Read the Docs upload. First Test.

docs/source/_static/version_switch.js

@@ -23,7 +23,7 @@
     vswitch.push('<div class="rst-other-versions">');   
 
     vswitch.push('<dt>Downloads</dt>');
-    var pdf_url = root_url + "doc/build/latex/yann.pdf"
+    var pdf_url = root_url + "/yann.pdf"
     vswitch.push('<dd><a href=\"', pdf_url, '\">', 'PDF', '</a></dd>');
     vswitch.push('</dl>');  
 
@@ -35,8 +35,8 @@
 
     vswitch.push('<dl>');   
     vswitch.push('<dt>Creator</dt>');
-    var git_master = "http://www.public.asu.edu/~rvenka10/"
-    vswitch.push('<dd><a href=\"', "http://www.public.asu.edu/~rvenka10/" + '\">', 'Ragav Venkatesan', '</a></dd>');
+    var git_master = "http://www.ragav.net"
+    vswitch.push('<dd><a href=\"', "http://www.ragav.net" + '\">', 'Ragav Venkatesan', '</a></dd>');
     vswitch.push('</dl>');  
 
     vswitch.push('</div>');    
@@ -50,7 +50,7 @@
 
     // Check server for other doc versions and update switcher.
     if (url.startsWith('http')) {
-      $.getJSON(root_url + 'theano_versions/versions.json', function(data){
+      $.getJSON(root_url + 'yann_versions/versions.json', function(data){
         $.each(data, function(version, dir) {
             versions_dir[version] = dir;
         });

docs/source/conf.py

@@ -22,6 +22,17 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('../..'))
 
+import sys
+from unittest.mock import MagicMock
+
+class Mock(MagicMock):
+    @classmethod
+    def __getattr__(cls, name):
+            return Mock()
+
+MOCK_MODULES = ['sphinx_fontawesome']
+sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
+
 # General configuration
 # ---------------------
 

docs/source/index.rst

@@ -12,7 +12,7 @@ Yet Another Neural Network Toolbox
 .. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: license.html
 
-Welcome to the Yann Toolbox. It is a toolbox for doing convolutional neural networks, built on top 
+Welcome to the Yann Toolbox. It is a toolbox for convolutional neural networks, built on top 
 of `theano`_. To setup the toolbox refer the :ref:`setup` guide. Once setup you may start with the 
 :ref:`quick_start` guide or try your hand at the :ref:`tutorial` and the guide to 
 :ref:`getting_started`.
@@ -22,8 +22,7 @@ of `theano`_. To setup the toolbox refer the :ref:`setup` guide. Once setup you
 .. warning ::
 
     Yann is currently under its early phases and is presently undergoing massive development. 
-    Expect a lot of changes. Testing going on at the moment with the ``cook`` function and 
-    therefore some loss functions might not work temporarily.
+    Expect a lot of changes.
 
 .. warning ::
     
@@ -35,9 +34,12 @@ of `theano`_. To setup the toolbox refer the :ref:`setup` guide. Once setup you
     
     While, there are more formal and wholesome toolboxes that are similar and have a much larger 
     userbase such as `Lasagne`_, `Keras`_, `Blocks`_ and `Caffe`_, this toolbox is much more
-    simple and versatile. Yann is a supplement to an upcoming book on Convolutional Neural 
-    Networks. It is also good choice for a toolbox for running pre-trained models and build 
-    complicated, non-vannilla architecture that are not easy to build with the other toolboxes.
+    simple and versatile. Yann is designed as a supplement to an upcoming book on Convolutional 
+    Neural Networks and also the toolbox of choice for a deep learning course. Because of this 
+    reason, Yann is specifically designed to be intuitive and easy to use for beginners. That does 
+    not compromise Yann of any of its features. 
+    It is still a good choice for a toolbox for running pre-trained models and build 
+    complicated, non-vannilla CNN architectures that are not easy to build with the other toolboxes.
 
 .. _Lasagne: https://github.com/Lasagne/Lasagne
 .. _Keras: http://keras.io/

yann/network/__init__.py

@@ -1624,13 +1624,7 @@ def train(self, verbose = 2, **kwargs):
 
         nan_flag = False
         fine_tune = False 
-
-        index = T.lscalar()
-        temp_func = theano.function ( inputs = [index],
-                                      outputs = self.layers["conv_pool_1"].conv_out,
-                                      givens = { self.x: self.cooked_datastream.data_x[index * 
-                                                 self.cooked_datastream.batch_size:(index + 1) * 
-                                                             self.cooked_datastream.batch_size]})                                                             
+
         # main loop
         while (epoch_counter < (num_epochs + num_fine_tuning_epochs)) and (not early_termination):
 

yann/network/layers.py

@@ -41,9 +41,10 @@ def __init__(self, id, type, verbose = 2):
     def print_layer(self, prefix = " ", nest = True, last = True):
         """
         Print information about the layer
+        
         Args:
             nest: If True will print the tree from here on. If False it will print only this
-              layer.
+                layer.
             prefix: Is what prefix you want to add to the network print command.
         """       
         prefix_entry = prefix