Update sample app document (#5104)

- Describe runtime with tensorfliw lite is not official support
- Tensorflow -> tensorflow lite
- Update chapter "Build a model": title, etc.

Signed-off-by: Hyeongseok Oh <hseok82.oh@samsung.com>

1 files changed, 15 insertions, 11 deletions

diff --git a/docs/howto/HowtoMakeSampleAppOnNnfw.md b/docs/howto/HowtoMakeSampleAppOnNnfw.md
index 7a8689b99..0bd65c4b4 100644
--- a/docs/howto/HowtoMakeSampleAppOnNnfw.md
+++ b/docs/howto/HowtoMakeSampleAppOnNnfw.md
@@ -1,24 +1,28 @@
 # How to make a sample app on nnfw
 
-## Build a model
+Our runtime `neurun` support `NNAPI` as interface currently. To use `NNAPI` efficiently, one of solution is to use tensorflow lite. We support additional library to help using tensorflow lite in `/libs/tflite`. (this library is not official support)
 
-Include necessary headers. // TODO: which `BuiltinOpResolver`: ours or the one from tensorflow? Remove one of these includes
+To use tensorflow lite, you need to prepare tensorflow lite model file, and you should know input/output tensor name. Then write sample app.
+
+## Prepare loaded tensorflow lite model object
+
+You can select one of kernel register: tensorflow lite official kernel register or extended register (for pre-implemented custom op)
 ```
 #include "tensorflow/contrib/lite/kernels/register.h"
 #include "tflite/ext/kernels/register.h"
 ```
 
+To use tensorflow lite interpreter, need tensorflow lite interpreter session header
 ```
-#include "tflite/NNAPISession.h"
+#include "tflite/InterpreterSession.h"
 ```
-For NNAPI usage, or
 
+For NNAPI usage, need NNAPI session header
 ```
-#include "tflite/InterpreterSession.h"
+#include "tflite/NNAPISession.h"
 ```
-for TfLite interpreter.
 
-Load the model into `FlatBuffer`, create a `BuiltinOpResolver` and construct a tensorflow interpreter builder using them:
+Load the model object into `FlatBuffer`, create a tensorflow lite operator resolver `BuiltinOpResolver` and construct a tensorflow interpreter builder using them:
 ```
 tflite::StderrReporter error_reporter;
 auto model = tflite::FlatBufferModel::BuildFromFile(model_file.c_str(), &error_reporter);
@@ -35,19 +39,19 @@ std::unique_ptr<tflite::Interpreter> interpreter;
 builder(&interpreter);
 ```
 
-Create a tensorflow session to use NNAPI:
+Create a tensorflow lite session to use NNAPI:
 ```
 std::shared_ptr<nnfw::tflite::Session> sess = std::make_shared<nnfw::tflite::NNAPISession>(interpreter.get());
 ```
 
-If you want to use TfLite interpreter instead of NNAPI, then:
+If you want to use tensorflow lite interpreter instead of NNAPI, then:
 ```
 std::shared_ptr<nnfw::tflite::Session> sess = std::make_shared<nnfw::tflite::InterpreterSession>(interpreter.get());
 ```
 
 `NNAPISession` constructs a computational graph from the interpreter and builds the model.
 
-## Initialize the model
+## Prepare tensors memory allocation and model input for inference
 
 Allocate the memory for tensors of `tflite::Interpreter`:
 ```
@@ -87,7 +91,7 @@ Run the inference
 sess->run();
 ```
 
-Get the result from `interpreter->outputs()`. This is tensorflow specific, not nnfw, so one can use any method, that is applicable to Tensorflow, e.g.:
+Get the result from `interpreter->outputs()`. This is tensorflow lite specific, not nnfw, so one can use any method, that is applicable to tensorflow lite, e.g.:
 ```
 for (const auto &id : interpreter->outputs())
 {