x = tf.placeholder(tf.float32,[None,784],name='x_input')#输入节点：x_input
.
.
.
pre_num=tf.argmax(y,1,output_type='int32',name="output")#输出节点：output

# 保存训练好的模型
#形参output_node_names用于指定输出的节点名称,output_node_names=['output']对应pre_num=tf.argmax(y,1,name="output"),
output_graph_def = graph_util.convert_variables_to_constants(sess, sess.graph_def,output_node_names=['output'])
with tf.gfile.FastGFile('model/mnist.pb', mode='wb') as f:#'wb'中w代表写文件，b代表将数据以二进制方式写入文件。
 f.write(output_graph_def.SerializeToString())

#coding=utf-8
# 单隐层SoftMax Regression分类器：训练和保存模型模块
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
from tensorflow.python.framework import graph_util
print('tensortflow:{0}'.format(tf.__version__))
 
mnist = input_data.read_data_sets("Mnist_data/", one_hot=True)
 
#create model
with tf.name_scope('input'):
 x = tf.placeholder(tf.float32,[None,784],name='x_input')#输入节点名：x_input
 y_ = tf.placeholder(tf.float32,[None,10],name='y_input')
with tf.name_scope('layer'):
 with tf.name_scope('W'):
 #tf.zeros([3, 4], tf.int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
 W = tf.Variable(tf.zeros([784,10]),name='Weights')
 with tf.name_scope('b'):
 b = tf.Variable(tf.zeros([10]),name='biases')
 with tf.name_scope('W_p_b'):
 Wx_plus_b = tf.add(tf.matmul(x, W), b, name='Wx_plus_b')
 
 y = tf.nn.softmax(Wx_plus_b, name='final_result')
 
# 定义损失函数和优化方法
with tf.name_scope('loss'):
 loss = -tf.reduce_sum(y_ * tf.log(y))
with tf.name_scope('train_step'):
 train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
 print(train_step)
# 初始化
sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
sess.run(init)
# 训练
for step in range(100):
 batch_xs,batch_ys =mnist.train.next_batch(100)
 train_step.run({x:batch_xs,y_:batch_ys})
 # variables = tf.all_variables()
 # print(len(variables))
 # print(sess.run(b))
 
# 测试模型准确率
pre_num=tf.argmax(y,1,output_type='int32',name="output")#输出节点名：output
correct_prediction = tf.equal(pre_num,tf.argmax(y_,1,output_type='int32'))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
a = accuracy.eval({x:mnist.test.images,y_:mnist.test.labels})
print('测试正确率：{0}'.format(a))
 
# 保存训练好的模型
#形参output_node_names用于指定输出的节点名称,output_node_names=['output']对应pre_num=tf.argmax(y,1,name="output"),
output_graph_def = graph_util.convert_variables_to_constants(sess, sess.graph_def,output_node_names=['output'])
with tf.gfile.FastGFile('model/mnist.pb', mode='wb') as f:#'wb'中w代表写文件，b代表将数据以二进制方式写入文件。
 f.write(output_graph_def.SerializeToString())
sess.close()

import tensorflow as tf
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
 
#模型路径
model_path = 'model/mnist.pb'
#测试图片
testImage = Image.open("data/test_image.jpg");
 
with tf.Graph().as_default():
 output_graph_def = tf.GraphDef()
 with open(model_path, "rb") as f:
 output_graph_def.ParseFromString(f.read())
 tf.import_graph_def(output_graph_def, name="")
 
 with tf.Session() as sess:
 tf.global_variables_initializer().run()
 # x_test = x_test.reshape(1, 28 * 28)
 input_x = sess.graph.get_tensor_by_name("input/x_input:0")
 output = sess.graph.get_tensor_by_name("output:0")
 
 #对图片进行测试
 testImage=testImage.convert('L')
 testImage = testImage.resize((28, 28))
 test_input=np.array(testImage)
 test_input = test_input.reshape(1, 28 * 28)
 pre_num = sess.run(output, feed_dict={input_x: test_input})#利用训练好的模型预测结果
 print('模型预测结果为：',pre_num)
 #显示测试的图片
 # testImage = test_x.reshape(28, 28)
 fig = plt.figure(), plt.imshow(testImage,cmap='binary') # 显示图片
 plt.title("prediction result:"+str(pre_num))
 plt.show()

 multiDexEnabled true
 ndk {
 abiFilters "armeabi-v7a"
 }

 sourceSets {
 main {
 jniLibs.srcDirs = ['libs']
 }
 }

 compile files('libs/libandroid_tensorflow_inference_java.jar')

TensorFlowInferenceInterface.fillNodeFloat(); //送入输入数据
TensorFlowInferenceInterface.runInference(); //进行模型的推理
TensorFlowInferenceInterface.readNodeFloat(); //获取输出数据

TensorFlowInferenceInterface.feed()
TensorFlowInferenceInterface.run()
TensorFlowInferenceInterface.fetch()

package com.example.jinquan.pan.mnist_ensorflow_androiddemo;
 
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Color;
import android.graphics.Matrix;
import android.util.Log;
 
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
 
 
public class PredictionTF {
 private static final String TAG = "PredictionTF";
 //设置模型输入/输出节点的数据维度
 private static final int IN_COL = 1;
 private static final int IN_ROW = 28*28;
 private static final int OUT_COL = 1;
 private static final int OUT_ROW = 1;
 //模型中输入变量的名称
 private static final String inputName = "input/x_input";
 //模型中输出变量的名称
 private static final String outputName = "output";
 
 TensorFlowInferenceInterface inferenceInterface;
 static {
 //加载libtensorflow_inference.so库文件
 System.loadLibrary("tensorflow_inference");
 Log.e(TAG,"libtensorflow_inference.so库加载成功");
 }
 
