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tfmobile-mnist-android

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MNIST with TensorFlow Mobile on Android

tfmobile-mnist-android 최신버젼 다운로드

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# MNIST with TensorFlow Mobile on Android

This project demonstrates how to use [TensorFlow Mobile](https://www.tensorflow.org/mobile/mobile_intro) on Android for handwritten digits classification from MNIST.

Prebuilt APK can be downloaded from [here](https://github.com/nex3z/tfmobile-mnist-android/releases/download/v1.0.0/tfmobile-mnist.apk).

If you are interested in a [TensorFlow Lite](https://www.tensorflow.org/mobile/tflite/) version, please refer to [tflite-mnist-android](https://github.com/nex3z/tflite-mnist-android).

## How to build from scratch

### Requirement

- Python 3.6, TensorFlow 1.8.0
- Android Studio 3.0, Gradle 4.1

### Step 1. Training

The model is defined in [mnist.py](https://github.com/nex3z/tfmobile-mnist-android/blob/master/model/mnist.py), run the following command to train the model.

```
python train.py --model_dir ./saved_model --iterations 10000
```

[train.py](https://github.com/nex3z/tfmobile-mnist-android/blob/master/train.py) uses a simple convontional neural network. [train_bn.py](https://github.com/nex3z/tfmobile-mnist-android/blob/master/train_bn.py) provides a bigger network with batch normalization, which hopefully would achieve 99.5% accuracy on validation set within 10000 iterations as shown below.

After training, a collection of checkpoint files and a frozen GraphDef file `mnist.pb` will be generated in `./saved_model`.

You can test the model on test set using the command below.

```
python test.py --model_dir ./saved_model
```

A pre-trained model can be downloaded from [here](https://github.com/nex3z/tfmobile-mnist-android/releases/download/v1.0.0/mnist-10000.zip).

### Step 2. Model optimization

TensorFlow provides [optimize_for_inference.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py) to optimize the model by removing parts of a graph that are only needed for training.

Navigate to the TensorFlow repository directory, run the following command to optimize the model.

```
python tensorflow/python/tools/optimize_for_inference.py \
    --input=model_path/mnist.pb \
    --output=output_path/mnist_optimized.pb \
    --input_names=x \
    --output_names=output   
```

The `input` argument should point to the TensorFlow GraphDef file (`mnist.pb`) trained in Step 1. The `output` argument specifies the location for the optimized model.

Notice that the `mnist.pb` generated by [train.py](https://github.com/nex3z/tfmobile-mnist-android/blob/master/train.py) is already frozen, otherwise we will have to freeze the graph first by using [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py) before optimization.

A optimized model file can be downloaded from [here](https://github.com/nex3z/tfmobile-mnist-android/releases/download/v1.0.0/mnist_optimized.pb).

### Step 3. Build Android app

Copy the `mnist_optimized.pb` generated in Step 2 to `/android/app/src/main/assets`, then build and run the app.

The [Classifer](https://github.com/nex3z/tfmobile-mnist-android/blob/master/android/app/src/main/java/com/nex3z/tfmobilemnist/Classifier.java) creates a [TensorFlowInferenceInterface](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java) from  `mnist_optimized.pb`. The TensorFlowInferenceInterface provides an interface for inference and performance summarization, which is included in the following library.

```
implementation "org.tensorflow:tensorflow-android:1.8.0"
```

## Credits

- The basic model architecture comes from [tensorflow-mnist-tutorial](https://github.com/martin-gorner/tensorflow-mnist-tutorial).
- The official TensorFlow Mobile [Android demo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android).
- The [FingerPaint](https://android.googlesource.com/platform/development/+/master/samples/ApiDemos/src/com/example/android/apis/graphics/FingerPaint.java) from Android API demo.

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