前言

當寫下這篇文章的時候，其實我連TensorFlow怎么用都不會，所以這篇文章你們就當我放屁好了。我是一個Android開發者，且我不會python（別鄙視我），所以取名為《當Android開發者遇見TensorFlow》。文章并沒有什么實質性內容，僅僅是為了敲開機器學習的大門。

Java調用TensorFlow

前面說了，本寶寶是一只不會python的寶寶，所以這篇文章不會涉及到任何python相關的內容，所以Java自然而然地成為了我的首選語言。

Google開源的TensorFlow的核心代碼是C++寫的，因此Java自然而然的可以使用他，只是中間多了一層JNI。加上平時我對Gradle的接觸程度，選擇Gradle做構建工具，而不是maven。

這里不得不再贊一下Intellij Idea，今天突然發現2017.1版本的Intellij Idea已經能夠自動將maven依賴轉換為gradle依賴了，我們直接復制maven依賴到gradle中，它就會自動轉換為gradle依賴，再也不用我們手動轉換。見證奇跡的時候到了

maven 依賴

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<dependency> <groupId>org.tensorflow</groupId> <artifactId>tensorflow</artifactId> <version>1.1.0</version></dependency>

轉換后的gradle依賴為

123	dependencies { compile 'org.tensorflow:tensorflow:1.1.0'}

為了運行java程序，應用application插件，并指定mainClassName，對應的類在后文創建

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apply plugin: 'application'apply plugin: 'idea'mainClassName = "com.lizhangqu.application.Main"sourceCompatibility = 1.8dependencies { compile 'org.tensorflow:tensorflow:1.1.0'}

來點有難度的，參考LabelImage.java，我們來做一個圖片識別工具

首先下載訓練好的模型?inception5h.zip，將模型內容解壓到src/main/resources/model目錄，如圖

然后隨便下載一張圖作為待識別的圖，這里使用這張圖，好大一座山

將其放到src/main/resources/pic目錄，如圖

然后新建一個Main類，拷貝一波LabelImage.java代碼，修改其main函數為

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public static void main(String[] args) { //模型下載 //https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip String modelPath = Main.class.getClassLoader().getResource("model").getPath(); String picPath = Main.class.getClassLoader().getResource("pic").getPath(); byte[] graphDef = readAllBytesOrExit(Paths.get(modelPath, "tensorflow_inception_graph.pb"));List<String> labels =readAllLinesOrExit(Paths.get(modelPath, "imagenet_comp_graph_label_strings.txt")); byte[] imageBytes = readAllBytesOrExit(Paths.get(picPath, "moutain.jpg")); try (Tensor image = constructAndExecuteGraphToNormalizeImage(imageBytes)) { float[] labelProbabilities = executeInceptionGraph(graphDef, image); int bestLabelIdx = maxIndex(labelProbabilities);System.out.println( String.format( "BEST MATCH: %s (%.2f%% likely)",labels.get(bestLabelIdx), labelProbabilities[bestLabelIdx] * 100f));}}

