Android TensorFlow支持和TensorFlow Lite for Android是两种用于在Android设备上部署和运行TensorFlow模型的不同方式。

Android TensorFlow支持是TensorFlow官方提供的一个库，它允许开发者在Android设备上使用TensorFlow模型进行预测。它使用TensorFlow原生的Java API，并且可以直接加载和运行TensorFlow SavedModel，FrozenModel和GraphDef模型。

以下是一个使用Android TensorFlow支持进行图像分类的示例代码：

import android.content.res.AssetFileDescriptor;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.os.Bundle;
import android.support.v7.app.AppCompatActivity;
import android.util.Log;
import android.widget.ImageView;
import android.widget.TextView;

import org.tensorflow.contrib.android.TensorFlowInferenceInterface;

import java.io.IOException;
import java.io.InputStream;

public class MainActivity extends AppCompatActivity {

    private static final String MODEL_FILE = "file:///android_asset/model.pb";
    private static final String INPUT_NODE = "input";
    private static final String OUTPUT_NODE = "output";
    private static final int INPUT_SIZE = 224;
    private static final int NUM_CLASSES = 1000;

    private TensorFlowInferenceInterface inferenceInterface;
    private Bitmap inputBitmap;
    private ImageView imageView;
    private TextView textView;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        imageView = findViewById(R.id.imageView);
        textView = findViewById(R.id.textView);

        inferenceInterface = new TensorFlowInferenceInterface(getAssets(), MODEL_FILE);

        try {
            inputBitmap = getBitmapFromAsset("input.jpg");
            imageView.setImageBitmap(inputBitmap);
            float[] result = classifyImage(inputBitmap);
            String label = getLabel(result);
            textView.setText("Class: " + label);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    private float[] classifyImage(Bitmap bitmap) {
        float[] inputFloats = preprocessImage(bitmap);

        inferenceInterface.feed(INPUT_NODE, inputFloats, 1, INPUT_SIZE, INPUT_SIZE, 3);
        inferenceInterface.run(new String[]{OUTPUT_NODE});
        float[] outputFloats = new float[NUM_CLASSES];
        inferenceInterface.fetch(OUTPUT_NODE, outputFloats);

        return outputFloats;
    }

    private float[] preprocessImage(Bitmap bitmap) {
        Bitmap resizedBitmap = Bitmap.createScaledBitmap(bitmap, INPUT_SIZE, INPUT_SIZE, false);
        int[] intValues = new int[INPUT_SIZE * INPUT_SIZE];
        float[] floatValues = new float[INPUT_SIZE * INPUT_SIZE * 3];
        resizedBitmap.getPixels(intValues, 0, resizedBitmap.getWidth(), 0, 0, resizedBitmap.getWidth(), resizedBitmap.getHeight());

        for (int i = 0; i < intValues.length; ++i) {
            final int val = intValues[i];
            floatValues[i * 3 + 0] = ((val >> 16) & 0xFF) / 255.0f;
            floatValues[i * 3 + 1] = ((val >> 8) & 0xFF) / 255.0f;
            floatValues[i * 3 + 2] = (val & 0xFF) / 255.0f;
        }

        return floatValues;
    }

    private String getLabel(float[] result) {
        // Load labels from file
        String labelFile = "file:///android_asset/labels.txt";
        String actualFilename = labelFile.split("file:///android_asset/")[1];
        AssetManager assetManager = getAssets();
        InputStream labelsInput;
        String[] labels = new String[NUM_CLASSES];
        try {
            labelsInput = assetManager.open(actualFilename);
            int bytesRead = labelsInput.read();
            StringBuilder sb = new StringBuilder();
            int i = 0;
            while (bytesRead != -1) {
                if ((char) bytesRead == '\n') {
                    labels[i] = sb.toString();
                    sb = new StringBuilder();
                    i++;
                } else {
                    sb.append((char) bytesRead);
                }
                bytesRead = labelsInput.read();
            }
            labelsInput.close();
        } catch (IOException e) {
            e.printStackTrace();
        }

        int maxIndex = 0;
        float maxValue = result[0];
        for (int i = 1; i < result.length; i++) {
            if (result[i] > maxValue) {
                maxIndex = i;
                maxValue = result[i];
            }
        }

        return labels[maxIndex];
    }

    private Bitmap getBitmapFromAsset(String fileName) throws IOException {
        AssetManager assetManager = getAssets();
        InputStream inputStream = null;
        try