From 321a2e213df0a4707184595c399aa52e9bdad69b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ji=C5=99=C3=AD=20N=C4=9Bme=C4=8Dek?= Date: Sat, 24 Aug 2024 19:15:47 +0200 Subject: [PATCH] Fixing 404 errors of links to notebooks in the documentation (#143) I assume that the notebooks have been moved, but the documentation links did not reflect that **Legal Acknowledgement**\ By contributing to this software project, I agree my contributions are submitted under the BSD license. I represent I am authorized to make the contributions and grant the license. If my employer has rights to intellectual property that includes these contributions, I represent that I have received permission to make contributions and grant the required license on behalf of that employer. --- docs/notebooks.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/notebooks.rst b/docs/notebooks.rst index f7da92f4..ae587d87 100644 --- a/docs/notebooks.rst +++ b/docs/notebooks.rst @@ -14,7 +14,7 @@ The first set of notebooks demonstrates the basic mechanics of OMLT and shows ho * `index_handling.ipynb `_ shows how to use `IndexMapper` to handle the mappings between indexes. -* `bo_with_trees.ipynb `_ incorporates gradient-boosted trees into a Bayesian optimization loop to optimize the Rosenbrock function. +* `bo_with_trees.ipynb `_ incorporates gradient-boosted trees into a Bayesian optimization loop to optimize the Rosenbrock function. * `linear_tree_formulations.ipynb `_ showcases the different linear model decision tree formulations available in OMLT. @@ -24,7 +24,7 @@ The second set of notebooks gives application-specific examples: * `mnist_example_convolutional.ipynb `_ trains a convolutional neural network on MNIST and uses OMLT to find adversarial examples. -* `graph_neural_network_formulation.ipynb `_ transforms graph neural networks into OMLT and builds formulation to solve optimization problems. +* `graph_neural_network_formulation.ipynb `_ transforms graph neural networks into OMLT and builds formulation to solve optimization problems. * `auto-thermal-reformer.ipynb `_ develops a neural network surrogate (using sigmoid activations) with data from a process model built using `IDAES-PSE `_.