做的修改很簡單，將參數從外部傳入，修改為了從resources目錄讀取

Main類完整代碼如下

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package com.lizhangqu.application;import java.io.IOException;import java.nio.charset.Charset;import java.nio.file.Files;import java.nio.file.Path;import java.nio.file.Paths;import java.util.Arrays;import java.util.List;import org.tensorflow.DataType;import org.tensorflow.Graph;import org.tensorflow.Output;import org.tensorflow.Session;import org.tensorflow.Tensor;public class Main { public static void main(String[] args) { //模型下載 //https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip String modelPath = Main.class.getClassLoader().getResource("model").getPath(); String picPath = Main.class.getClassLoader().getResource("pic").getPath(); byte[] graphDef = readAllBytesOrExit(Paths.get(modelPath, "tensorflow_inception_graph.pb"));List<String> labels =readAllLinesOrExit(Paths.get(modelPath, "imagenet_comp_graph_label_strings.txt")); byte[] imageBytes = readAllBytesOrExit(Paths.get(picPath, "moutain.jpg")); try (Tensor image = constructAndExecuteGraphToNormalizeImage(imageBytes)) { float[] labelProbabilities = executeInceptionGraph(graphDef, image); int bestLabelIdx = maxIndex(labelProbabilities);System.out.println( String.format( "BEST MATCH: %s (%.2f%% likely)",labels.get(bestLabelIdx), labelProbabilities[bestLabelIdx] * 100f));}} private static Tensor constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) { try (Graph g = new Graph()) {GraphBuilder b = new GraphBuilder(g); // Some constants specific to the pre-trained model at: // https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip // // - The model was trained with images scaled to 224x224 pixels. // - The colors, represented as R, G, B in 1-byte each were converted to // float using (value - Mean)/Scale. final int H = 224; final int W = 224; final float mean = 117f; final float scale = 1f; // Since the graph is being constructed once per execution here, we can use a constant for the // input image. If the graph were to be re-used for multiple input images, a placeholder would // have been more appropriate. final Output input = b.constant("input", imageBytes); final Output output =b.div(b.sub(b.resizeBilinear(b.expandDims(b.cast(b.decodeJpeg(input, 3), DataType.FLOAT),b.constant("make_batch", 0)),b.constant("size", new int[]{H, W})),b.constant("mean", mean)),b.constant("scale", scale)); try (Session s = new Session(g)) { return s.runner().fetch(output.op().name()).run().get(0);}}} private static float[] executeInceptionGraph(byte[] graphDef, Tensor image) { try (Graph g = new Graph()) {g.importGraphDef(graphDef); try (Session s = new Session(g);Tensor result = s.runner().feed("input", image).fetch("output").run().get(0)) { final long[] rshape = result.shape(); if (result.numDimensions() != 2 || rshape[0] != 1) { throw new RuntimeException( String.format( "Expected model to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape %s",Arrays.toString(rshape)));} int nlabels = (int) rshape[1]; return result.copyTo(new float[1][nlabels])[0];}}} private static int maxIndex(float[] probabilities) { int best = 0; for (int i = 1; i < probabilities.length; ++i) { if (probabilities[i] > probabilities[best]) {best = i;}} return best;} private static byte[] readAllBytesOrExit(Path path) { try { return Files.readAllBytes(path);} catch (IOException e) {System.err.println("Failed to read [" + path + "]: " + e.getMessage());System.exit(1);} return null;} private static List<String> readAllLinesOrExit(Path path) { try { return Files.readAllLines(path, Charset.forName("UTF-8"));} catch (IOException e) {System.err.println("Failed to read [" + path + "]: " + e.getMessage());System.exit(0);} return null;} // In the fullness of time, equivalents of the methods of this class should be auto-generated from // the OpDefs linked into libtensorflow_jni.so. That would match what is done in other languages // like Python, C++ and Go. static class GraphBuilder {GraphBuilder(Graph g) { this.g = g;}Output div(Output x, Output y) { return binaryOp("Div", x, y);}Output sub(Output x, Output y) { return binaryOp("Sub", x, y);}Output resizeBilinear(Output images, Output size) { return binaryOp("ResizeBilinear", images, size);}Output expandDims(Output input, Output dim) { return binaryOp("ExpandDims", input, dim);}Output cast(Output value, DataType dtype) { return g.opBuilder("Cast", "Cast").addInput(value).setAttr("DstT", dtype).build().output(0);}Output decodeJpeg(Output contents, long channels) { return g.opBuilder("DecodeJpeg", "DecodeJpeg").addInput(contents).setAttr("channels", channels).build().output(0);}Output constant(String name, Object value) { try (Tensor t = Tensor.create(value)) { return g.opBuilder("Const", name).setAttr("dtype", t.dataType()).setAttr("value", t).build().output(0);}} private Output binaryOp(String type, Output in1, Output in2) { return g.opBuilder(type, type).addInput(in1).addInput(in2).build().output(0);} private Graph g;}}

