eval_data1 = tf.placeholder(

      data_type(),

      shape=(1, IMAGE_SIZE, IMAGE_SIZE, 1))

  eval_prediction1 = tf.nn.softmax(model(eval_data1))

        if FLAGS.save_tflite:

            # save tflite

            converter = tf.lite.TFLiteConverter.from_session(sess, [eval_data1], [eval_prediction1])

            tflite_model = converter.convert()

            open("converted_model.tflite", "wb").write(tflite_model)

            print('===========save tflite over =============')

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Simple, end-to-end, LeNet-5-like convolutional MNIST model example.

This should achieve a test error of 0.7%. Please keep this model as simple and

linear as possible, it is meant as a tutorial for simple convolutional models.

Run with --self_test on the command line to execute a short self-test.

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import argparse

import gzip

import os

import sys

import time

import numpy

from six.moves import urllib

from six.moves import xrange  # pylint: disable=redefined-builtin

import tensorflow as tf

# CVDF mirror of http://yann.lecun.com/exdb/mnist/

SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'

WORK_DIRECTORY = 'data'

IMAGE_SIZE = 28

NUM_CHANNELS = 1

PIXEL_DEPTH = 255

NUM_LABELS = 10

VALIDATION_SIZE = 5000  # Size of the validation set.

SEED = 66478  # Set to None for random seed.

BATCH_SIZE = 64

NUM_EPOCHS = 10

EVAL_BATCH_SIZE = 64

EVAL_FREQUENCY = 100  # Number of steps between evaluations.

FLAGS = None

def data_type():

  """Return the type of the activations, weights, and placeholder variables."""

  if FLAGS.use_fp16:

    return tf.float16

  else:

    return tf.float32

def maybe_download(filename):

  """Download the data from Yann's website, unless it's already here."""

  if not tf.gfile.Exists(WORK_DIRECTORY):

    tf.gfile.MakeDirs(WORK_DIRECTORY)

  filepath = os.path.join(WORK_DIRECTORY, filename)

  if not tf.gfile.Exists(filepath):

    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)

    with tf.gfile.GFile(filepath) as f:

      size = f.size()

    print('Successfully downloaded', filename, size, 'bytes.')

  return filepath

def extract_data(filename, num_images):

  """Extract the images into a 4D tensor [image index, y, x, channels].

  Values are rescaled from [0, 255] down to [-0.5, 0.5].

  """

  print('Extracting', filename)

  with gzip.open(filename) as bytestream:

    bytestream.read(16)

    buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)

    data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)

    data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH

    data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)

    return data

def extract_labels(filename, num_images):

  """Extract the labels into a vector of int64 label IDs."""

  print('Extracting', filename)

  with gzip.open(filename) as bytestream:

    bytestream.read(8)

    buf = bytestream.read(1 * num_images)

    labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)

  return labels

def fake_data(num_images):

  """Generate a fake dataset that matches the dimensions of MNIST."""

  data = numpy.ndarray(

      shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),

      dtype=numpy.float32)

  labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)

  for image in xrange(num_images):

    label = image % 2

    data[image, :, :, 0] = label - 0.5

    labels[image] = label

  return data, labels

def error_rate(predictions, labels):

  """Return the error rate based on dense predictions and sparse labels."""

  return 100.0 - (

      100.0 *

      numpy.sum(numpy.argmax(predictions, 1) == labels) /

      predictions.shape[0])

def main(_):

  if FLAGS.self_test:

    print('Running self-test.')

    train_data, train_labels = fake_data(256)

    validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)

    test_data, test_labels = fake_data(EVAL_BATCH_SIZE)

    num_epochs = 1

  else:

    # Get the data.

    train_data_filename = maybe_download('train-images-idx3-ubyte.gz')

    train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')

    test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')

    test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')

    # Extract it into numpy arrays.

    train_data = extract_data(train_data_filename, 60000)

    train_labels = extract_labels(train_labels_filename, 60000)

    test_data = extract_data(test_data_filename, 10000)

    test_labels = extract_labels(test_labels_filename, 10000)

    # Generate a validation set.

    validation_data = train_data[:VALIDATION_SIZE, ...]

    validation_labels = train_labels[:VALIDATION_SIZE]

    train_data = train_data[VALIDATION_SIZE:, ...]

