diff options
Diffstat (limited to 'src/python/doc')
| -rw-r--r-- | src/python/doc/cubical_complex_sum.inc | 3 | ||||
| -rw-r--r-- | src/python/doc/cubical_complex_tflow_itf_ref.rst | 40 | ||||
| -rw-r--r-- | src/python/doc/differentiation_sum.inc | 12 | ||||
| -rw-r--r-- | src/python/doc/installation.rst | 8 | ||||
| -rw-r--r-- | src/python/doc/ls_simplex_tree_tflow_itf_ref.rst | 53 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_sum.inc | 5 | ||||
| -rw-r--r-- | src/python/doc/rips_complex_tflow_itf_ref.rst | 48 | ||||
| -rw-r--r-- | src/python/doc/simplex_tree_sum.inc | 23 |
8 files changed, 179 insertions, 13 deletions
diff --git a/src/python/doc/cubical_complex_sum.inc b/src/python/doc/cubical_complex_sum.inc index e2fd55bb..f1cf25d4 100644 --- a/src/python/doc/cubical_complex_sum.inc +++ b/src/python/doc/cubical_complex_sum.inc @@ -10,6 +10,9 @@ | * :doc:`cubical_complex_user` | * :doc:`cubical_complex_ref` | | | * :doc:`periodic_cubical_complex_ref` | +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ + | | * :doc:`cubical_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | | | | + +--------------------------------------------------------------------------+--------------------------------------------------------------+-------------------------------------------------------------+ | .. image:: | * :doc:`cubical_complex_sklearn_itf_ref` | :Requires: `Scikit-learn <installation.html#scikit-learn>`_ | | img/sklearn.png | | | | :target: https://scikit-learn.org | | | diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst new file mode 100644 index 00000000..b32f5e47 --- /dev/null +++ b/src/python/doc/cubical_complex_tflow_itf_ref.rst @@ -0,0 +1,40 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +TensorFlow layer for cubical persistence +######################################## + +.. include:: differentiation_sum.inc + +Example of gradient computed from cubical persistence +----------------------------------------------------- + +.. testcode:: + + from gudhi.tensorflow import CubicalLayer + import tensorflow as tf + + X = tf.Variable([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=tf.float32, trainable=True) + cl = CubicalLayer(homology_dimensions=[0]) + + with tf.GradientTape() as tape: + dgm = cl.call(X)[0][0] + loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) + + grads = tape.gradient(loss, [X]) + print(grads[0].numpy()) + +.. testoutput:: + + [[ 0. 0. 0. ] + [ 0. 0.5 0. ] + [ 0. 0. -0.5]] + +Documentation for CubicalLayer +------------------------------ + +.. autoclass:: gudhi.tensorflow.CubicalLayer + :members: + :special-members: __init__ + :show-inheritance: diff --git a/src/python/doc/differentiation_sum.inc b/src/python/doc/differentiation_sum.inc new file mode 100644 index 00000000..3aec33df --- /dev/null +++ b/src/python/doc/differentiation_sum.inc @@ -0,0 +1,12 @@ +.. list-table:: + :widths: 40 30 30 + :header-rows: 0 + + * - :Since: GUDHI 3.5.0 + - :License: MIT + - :Requires: `TensorFlow <installation.html#tensorflow>`_ + +We provide TensorFlow 2 models that can handle automatic differentiation for the computation of persistence diagrams from complexes available in the Gudhi library. +This includes simplex trees, cubical complexes and Vietoris-Rips complexes. Detailed example on how to use these layers in practice are available +in the following `notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-optimization.ipynb>`_. Note that even if TensorFlow GPU is enabled, all +internal computations using Gudhi will be done on CPU. diff --git a/src/python/doc/installation.rst b/src/python/doc/installation.rst index cff84691..4eefd415 100644 --- a/src/python/doc/installation.rst +++ b/src/python/doc/installation.rst @@ -175,7 +175,7 @@ A complete configuration would be : Scikit-learn version 1.0.1 POT version 0.8.0 HNSWlib found - PyKeOps version [pyKeOps]: 1.5 + PyKeOps version [pyKeOps]: 2.1 EagerPy version 0.30.0 TensorFlow version 2.7.0 Sphinx version 4.3.0 @@ -396,7 +396,11 @@ mathematics, science, and engineering. TensorFlow ---------- -`TensorFlow <https://www.tensorflow.org>`_ is currently only used in some automatic differentiation tests. +The :doc:`cubical complex </cubical_complex_tflow_itf_ref>`, :doc:`simplex tree </ls_simplex_tree_tflow_itf_ref>` +and :doc:`Rips complex </rips_complex_tflow_itf_ref>` modules require `TensorFlow <https://www.tensorflow.org>`_ +for incorporating them in neural nets. + +`TensorFlow <https://www.tensorflow.org>`_ is also used in some automatic differentiation tests. Bug reports and contributions ***************************** diff --git a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst new file mode 100644 index 00000000..9d7d633f --- /dev/null +++ b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst @@ -0,0 +1,53 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +TensorFlow layer for lower-star persistence on simplex trees +############################################################ + +.. include:: differentiation_sum.inc + +Example of gradient computed from lower-star filtration