arm_compute v18.05

[platform/upstream/armcl.git] / docs / 03_scripts.dox

diff --git a/docs/03_scripts.dox b/docs/03_scripts.dox
index 5601428..eede8b5 100644 (file)
--- a/docs/03_scripts.dox
+++ b/docs/03_scripts.dox
@@ -8,7 +8,7 @@
 One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on
 caffe's official github repository.
 
-The caffe_data_extractor.py provided in the @ref scripts folder is an example script that shows how to
+The caffe_data_extractor.py provided in the scripts folder is an example script that shows how to
 extract the parameter values from a trained model.
 
 @note complex networks might require altering the script to properly work.
@@ -35,7 +35,7 @@ The script has been tested under Python2.7.
 If the script runs successfully, it prints the names and shapes of each layer onto the standard
 output and generates *.npy files containing the weights and biases of each layer.
 
-The @ref arm_compute::utils::load_trained_data shows how one could load
+The arm_compute::utils::load_trained_data shows how one could load
 the weights and biases into tensor from the .npy file by the help of Accessor.
 
 @section tensorflow_data_extractor Extract data from pre-trained tensorflow model
@@ -87,6 +87,6 @@ The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3
 If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates
  *.npy files containing the weights and biases of each layer.
 
-The @ref arm_compute::utils::load_trained_data shows how one could load
+The arm_compute::utils::load_trained_data shows how one could load
 the weights and biases into tensor from the .npy file by the help of Accessor.
 */