    train_labels = train_labels[VALIDATION_SIZE:]

    num_epochs = NUM_EPOCHS

  train_size = train_labels.shape[0]

  # This is where training samples and labels are fed to the graph.

  # These placeholder nodes will be fed a batch of training data at each

  # training step using the {feed_dict} argument to the Run() call below.

  train_data_node = tf.placeholder(

      data_type(),

      shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

  train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))

  eval_data = tf.placeholder(

      data_type(),

      shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

  eval_data1 = tf.placeholder(

      data_type(),

      shape=(1, IMAGE_SIZE, IMAGE_SIZE, 1))

  # The variables below hold all the trainable weights. They are passed an

  # initial value which will be assigned when we call:

  # {tf.global_variables_initializer().run()}

  conv1_weights = tf.Variable(

      tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.

                          stddev=0.1,

                          seed=SEED, dtype=data_type()))

  conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))

  conv2_weights = tf.Variable(tf.truncated_normal(

      [5, 5, 32, 64], stddev=0.1,

      seed=SEED, dtype=data_type()))

  conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))

  fc1_weights = tf.Variable(  # fully connected, depth 512.

      tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],

                          stddev=0.1,

                          seed=SEED,

                          dtype=data_type()))

  fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))

  fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS],

                                                stddev=0.1,

                                                seed=SEED,

                                                dtype=data_type()))

  fc2_biases = tf.Variable(tf.constant(

      0.1, shape=[NUM_LABELS], dtype=data_type()))

  # We will replicate the model structure for the training subgraph, as well

  # as the evaluation subgraphs, while sharing the trainable parameters.

  def model(data, train=False):

    """The Model definition."""

    # 2D convolution, with 'SAME' padding (i.e. the output feature map has

    # the same size as the input). Note that {strides} is a 4D array whose

    # shape matches the data layout: [image index, y, x, depth].

    conv = tf.nn.conv2d(data,

                        conv1_weights,

                        strides=[1, 1, 1, 1],

                        padding='SAME')

    # Bias and rectified linear non-linearity.

    relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))

    # Max pooling. The kernel size spec {ksize} also follows the layout of

    # the data. Here we have a pooling window of 2, and a stride of 2.

    pool = tf.nn.max_pool(relu,

                          ksize=[1, 2, 2, 1],

                          strides=[1, 2, 2, 1],

                          padding='SAME')

    conv = tf.nn.conv2d(pool,

                        conv2_weights,

                        strides=[1, 1, 1, 1],

                        padding='SAME')

    relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))

    pool = tf.nn.max_pool(relu,

                          ksize=[1, 2, 2, 1],

                          strides=[1, 2, 2, 1],

                          padding='SAME')

    # Reshape the feature map cuboid into a 2D matrix to feed it to the

    # fully connected layers.

    pool_shape = pool.get_shape().as_list()

    reshape = tf.reshape(

        pool,

        [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])

    # Fully connected layer. Note that the '+' operation automatically

    # broadcasts the biases.

    hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)

    # Add a 50% dropout during training only. Dropout also scales

    # activations such that no rescaling is needed at evaluation time.

    if train:

      hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)

    return tf.matmul(hidden, fc2_weights) + fc2_biases

  # Training computation: logits + cross-entropy loss.

  logits = model(train_data_node, True)

  loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

      labels=train_labels_node, logits=logits))

  # L2 regularization for the fully connected parameters.

  regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +

                  tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))

  # Add the regularization term to the loss.

  loss += 5e-4 * regularizers

  # Optimizer: set up a variable that's incremented once per batch and

  # controls the learning rate decay.

  batch = tf.Variable(0, dtype=data_type())

  # Decay once per epoch, using an exponential schedule starting at 0.01.

  learning_rate = tf.train.exponential_decay(

      0.01,                # Base learning rate.

      batch * BATCH_SIZE,  # Current index into the dataset.

      train_size,          # Decay step.

