslay.py>`_ to produce topological features.
+
+TensorFlow models
+-----------------
+.. automodule:: gudhi.differentiation
+ :members:
+ :special-members:
+ :show-inheritance:
diff --git a/src/python/doc/differentiation_sum.inc b/src/python/doc/differentiation_sum.inc
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+.. table::
+ :widths: 30 40 30
+
+ +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+
+ | .. figure:: | Deep learning models for differentiating persistence diagrams. | :Author: Mathieu Carrière |
+ | img/ripsTF.png | | |
+ | | | :Since: GUDHI 3.1.0 |
+ | | | |
+ | | | :License: MIT |
+ | | | |
+ | | | :Requires: `TensorFlow 2 <installation.html#tensorflow>`_ |
+ +------------------------------------------------------------------+----------------------------------------------------------------+-------------------------------------------------------------+
+ | * :doc:`differentiation` |
+ +------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------+
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