add documentation

example_frozen_graph_char_rnn/README.md

 # Basic example
-This example demonstrates how to load and run a frozen graph developed in TensorFlow 1.
+This example demonstrates how to load and run a frozen graph developed in TensorFlow. More specific, we demonstrate how to port an example of _ofxMSATensorFlow_ to _ofxTensorFlow2_.
 
-### TensorFlow2
+### TensorFlow
+From Memo Akten's exmaple:
+
+> Models are trained and saved in python with this code (https://github.com/memo/char-rnn-tensorflow)
+and loaded in openframeworks for prediction.
+I'm supplying a bunch of pretrained models (bible, cooking, erotic, linux, love songs, shakespeare, trump),
+and while the text is being generated character by character (at 60fps!) you can switch models in realtime mid-sentence or mid-word.
+(Drop more trained models into the folder and they'll be loaded too).
+Note, all models are trained really quickly with no hyperparameter search or cross validation,
+using default architecture of 2 layer LSTM of size 128 with no dropout or any other regularisation.
 
 ### openFrameworks
 By default `ofxTF2::Model` will use the `SavedModel` format. To use the `FrozenGraph` format you may either add the type to the constructor or call `setModelType()` afterwards.
@@ -9,16 +18,43 @@ As the default names differ from the names in the `SavedModel` format make sure
 ```c++
 // set model type and i/o names
 model.setModelType(cppflow::model::TYPE::FROZEN_GRAPH);
-model.setup({{"x:0"}}, {{"Identity:0"}});
+std::vector<std::string> inputNames = {
+	"data_in",
+	"state_in",
+};
+std::vector<std::string> outputNames = {
+	"data_out",
+	"state_out",
+};
+model.setup(inputNames, outputNames);
 ```
+
 Afterwards you can load the _pb file_ using the `load()` function.
 ```c++
-// load the model, bail out on error
-if(!model.load("model.pb")) {
+if(!model.load(model_path)) {
 	std::exit(EXIT_FAILURE);
 }
 ```
+This approach is different to _ofxMSATensorFlow_ where you would name the input and output tensors when running the model.
+Lets have a look at the way we ran the model in _ofxMSATensorFlow_:
+```c++
+// run the model
+std::vector<tensorflow::Tensor> t_out;
+std::vector<std::string> fetch_tensors = { "data_out", "state_out" };
+session->Run({ { "data_in", t_data_in }, { "state_in", t_state_in } }, fetch_tensors, {}, &t_out);
+```
+In _ofxTensorFlow2_ we can use the `Model::run()` function (if we have a single input) or the more general `Model::runMultiModel()` function to execute the model on some input data:
+```c++
+// run the model
+std::vector<cppflow::tensor> vectorOfInputTensors = {t_data_in, t_state_in};
+auto vectorOfOutputTensors = model.runMultiModel(vectorOfInputTensors);
+```
+
+The class for the tensor also differs a little bit. In _ofxTensorFlow2_ we will use the `cppflow::tensor` class instead of `tensorflow::tensor`.
+
+As in _ofxMSATensorFlow_, we provide functions for coverting from a tensor to `ofImage, ofPixels` and `std::vector`, and the other way around, e.g. `ofxTF2::vectorToTensor`. Regarding the utilities section, there are now some new handy functions like `ofxTF2::setGPUMaxMemory` and `ofxTF2::ThreadedModel`, while other less general functions have been dropped. Especially `msa::tf::adjust_probs_with_temp` and `msa::tf::sample_from_prob` are not in the utilities anymore. For this reason, we put those functions into the example's source code.
 
-Everything else should work the same.
+__Note:__ With the current version of cppflow, calling `get_data<T>` or `shape()` on a default (uninitalised) `cppflow::tensor` will result in a segfault. Wile you usually don't need to call them yourself, this effects the previously mentioned conversion functions.
+Therefore, we've changed a few lines of code when priming the model with new data.
 
 Please understand that we wont be able to invest a lot of time in supporting this feature in the future.
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