Backprop documentation got borked with new code added. (#926)

Also the ONNX how to import file was tested and it was discovered
that the wheel was actually output to a different directory than
the one specified.

doc/sphinx/source/howto/derive-for-training.rst

@@ -118,7 +118,7 @@ calculations for the adjustments to be further optimized.
 
 .. literalinclude:: ../../../examples/mnist_mlp.cpp
    :language: cpp
-   :lines: 172
+   :lines: 172-175
 
 
 For any node ``N``, if the update for ``loss`` is ``delta``, the
@@ -129,11 +129,12 @@ update computation for ``N`` will be given by the node
    auto update = loss->backprop_node(N, delta);
 
 The different update nodes will share intermediate computations. So to
-get the updated value for the weights we just say:
+get the updated values for the weights we just say:
 
 .. literalinclude:: ../../../examples/mnist_mlp.cpp
    :language: cpp
-   :lines: 168-178
+   :lines: 177-217
+
 
 .. _update:
 
@@ -152,5 +153,5 @@ compile clones of the nodes.
 
 .. literalinclude:: ../../../examples/mnist_mlp.cpp
    :language: cpp
-   :lines: 248-260
+   :lines: 220-226
 

doc/sphinx/source/howto/import.rst

@@ -67,18 +67,23 @@ skip ahead to the next section, :ref:`install_ngonnx`.
       $ export NGRAPH_CPP_BUILD_PATH=$HOME/ngraph_dist
       $ python3 setup.py bdist_wheel
 
-#. After the Python3 binary wheel file (``ngraph-*.whl``) is prepared, install  
-   with :command:`pip3`, or :command:`pip` in a virtual environment.
+#. Check for the binary wheel file under ``/ngraph/python/dist/`` and activate a 
+   Python3 virtual environment to work with ONNX.
 
    .. code-block:: console
 
-      (your_venv) $ pip install -U build/dist/ngraph-0.1.0-cp35-cp35m-linux_x86_64.whl
+      $ cd dist/
+      $ python3 -m venv onnx
+      $ cd onnx/
+      $ . bin/activate
+      (onnx)$ pip install -U ngraph-0.2.0-cp35-cp35m-linux_x86_64.whl    
+
 
 #. Confirm ngraph is properly installed through a Python interpreter:
 
    .. code-block:: console
 
-      (your_venv) $ python3
+      (onnx) $ python3
 
    .. code-block:: python
       
@@ -98,7 +103,7 @@ Install the ``ngraph-onnx`` companion tool using pip:
 
 .. code-block:: console
 
-   (your_venv) $ pip install git+https://github.com/NervanaSystems/ngraph-onnx/
+   (onnx) $ pip install git+https://github.com/NervanaSystems/ngraph-onnx/
  
 
 Importing a serialized model
@@ -115,6 +120,26 @@ that has been trained on the CIFAR10 data set and serialized with
 ONNX. 
 
 
+(Optional) Localize your export to the virtual environment 
+----------------------------------------------------------
+
+For this example, let's say that our serialized file was output under our $HOME 
+directory, say at ``~/onnx_conversions/trained_model.onnx``. To make loading this 
+file easier, you can run the example below from your Venv in that directory. If 
+you invoke your python interpreter in a different directory, you will need to 
+specify the relative path to the location of the ``.onnx`` file.
+
+.. important:: If you invoke your Python interpreter in directory other than 
+   where you outputted your trained model, you will need to specify the 
+   **relative** path to the location of the ``.onnx`` file.
+
+
+.. code-block:: console 
+
+   (onnx) $ cd ~/onnx_conversions 
+   (onnx) $ python3 
+
+
 Enable ONNX and load an ONNX file from disk
 --------------------------------------------