      0.95,                # Decay rate.

      staircase=True)

  # Use simple momentum for the optimization.

  optimizer = tf.train.MomentumOptimizer(learning_rate,

                                         0.9).minimize(loss,

                                                       global_step=batch)

  # Predictions for the current training minibatch.

  train_prediction = tf.nn.softmax(logits)

  # Predictions for the test and validation, which we'll compute less often.

  eval_prediction = tf.nn.softmax(model(eval_data))

  eval_prediction1 = tf.nn.softmax(model(eval_data1))

  # Small utility function to evaluate a dataset by feeding batches of data to

  # {eval_data} and pulling the results from {eval_predictions}.

  # Saves memory and enables this to run on smaller GPUs.

  def eval_in_batches(data, sess):

    """Get all predictions for a dataset by running it in small batches."""

    size = data.shape[0]

    if size < EVAL_BATCH_SIZE:

      raise ValueError("batch size for evals larger than dataset: %d" % size)

    predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)

    for begin in xrange(0, size, EVAL_BATCH_SIZE):

      end = begin + EVAL_BATCH_SIZE

      if end <= size:

        predictions[begin:end, :] = sess.run(

            eval_prediction,

            feed_dict={eval_data: data[begin:end, ...]})

      else:

        batch_predictions = sess.run(

            eval_prediction,

            feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})

        predictions[begin:, :] = batch_predictions[begin - size:, :]

    return predictions

  # Create a local session to run the training.

  start_time = time.time()

  with tf.Session() as sess:

    # Run all the initializers to prepare the trainable parameters.

    tf.global_variables_initializer().run()

    print('Initialized!')

    if FLAGS.use_tflite:

        interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")

        interpreter.allocate_tensors()

        input_details = interpreter.get_input_details()

        output_details = interpreter.get_output_details()

        #eval_data = test_data[0:EVAL_BATCH_SIZE, ...]

        #print(test_data[0, ..., 0])

        print("===========")

        # eval_data = numpy.ndarray(

        #     shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),

        #     dtype=numpy.float32)

        # #eval_data = test_data[0, ...]

        # interpreter.set_tensor(input_details[0]['index'], test_data[0:EVAL_BATCH_SIZE, ...])

        my_data = numpy.ndarray(

                shape=(1, IMAGE_SIZE, IMAGE_SIZE, 1),

                dtype=numpy.float32)

        my_data[0, :, :, 0] = test_data[1, :, :, 0]

        interpreter.set_tensor(input_details[0]['index'], my_data)

        interpreter.invoke()

        eval_prediction = interpreter.get_tensor(output_details[0]['index'])

        print(eval_prediction)

        print(numpy.argmax(eval_prediction, 1))

        print(test_labels[0:EVAL_BATCH_SIZE, ...])

    else:

        # #restore checkpoint

        # saver = tf.train.Saver()

        # ckpt = tf.train.get_checkpoint_state("./mnist-model/")

        # print('===================')

        # print(ckpt)

        # print('===================')

        # '''saver.restore(sess, ckpt.all_model_checkpoint_paths[0])

        # print (ckpt.all_model_checkpoint_paths[0])'''

        #

        # saver.restore(sess, ckpt.model_checkpoint_path)

        # print(ckpt.model_checkpoint_path)

        # print('===================')

        # Loop through training steps.

        for step in xrange(int(num_epochs * train_size) // BATCH_SIZE):

            # Compute the offset of the current minibatch in the data.

            # Note that we could use better randomization across epochs.

            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)

            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]

            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]

            # This dictionary maps the batch data (as a numpy array) to the

            # node in the graph it should be fed to.

            feed_dict = {train_data_node: batch_data,

                   train_labels_node: batch_labels}

            # Run the optimizer to update weights.

            sess.run(optimizer, feed_dict=feed_dict)

            # print some extra information once reach the evaluation frequency

            if step % EVAL_FREQUENCY == 0:

                # #save checkpoint

                # saver.save(sess, "./mnist-model/model.ckpt", global_step=step)

                # fetch some extra nodes' data

                l, lr, predictions = sess.run([loss, learning_rate, train_prediction], feed_dict=feed_dict)

                elapsed_time = time.time() - start_time

                start_time = time.time()

                print('Step %d (epoch %.2f), %.1f ms' %

                    (step, float(step) * BATCH_SIZE / train_size,

                    1000 * elapsed_time / EVAL_FREQUENCY))

                print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))

                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))

                print('Validation error: %.1f%%' % error_rate(

                    eval_in_batches(validation_data, sess), validation_labels))

                sys.stdout.flush()

