Adding Resnet50 Readme to private-ngraph-cpp as requested in PR#2282 on private-ngraph

Signed-off-by: Evan Couzens <evan.couzens@intel.com>

README-RESNET.rst

+.. ---------------------------------------------------------------------------
+.. Copyright 2017 Intel Corporation
+.. Licensed under the Apache License, Version 2.0 (the "License");
+.. you may not use this file except in compliance with the License.
+.. You may obtain a copy of the License at
+..
+..      http://www.apache.org/licenses/LICENSE-2.0
+..
+.. Unless required by applicable law or agreed to in writing, software
+.. distributed under the License is distributed on an "AS IS" BASIS,
+.. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+.. See the License for the specific language governing permissions and
+.. limitations under the License.
+.. ---------------------------------------------------------------------------
+
+Intel® Nervana™ graph
+*********************
+
+Intel® Nervana™ graph is Intel's library for developing frameworks that can efficiently run deep learning computations on a variety of compute platforms. It consists of three primary API components:
+
+- An API for creating computational *Intel Nervana graphs*.
+- Two higher level frontend APIs (TensorFlow* and neon™) utilizing the Intel Nervana graph API for common deep learning workflows.
+- A transformer API for compiling and executing these graphs.
+
+For more information, refer to the `blog post <https://www.intelnervana.com/intel-nervana-graph-preview-release/?_ga=2.139466358.473888884.1509049473-747831713.1505851199/>`_ announcing our
+preview release!
+
+Installation
+============
+
+Follow the instructions in the installation documentation
+`here <https://ngraph.nervanasys.com/docs/latest/installation.html/>`_.
+
+After you complete the prerequisites and install the base Intel Nervana graph package as explained in the installation documentation, you will need to install some additional packages to run
+Intel Nervana graph on various compute platforms.
+
+CPU/Intel® architecture transformer
+---------------------------------------
+
+To run Intel Nervana graph on a CPU backend, you need to install Intel Nervana graph with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) support:
+
+  1. Download Intel® MKL-DNN from [here](https://github.com/01org/mkl-dnn).
+  2. Follow the installation instructions in the README.md to install MKL-DNN. 
+  3. Set the environment variable `MKLDNN_ROOT` to point to the location where you installed Intel MKL-DNN.
+  4. Follow the rest of the steps to install Nervana Graph::
+
+    export MKLDNN_ROOT=/path/to/mkldnn/root
+
+GPU transformer
+---------------
+
+To run Intel Nervana graph on a GPU backend, you need to install CUDA* and then enable the GPU transformer:
+
+  1. Download and install CUDA according to the `instructions <http://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html>`_.
+  2. On your system, enable the GPU transformer::
+
+    make gpu_prepare
+    
+
+Virtual environment activation
+==================================
+We recommend installing Intel Nervana graph inside a virtual environment. Make sure that you have installed all prerequisites in the installation documentation
+`here <https://ngraph.nervanasys.com/docs/latest/installation.html>`_.
+
+To create and activate a Python 3 virtualenv, run the following commands::
+  
+  python3 -m venv .venv
+  . .venv/bin/activate
+
+If you would like to create and activate a Python 2.7 virtualenv instead::
+
+  virtualenv -p python2.7 .venv
+  . .venv/bin/activate
+
+Build Intel Nervana graph
+=========================
+
+Run the following command within your virtual environment to build Intel Nervana graph::
+
+  make install
+
+
+Additional options
+==================
+
+Use these commands to run the tests::
+
+  make [test_cpu|test_mkldnn|test_gpu|test_integration]
+
+
+Before checking in code, ensure there are no "make style" errors by running the following check::
+  
+  make style
+
+
+If the check returns any errors, use this command to fix style errors::
+  
+  make fixstyle
+
+
+To generate the documentation as HTML files::
+  
+  sudo apt-get install pandoc
+  make doc
+
+
+Examples
+========
+
+- *examples/walk_through/* contains several code walk throughs.
+- *examples/mnist/mnist_mlp.py* uses the neon front-end to define and train a MLP model on MNIST data.
+- *examples/cifar10/cifar10_conv.py* uses the neon front-end to define and train a CNN model on CIFAR10 data.
+- *examples/cifar10/cifar10_mlp.py* uses the neon front-end to define and train a MLP model on CIFAR10 data.
+- *examples/ptb/char_rnn.py* uses the neon front-end to define and train a character-level RNN model on Penn Treebank data.
+
+Training deep residual networks
+===============================
+
+This example demonstrates training a deep residual network as first described in `He et. al. msra1 <http://arxiv.org/abs/1512.03385>`_. It can handle CIFAR10 and Imagenet datasets
+
+Files
+-----
+
+- *data.py*: Loads CIFAR10 or imagenet dataset and creates aeon objects.
+- *resnet.py*: Defines object for Residual network.
+- *train_resnet.py*: Trains the resnet depending on dataset and size choosen.
+
+Dataset
+-------
+
+The `CIFAR10` Dataset gets downloaded automatically to *~/*. To download and use the dataset from a specific location, set ``--data_dir i1k``.
+For imagenet, update ``manifest_root`` to the location of your imagenet dataset in *data.py*. Also update ``path`` to the directory where manifest ``.csv`` files are stored in *data.py*.
+
+Usage
+-----
+
+Use the following command to run training on Intel Nervana graph::
+
+  python train_resnet.py -b <cpu,gpu> --size <20,56> -t 64000 -z <64,128>
+
+Citation
+--------
+
+`Deep Residual Learning for Image Recognition <http://arxiv.org/abs/1512.03385>`_
+