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| author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2021-11-22 23:58:49 +0100 |
|---|---|---|
| committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2021-11-22 23:58:49 +0100 |
| commit | b966a15818fd7a397ed6edc2b17ee6e188df6df0 (patch) | |
| tree | 923382a47f5f9e783c9ad60781198e0addc90ea9 /src/python/doc | |
| parent | 00ac6d157cacf37cca2f2934226644d037c0ffe6 (diff) | |
small change on doc
Diffstat (limited to 'src/python/doc')
| -rw-r--r-- | src/python/doc/differentiation_sum.inc | 3 |
1 files changed, 2 insertions, 1 deletions
diff --git a/src/python/doc/differentiation_sum.inc b/src/python/doc/differentiation_sum.inc index 3dd8e59c..3aec33df 100644 --- a/src/python/doc/differentiation_sum.inc +++ b/src/python/doc/differentiation_sum.inc @@ -8,4 +8,5 @@ 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>`_. +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. |