跑一波，命令行執行

1	./gradlew run

看下輸出內容

輸出為

1	BEST MATCH: alp (58.23% likely)

我擦，alp是什么鬼，查下英文字典

12	alp 英 [?lp] 美 [?lp] 　 　n. 高山

恩，沒錯，58.23%的概率這張圖是大山。沒錯，這張圖就是大山。當然識別的圖的準確率跟這個訓練好的模型直接相關，模型越屌，準確率就越高。具體代碼什么意思你也別問我，問我我也不知道，文章開頭已經說過了，寫下這篇文章的時候，我還不會用TensorFlow。

Android調用TensorFlow

Java能調用，Android自然在一定程度上也能調用。

引入依賴

1	compile 'org.tensorflow:tensorflow-android:1.2.0-rc0'

將minSdkVersion設成19，因為用到了高Api，當然如果你想設成14，自行將高Api的代碼刪了，主要是android.os.Trace類，去除了不影響正常使用

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android { defaultConfig { minSdkVersion 19}}

還是一樣的訓練模型，這次把他們扔到assets/model下，待識別的圖片放在assets/pic下，如圖

不過這次我們待識別的圖換了，換成了一個大蘋果

還是拷貝點代碼，到android/demo下，拷貝Classifier.java和TensorFlowImageClassifier.java兩個類，代碼就不貼了。

然后參考下ClassifierActivity.java的代碼，將assets/pic/apple.jpg進行識別

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public class MainActivity extends AppCompatActivity { private TextView result; private Button btn; private Classifier classifier; private static final int INPUT_SIZE = 224; private static final int IMAGE_MEAN = 117; private static final float IMAGE_STD = 1; private static final String INPUT_NAME = "input"; private static final String OUTPUT_NAME = "output"; private static final String MODEL_FILE = "file:///android_asset/model/tensorflow_inception_graph.pb"; private static final String LABEL_FILE = "file:///android_asset/model/imagenet_comp_graph_label_strings.txt";@Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState);setContentView(R.layout.activity_main);btn = (Button) findViewById(R.id.btn);result = (TextView) findViewById(R.id.result);classifier = TensorFlowImageClassifier.create(getAssets(),MODEL_FILE,LABEL_FILE,INPUT_SIZE,IMAGE_MEAN,IMAGE_STD,INPUT_NAME,OUTPUT_NAME);btn.setOnClickListener(new View.OnClickListener() {@Override public void onClick(View v) { new Thread(new Runnable() {@Override public void run() { try {Bitmap croppedBitmap = getBitmap(getApplicationContext(), "pic/apple.jpg", INPUT_SIZE); final List<Classifier.Recognition> results = classifier.recognizeImage(croppedBitmap); new Handler(Looper.getMainLooper()).post(new Runnable() {@Override public void run() {result.setText("results:" + results);}});} catch (IOException e) {e.printStackTrace();}}}).start();}});} private static Bitmap getBitmap(Context context, String path, int size) throws IOException {Bitmap bitmap = null;InputStream inputStream = null;inputStream = context.getAssets().open(path);bitmap = BitmapFactory.decodeStream(inputStream);inputStream.close(); int width = bitmap.getWidth(); int height = bitmap.getHeight(); float scaleWidth = ((float) size) / width; float scaleHeight = ((float) size) / height;Matrix matrix = new Matrix();matrix.postScale(scaleWidth, scaleHeight); return Bitmap.createBitmap(bitmap, 0, 0, width, height, matrix, true);}}

識別結果如下:

識別率最高的是granny smith，什么意思呢，查一下發現是“青蘋果”，哭笑不得，這是一個紅蘋果，這么小的訓練模型，也不期待識別率有多高了。

NDK交叉編譯TensorFlow

上面我們用了org.tensorflow:tensorflow-android:1.2.0-rc0這個庫，還是得掌握下它的由來，下面我們就編譯他。

tensorflow使用bazel構建，且依賴一些python庫，因此先安裝它們

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$ brew install bazel$ brew install swig$ brew install python$ sudo easy_install pip$ sudo pip install six numpy wheel