of a simplex tree +------------------------------------------------------------------------- + +.. testcode:: + + from gudhi.tensorflow import LowerStarSimplexTreeLayer + import tensorflow as tf + import gudhi as gd + + st = gd.SimplexTree() + st.insert([0, 1]) + st.insert([1, 2]) + st.insert([2, 3]) + st.insert([3, 4]) + st.insert([4, 5]) + st.insert([5, 6]) + st.insert([6, 7]) + st.insert([7, 8]) + st.insert([8, 9]) + st.insert([9, 10]) + + F = tf.Variable([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=tf.float32, trainable=True) + sl = LowerStarSimplexTreeLayer(simplextree=st, homology_dimensions=[0]) + + with tf.GradientTape() as tape: + dgm = sl.call(F)[0][0] + loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) + + grads = tape.gradient(loss, [F]) + print(grads[0].indices.numpy()) + print(grads[0].values.numpy()) + +.. testoutput:: + + [2 4] + [-1. 1.] + +Documentation for LowerStarSimplexTreeLayer +------------------------------------------- + +.. autoclass:: gudhi.tensorflow.LowerStarSimplexTreeLayer + :members: + :special-members: __init__ + :show-inheritance: diff --git a/src/python/doc/rips_complex_sum.inc b/src/python/doc/rips_complex_sum.inc index 2cb24990..6931ebee 100644 --- a/src/python/doc/rips_complex_sum.inc +++ b/src/python/doc/rips_complex_sum.inc @@ -11,4 +11,7 @@ | | | | +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ | * :doc:`rips_complex_user` | * :doc:`rips_complex_ref` | - +----------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ + | | * :doc:`rips_complex_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+----------------------------------------------------------------------------------+ diff --git a/src/python/doc/rips_complex_tflow_itf_ref.rst b/src/python/doc/rips_complex_tflow_itf_ref.rst new file mode 100644 index 00000000..3ce75868 --- /dev/null +++ b/src/python/doc/rips_complex_tflow_itf_ref.rst @@ -0,0 +1,48 @@ +:orphan: + +.. To get rid of WARNING: document isn't included in any toctree + +TensorFlow layer for Vietoris-Rips persistence +############################################## + +.. include:: differentiation_sum.inc + +Example of gradient computed from Vietoris-Rips persistence +----------------------------------------------------------- + +.. testsetup:: + + import numpy + numpy.set_printoptions(precision=4) + +.. testcode:: + + from gudhi.tensorflow import RipsLayer + import tensorflow as tf + + X = tf.Variable([[1.,1.],[2.,2.]], dtype=tf.float32, trainable=True) + rl = RipsLayer(maximum_edge_length=2., homology_dimensions=[0]) + + with tf.GradientTape() as tape: + dgm = rl.call(X)[0][0] + loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0]))) + + grads = tape.gradient(loss, [X]) + print(grads[0].numpy()) + +.. testcleanup:: + + numpy.set_printoptions(precision=8) + +.. testoutput:: + + [[-0.5 -0.5] + [ 0.5 0.5]] + +Documentation for RipsLayer +--------------------------- + +.. autoclass:: gudhi.tensorflow.RipsLayer + :members: + :special-members: __init__ + :show-inheritance: diff --git a/src/python/doc/simplex_tree_sum.inc b/src/python/doc/simplex_tree_sum.inc index a8858f16..3ad1292c 100644 --- a/src/python/doc/simplex_tree_sum.inc +++ b/src/python/doc/simplex_tree_sum.inc @@ -1,13 +1,16 @@ .. table:: :widths: 30 40 30 - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ - | .. figure:: | The simplex tree is an efficient and flexible data structure for | :Author: Clément Maria | - | ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | | - | :alt: Simplex tree representation | | :Since: GUDHI 2.0.0 | - | :figclass: align-center | The data structure is described in | | - | | :cite:`boissonnatmariasimplextreealgorithmica` | :License: MIT | - | | | | - +----------------------------------------------------------------+------------------------------------------------------------------------+-----------------------------+ - | * :doc:`simplex_tree_user` | * :doc:`simplex_tree_ref` | - +----------------------------------------------------------------+------------------------------------------------------------------------------------------------------+ + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ + | .. figure:: | The simplex tree is an efficient and flexible data structure for | :Author: Clément Maria | + | ../../doc/Simplex_tree/Simplex_tree_representation.png | representing general (filtered) simplicial complexes. | | + | :alt: Simplex tree representation | | :Since: GUDHI 2.0.0 | + | :figclass: align-center | The data structure is described in | | + | | :cite:`boissonnatmariasimplextreealgorithmica` | :License: MIT | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ + | * :doc:`simplex_tree_user` | * :doc:`simplex_tree_ref` | + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ + | | * :doc:`ls_simplex_tree_tflow_itf_ref` | :requires: `TensorFlow <installation.html#tensorflow>`_ | + | | | | + +----------------------------------------------------------------+------------------------------------------------------------------------+---------------------------------------------------------+ |