        # Finally print the result!

        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)

        print('Test error: %.1f%%' % test_error)

        if FLAGS.save_tflite:

            # save tflite, if we want to save with multiple inputs and outputs, the format is like [eval_data, eval_data1], [eval_predictiion, eval_prediction1]. 
            # you must notice that each output must can be worked out by inputs, or there may be segment fault.

            converter = tf.lite.TFLiteConverter.from_session(sess, [eval_data1], [eval_prediction1])

            tflite_model = converter.convert()

            open("converted_model.tflite", "wb").write(tflite_model)

            print('===========save tflite over =============')

        if FLAGS.self_test:

            print('test_error', test_error)

            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (

                test_error,)

if __name__ == '__main__':

  parser = argparse.ArgumentParser()

  parser.add_argument(

      '--use_fp16',

      default=False,

      help='Use half floats instead of full floats if True.',

      action='store_true')

  parser.add_argument(

      '--self_test',

      default=False,

      action='store_true',

      help='True if running a self test.')

  parser.add_argument(

      '--use_tflite',

      default=False,

      action='store_true',

      help='True if running by tflite.')

  parser.add_argument(

      '--save_tflite',

      default=False,

      action='store_true',

      help='True if running by tflite.')

  FLAGS, unparsed = parser.parse_known_args()

  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

apply plugin: 'com.android.application'

android {

    compileSdkVersion 29

    buildToolsVersion "29.0.1"

    defaultConfig {

        applicationId "com.example.testtflite"

        minSdkVersion 28

        targetSdkVersion 29

        versionCode 1

        versionName "1.0"

        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"

    }

    buildTypes {

        release {

            minifyEnabled false

            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'

        }

    }

    android.applicationVariants.all {

        variant ->

            variant.outputs.all {

                outputFileName = "app_test.apk"

            }

    }

}

dependencies {

    implementation fileTree(dir: 'libs', include: ['*.jar'])

    implementation 'androidx.appcompat:appcompat:1.1.0'

    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'

    testImplementation 'junit:junit:4.12'

    androidTestImplementation 'androidx.test:runner:1.2.0'

    androidTestImplementation 'androidx.test.espresso:espresso-core:3.2.0'

    //tflite

    implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly'

}

package com.example.testtflite;

import android.content.res.AssetFileDescriptor;

import android.os.Bundle;

import android.util.Log;

import androidx.appcompat.app.AppCompatActivity;

import org.tensorflow.lite.Interpreter;

import java.io.FileInputStream;

import java.io.IOException;

import java.nio.ByteBuffer;

import java.nio.channels.FileChannel;

public class MainActivity extends AppCompatActivity {

    @Override

    protected void onCreate(Bundle savedInstanceState) {

        super.onCreate(savedInstanceState);

        setContentView(R.layout.activity_main);

        Log.i("YY", "begin");

        try {

            AssetFileDescriptor fileDescriptor = getAssets().openFd("converted_model.mp3");

            FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());

            FileChannel fileChannel = inputStream.getChannel();

            long startOffset = fileDescriptor.getStartOffset();

            long declaredLength = fileDescriptor.getDeclaredLength();

            ByteBuffer tfliteModel = fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);

            Log.i("YY", "get model");

            Interpreter interpreter = new Interpreter(tfliteModel);

            Log.i("YY", "load model");

            float[][][][] input = new float[1][28][28][1];

            for (int i=0; i<28; i++) {

                for (int j=0; j<28; j++) {

                    if ((i == 7 || i == 8) && (j > 2 && j<14)) {

                        input[0][i][j][0] = 0.5f;

                    }else if (i > 7 && (j == 12 || j== 13)) {

                        input[0][i][j][0] = 0.5f;

                    } else {

                        input[0][i][j][0] = -0.5f;

                    }

                }

            }

            float[][] output = new float[1][10];

            interpreter.run(input, output);

            float out, maxOut=0;

            int max_index = -1;

            for (int i=0; i<10; i++) {

                 out = output[0][i];

                 if(maxOut < out) {

                     maxOut = out;

                     max_index = i;

                 }

//                Log.i("YY", Float.toString(out));

            }

            Log.i("YY", "predict number : " + max_index);

            Log.i("YY", "finish");

        } catch (IOException e) {

            e.printStackTrace();

            Log.e("YY", e.getMessage());

        }

    }

}