From 94bd5f859871c04a633f0da07b913e46e2523c60 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Leon=20Michel=20Gori=C3=9Fen?=
<leon.gorissen@llt.rwth-aachen.de>
Date: Fri, 25 Oct 2024 14:47:22 +0000
Subject: [PATCH] Update TODOs [ci skip]
---
TODOs | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/TODOs b/TODOs
index 02c30e2..562bcd2 100644
--- a/TODOs
+++ b/TODOs
@@ -25,8 +25,8 @@ dynamics_learning/benchmark_number_of_runs.py:214: # TODO load ITA model
dynamics_learning/benchmark_number_of_runs.py:265: # TODO load ITA model instead of dummy model
dynamics_learning/dynamics_learning/testing/__init__.py:256: # somewhere FIXME: Daten scheinen zu kurz zu sein?
dynamics_learning/dynamics_learning/testing/__init__.py:640: # somewhere FIXME: Daten scheinen zu kurz zu sein?
-dynamics_learning/dynamics_learning/training/__init__.py:265: ) # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine.
-dynamics_learning/dynamics_learning/training/__init__.py:271: f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models based on MITM base profile
+dynamics_learning/dynamics_learning/training/__init__.py:275: ) # TODO implement another naming schema, as soon as a custom application profiles for the models is available in coscine.
+dynamics_learning/dynamics_learning/training/__init__.py:281: f"{decision_metric}_val_loss" # TODO implement custom coscine application profile for the models based on MITM base profile
dynamics_learning/train_instance.py:38: # TODO download data from ita / use data from ita
dynamics_learning/train_instance.py:71: # TODO save model and hyperparameters locally
franka_lock_unlock/README.rst:1:.. TODO describe docker compose up for individual component
--
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