如果后面報各種各樣的環境缺失，請自行Google并補齊環境。

clone TensorFlow 代碼

1	git clone --recurse-submodules https://github.com/tensorflow/tensorflow.git

修改TensorFlow項目根下的WROKSPACE文件

將以下代碼反注釋

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# Uncomment and update the paths in these entries to build the Android demo.android_sdk_repository(name = "androidsdk",api_level = 23,# Ensure that you have the build_tools_version below installed in the# SDK manager as it updates periodically.build_tools_version = "25.0.2",# Replace with path to Android SDK on your systempath = "/Users/lizhangqu/AndroidSDK",)## Android NDK r12b is recommended (higher may cause issues with Bazel)android_ndk_repository( name="androidndk", path="/Users/lizhangqu/AndroidNDK/android-ndk-r12b",# This needs to be 14 or higher to compile TensorFlow.# Note that the NDK version is not the API level. api_level=14)

然后修改android_sdk_repository中的path為自己電腦中的android sdk目錄，修改android_ndk_repository中的path為自己電腦的android ndk目錄。

值得注意的是，ndk的版本，官方建議使用r12b版本，事實證明，我用android sdk下的ndk-bundle是編譯不過去的，所以還是老老實實用r12b，下載地址android-ndk-r12b-darwin-x86_64.zip

編譯C++部分代碼

1234	bazel build -c opt //tensorflow/contrib/android:libtensorflow_inference.so \--crosstool_top=//external:android/crosstool \ --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ --cpu=armeabi-v7a

如果你需要構建其他cpu結構的so，請自行修改armeabi-v7a為對應的值，比如修改為x86_64

構建好的so位于 bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so，如圖所示

將libtensorflow_inference.so拷貝出來備份起來，因為下一步構建java代碼時，此文件會被刪除。

編譯java部分代碼

1	bazel build //tensorflow/contrib/android:android_tensorflow_inference_java

編譯好的jar位于 bazel-bin/tensorflow/contrib/android/libandroid_tensorflow_inference_java.jar，如圖所示

然后將libandroid_tensorflow_inference_java.jar和libtensorflow_inference.so結合起來，發布到maven，就是我們依賴的org.tensorflow:tensorflow-android:1.2.0-rc0了。

編譯PC上的Java版TensorFlow

不多說，和NDK交叉編譯差不多，編譯腳本

1234	./configurebazel build --config opt \//tensorflow/java:tensorflow \//tensorflow/java:libtensorflow_jni

編譯產物位于bazel-bin/tensorflow/java，該目錄下有

• libtensorflow.jar文件
• libtensorflow_jni.so(linux)或libtensorflow_jni.dylib(mac)或tensorflow_jni.dll(windows，注：mac無法編譯出dll)文件，

如圖所示

編譯時依賴，請添加libtensorflow.jar

1	javac -cp bazel-bin/tensorflow/java/libtensorflow.jar ...

運行期依賴，請添加libtensorflow.jar和libtensorflow_jni的路徑

123	java -cp bazel-bin/tensorflow/java/libtensorflow.jar \-Djava.library.path=bazel-bin/tensorflow/java \...

總結

當然一般情況下，我們沒有必要自己去編譯TensorFlow，只需要使用編譯好的現成庫即可。

寫了這么多，可是寶寶還是不會TensorFlow~

http://fucknmb.com/2017/06/02/%E5%BD%93Android%E5%BC%80%E5%8F%91%E8%80%85%E9%81%87%E8%A7%81TensorFlow/

總結

以上是生活随笔為你收集整理的当 Android 开发者遇见 TensorFlow的全部內容，希望文章能夠幫你解決所遇到的問題。

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