 PredictionTF(AssetManager assetManager, String modePath) {
 //初始化TensorFlowInferenceInterface对象
 inferenceInterface = new TensorFlowInferenceInterface(assetManager,modePath);
 Log.e(TAG,"TensoFlow模型文件加载成功");
 }
 
 /**
 * 利用训练好的TensoFlow模型预测结果
 * @param bitmap 输入被测试的bitmap图
 * @return 返回预测结果，int数组
 */
 public int[] getPredict(Bitmap bitmap) {
 float[] inputdata = bitmapToFloatArray(bitmap,28, 28);//需要将图片缩放带28*28
 //将数据feed给tensorflow的输入节点
 inferenceInterface.feed(inputName, inputdata, IN_COL, IN_ROW);
 //运行tensorflow
 String[] outputNames = new String[] {outputName};
 inferenceInterface.run(outputNames);
 ///获取输出节点的输出信息
 int[] outputs = new int[OUT_COL*OUT_ROW]; //用于存储模型的输出数据
 inferenceInterface.fetch(outputName, outputs);
 return outputs;
 }
 
 /**
 * 将bitmap转为（按行优先）一个float数组，并且每个像素点都归一化到0~1之间。
 * @param bitmap 输入被测试的bitmap图片
 * @param rx 将图片缩放到指定的大小（列）->28
 * @param ry 将图片缩放到指定的大小（行）->28
 * @return 返回归一化后的一维float数组 ->28*28
 */
 public static float[] bitmapToFloatArray(Bitmap bitmap, int rx, int ry){
 int height = bitmap.getHeight();
 int width = bitmap.getWidth();
 // 计算缩放比例
 float scaleWidth = ((float) rx) / width;
 float scaleHeight = ((float) ry) / height;
 Matrix matrix = new Matrix();
 matrix.postScale(scaleWidth, scaleHeight);
 bitmap = Bitmap.createBitmap(bitmap, 0, 0, width, height, matrix, true);
 Log.i(TAG,"bitmap width:"+bitmap.getWidth()+",height:"+bitmap.getHeight());
 Log.i(TAG,"bitmap.getConfig():"+bitmap.getConfig());
 height = bitmap.getHeight();
 width = bitmap.getWidth();
 float[] result = new float[height*width];
 int k = 0;
 //行优先
 for(int j = 0;j < height;j++){
 for (int i = 0;i < width;i++){
 int argb = bitmap.getPixel(i,j);
 int r = Color.red(argb);
 int g = Color.green(argb);
 int b = Color.blue(argb);
 int a = Color.alpha(argb);
 //由于是灰度图，所以r,g,b分量是相等的。
 assert(r==g && g==b);
// Log.i(TAG,i+","+j+" : argb = "+argb+", a="+a+", r="+r+", g="+g+", b="+b);
 result[k++] = r / 255.0f;
 }
 }
 return result;
 }
}

package com.example.jinquan.pan.mnist_ensorflow_androiddemo;
 
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.util.Log;
import android.view.View;
import android.widget.ImageView;
import android.widget.TextView;
 
public class MainActivity extends AppCompatActivity {
 
 // Used to load the 'native-lib' library on application startup.
 static {
 System.loadLibrary("native-lib");//可以去掉
 }
 
 private static final String TAG = "MainActivity";
 private static final String MODEL_FILE = "file:///android_asset/mnist.pb"; //模型存放路径
 TextView txt;
 TextView tv;
 ImageView imageView;
 Bitmap bitmap;
 PredictionTF preTF;
 @Override
 protected void onCreate(Bundle savedInstanceState) {
 super.onCreate(savedInstanceState);
 setContentView(R.layout.activity_main);
 
 // Example of a call to a native method
 tv = (TextView) findViewById(R.id.sample_text);
 txt=(TextView)findViewById(R.id.txt_id);
 imageView =(ImageView)findViewById(R.id.imageView1);
 bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.test_image);
 imageView.setImageBitmap(bitmap);
 preTF =new PredictionTF(getAssets(),MODEL_FILE);//输入模型存放路径，并加载TensoFlow模型
 }
 
 public void click01(View v){
 String res="预测结果为：";
 int[] result= preTF.getPredict(bitmap);
 for (int i=0;i<result.length;i++){
 Log.i(TAG, res+result[i] );
 res=res+String.valueOf(result[i])+" ";
 }
 txt.setText(res);
 tv.setText(stringFromJNI());
 }
 /**
 * A native method that is implemented by the 'native-lib' native library,
 * which is packaged with this application.
 */
 public native String stringFromJNI();//可以去掉
}
   activity_main布局文件：

<"1.0" encoding="utf-8"?>
<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
 android:layout_width="match_parent"
 android:layout_height="match_parent"
 android:orientation="vertical"
 android:paddingBottom="16dp"
 android:paddingLeft="16dp"
 android:paddingRight="16dp"
 android:paddingTop="16dp">
 <TextView
 android:id="@+id/sample_text"
 android:layout_width="wrap_content"
 android:layout_height="wrap_content"
 android:text="https://blog.csdn.net/guyuealian"
 android:layout_gravity="center"/>
 <Button
 android:onClick="click01"
 android:layout_width="match_parent"
 android:layout_height="wrap_content"
 android:text="click" />
 <TextView
 android:id="@+id/txt_id"
 android:layout_width="match_parent"
 android:layout_height="wrap_content"
 android:gravity="center"
 android:text="结果为："/>
 <ImageView
 android:id="@+id/imageView1"
 android:layout_width="wrap_content"
 android:layout_height="wrap_content"
 android:layout_gravity="center"/>
</LinearLayout>