contrib/android: Use the Java API to implement TensorFlowInferenceInterface

This removes the native code for tensorflow_inference_jni and makes it a pure Java class. Use of the Java API allows some "configuration" values (like the number of labels in the inception example) to be determined using shape inference instead of being passed in explicitly. Change: 147398845

Author: asimshankar

tensorflow/contrib/android/README.md

@@ -1,28 +1,21 @@
 # Android TensorFlow support
 
-This directory contains components geared towards supporting TensorFlow on
-Android.
+This directory defines components (a native `.so` library and a Java JAR)
+geared towards supporting TensorFlow on Android. This includes:
 
-## Android Java Inference Interface for TensorFlow
+- The [TensorFlow Java API](../../java/README.md)
+- A `TensorFlowInferenceInterface` class that provides a smaller API
+ surface suitable for inference and summarizing performance of model execution.
 
-This library contains a Java API capable of loading TensorFlow graphs and
-performing inference on Android. See [TensorFlowImageClassifier.java](../../examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java)
-in the [TensorFlow Android Camera Demo](../../examples/android) for an example.
-
-It also contains a Java class (`RunStats`) for summarizing performance
-of operations during graph execution.
-
-With both the native .so library and the Java JAR (or AAR which combines both),
-you will have everything you need to use TensorFlow for inference in your
-Android app. Note that training is not supported through this interface; for
-that you will have to use one of the other APIs.
+For example usage, see [TensorFlowImageClassifier.java](../../examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java)
+in the [TensorFlow Android Demo](../../examples/android).
 
 For prebuilt libraries, see the
 [nightly Android build artifacts](https://ci.tensorflow.org/view/Nightly/job/nightly-android/)
 page for a recent build.
 
-To build the inference libraries yourself (if, for example, you want to support
-custom TensorFlow operators), pick your preferred approach below:
+To build the libraries yourself (if, for example, you want to support custom
+TensorFlow operators), pick your preferred approach below:
 
 ### Bazel
 
@@ -64,8 +57,9 @@ For documentation on building a self-contained AAR file with cmake, see
 [tensorflow/contrib/android/cmake](cmake).
 
 
-## AssetManagerFileSystem:
+## AssetManagerFileSystem
 
-A TensorFlow filesystem supporting the Android asset manager. This may be
-useful when writing native (C/C++) code that is tightly coupled with TensorFlow
-(for typical usage the Inference Interface library above will be sufficient).
+This directory also contains a TensorFlow filesystem supporting the Android
+asset manager. This may be useful when writing native (C++) code that is tightly
+coupled with TensorFlow. For typical usage, the library above will be
+sufficient.