Formatting

Author: lamblin

doc/cnn_1D_segm.txt

@@ -151,11 +151,12 @@ The FCN implementation can be found in the following file:
 * `fcn1D.py <../code/cnn_1D_segm/fcn1D.py>`_ : Main script. Defines the model.
 * `train_fcn1D.py <../code/cnn_1D_segm/train_fcn1D.py>`_ : Training loop
 
-Change the *dataset_loaders/config.ini* file and add the right path for the dataset:
+Change the ``dataset_loaders/config.ini`` file and add the right path for the dataset:
 
-[cortical_layers]
+.. code-block:: cfg
 
-shared_path = /path/to/DeepLearningTutorials/data/cortical_layers/
+    [cortical_layers]
+    shared_path = /path/to/DeepLearningTutorials/data/cortical_layers/
 
 
 First define a *bn+relu+conv* block that returns the name of the last layer of

doc/fcn_2D_segm.txt

@@ -182,11 +182,12 @@ The FCN-8 implementation can be found in the following file:
 The user must install `Lasagne <http://lasagne.readthedocs.io/en/latest/user/installation.html>`_ ,
 and clone the GitHub repo `Dataset Loaders <https://github.com/fvisin/dataset_loaders>`_.
 
-Change the *dataset_loaders/config.ini* file and add the right path for the dataset:
+Change the ``dataset_loaders/config.ini`` file and add the right path for the dataset:
 
-[polyps912]
+.. code-block:: cfg
 
-shared_path = /path/to/DeepLearningTutorials/data/polyps_split7/
+    [polyps912]
+    shared_path = /path/to/DeepLearningTutorials/data/polyps_split7/
 
 We used Lasagne layers, as you can see in the code below.
 

doc/unet.txt

@@ -113,12 +113,12 @@ The user must install `Lasagne <http://lasagne.readthedocs.io/en/latest/user/ins
 `SimpleITK <http://www.simpleitk.org/SimpleITK/resources/software.html>`_ and 
 clone the GitHub repo `Dataset Loaders <https://github.com/fvisin/dataset_loaders>`_.
 
-Change the *dataset_loaders/config.ini* file to set the right path for the dataset:
+Change the ``dataset_loaders/config.ini`` file to set the right path for the dataset:
 
-[isbi_em_stacks]  
+.. code-block:: cfg
 
-
-shared_path = /path/to/DeepLearningTutorials/data/isbi_challenge_em_stacks
+    [isbi_em_stacks]
+    shared_path = /path/to/DeepLearningTutorials/data/isbi_challenge_em_stacks
 
 
 The user can now build a U-Net with a specified number of input channels and number of classes.