This is a demo app for Android with Tensorflow to detect handwritten digits.
This Android demo is based on Tensorflow tutorial.
MNIST For ML Beginners https://www.tensorflow.org/versions/r0.10/tutorials/mnist/beginners/index.html
Deep MNIST for Experts https://www.tensorflow.org/versions/r0.10/tutorials/mnist/pros/index.html
Training scripts for neural network model are located at
https://github.com/miyosuda/TensorFlowAndroidMNIST/tree/master/trainer-script
To create model by yourself, install Tensorflow and run python scripts like
$ python beginner.py
or
$ python expert.py
and locate exported .pb file to assets dir.
To export training model, I added some modification to original tutorial scripts.
Now Tensorflow cannot export network graph and trained network weight Variable at the same time, so we need to create another graph to export and convert Variable into constants.
After training is finished, converted trained Variable to numpy ndarray.
_W = W.eval(sess)
_b = b.eval(sess)
and then convert them into constant and re-create graph for exporting.
W_2 = tf.constant(_W, name="constant_W")
b_2 = tf.constant(_b, name="constant_b")
And then use tf.train.write_graph to export graph with trained weights.
Native .so files are already built in this project, but if you would like to build it by yourself, please install and setup NDK.
First download, extract and place Android NDK.
http://developer.android.com/intl/ja/ndk/downloads/index.html
And then update your PATH environment variable. For example,
export NDK_HOME="/Users/[your-username]/Development/android/android-ndk-r11b"
export PATH=$PATH:$NDK_HOME
And build .so file in jni-build dir.
$ cd jni-build
$ make
and copy .so file into app/src/main/jniLibs/armeabi-v7a/ with
$ make install
(Unlike original Android demo in Tensorflow, you don't need to install bazel to build this demo.
Tensorflow library files (.a files) and header files are extracted from original Tensorflow Android demo r0.10.