added LSTM char-rnn example

Author: memo

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+# ofxMSATensorFlow
+[OpenFrameworks](http://openframeworks.cc) addon for Google's graph based numerical computation, machine intelligence, deep learning library [TensorFlow](https://www.tensorflow.org).
+
+This update includes the newly released **TensorFlow r1.0**, and might have broken backwards compatibility. Sorry about that (tbh tensorflow has been breaking backwards compatibility with every update!). Hopefully from now on the API should be a bit more stable. 
+
+Tested on **openFrameworks 0.9.8**.
+
+I provide precompiled libraries for **Linux** and **OSX**. For linux there are both **GPU** and **CPU**-only libs, OSX is **CPU**-only (I don't have a Mac with NVidia). I haven't touched Windows yet as building from sources is 'experimental' (and doing Linux and OSX was painful enough).
+
+You can find instructions and more information in the **[wiki](https://github.com/memo/ofxMSATensorFlow/wiki)**, particularly for **[Getting Started](https://github.com/memo/ofxMSATensorFlow/wiki/Getting-started)**.
+
+---
+
+TensorFlow is written in C/C++ with python bindings, and most of the documentation and examples are for python. This addon wraps the C/C++ backend (and a little bit of the new C++ FrontEnd) with a number of examples. The basic idea is:
+
+1. Build graphs and/or train models in python, Java, C++ or any other language/platform with tensorflow bindings
+2. Save the graphs or trained models to binary files
+3. Load the graphs or trained models in openframeworks, feed them data, manipulate, get results, play, and connect them to the ofUniverse
+
+You could potentially do steps 1-2 in openframeworks as well, but the python API is a bit more user-friendly for building graphs and training. 
+
+---
+## Examples
+The examples are quite basic. They shouldn't be considered *tensorflow* examples or tutorials, but they mainly just demonstrate loading and manipulating of tensorflow models in openFrameworks. I really need to include more, but do checkout Parag's [tutorials](https://github.com/pkmital/tensorflow_tutorials) and [Kadenze course](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-iv/info) (both for tensorflow python). Building and training those models in python, and then playing with them in openframeworks should be relatively straight forward. 
+
+
+### example-basic
+The hello world (no not MNIST, that comes next). Build a very simple graph in python that multiplies two numbers. Load the graph in openframeworks and hey presto. 100s of lines of code, just to build a simple multiplication function. 
+
+### example-mnist
+MNIST clasffication with two different models - shallow and deep. Both models are built and trained in python (in bin/py folder). Loaded, manipulated and interacted with in openframeworks. Toggle between the two models with the 'm' key.
+![](https://cloud.githubusercontent.com/assets/144230/12665280/8fa4612a-c62e-11e5-950e-eaec14d4211d.png)
+
+**Single layer softmax regression:** Very simple multinomial logistic regression. Quick'n'easy but not very good. Trains in seconds. Accuracy on test set ~90%. 
+Implementation of https://www.tensorflow.org/versions/0.6.0/tutorials/mnist/beginners/index.html
+
+**Deep(ish) Convolutional Neural Network:** Basic convolutional neural network. Very similar to LeNet. Conv layers, maxpools, RELU's etc. Slower and heavier than above, but much better. Trains in a few minutes (on CPU). Accuracy 99.2%
+Implementation of https://www.tensorflow.org/versions/0.6.0/tutorials/mnist/pros/index.html#build-a-multilayer-convolutional-network
+
+
+### example-inception3
+openframeworks implementation for image recognition using Google's 'Inception-v3' architecture network, pre-trained on ImageNet. Background info at https://www.tensorflow.org/versions/0.6.0/tutorials/image_recognition/index.html
+![](https://cloud.githubusercontent.com/assets/144230/12665278/8caf4e8a-c62e-11e5-962a-8cd97af173ff.png)
+
+
+### example-build-graph
+Builds a simple flow graph in directly in openframeworks/C++ without using any python. (really not very exciting to look at. more of a syntax demo than anything). Based on https://www.tensorflow.org/api_guides/cc/guide
+
+
+### example-tests
+Just some unit tests. Very boring for most humans. Possibly exciting for computers (or humans that get excited at the thought of computers going wrong).
+