Jacky Tung Wonderland

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    type loggly
    loggly_url https://logs-01.loggly.com/inputs/TOKEN/tag/fluentd
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<match training_logs_match>
    type loggly
    loggly_url https://logs-01.loggly.com/inputs/TOKEN/tag/TrainingLogs-${Task}
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<match sys_logs_match>
    type loggly
    loggly_url https://logs-01.loggly.com/inputs/TOKEN/tag/SysLogs-${Task}
</match>

Requirement

Create Graph

import tensorflow as tf
import time
from tensorflow.examples.tutorials.mnist import input_data
# === prepare tensorflow network === #

#config setting
imageDim = 784
outputDim = 10

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# None means that a dimension can be any of any length
x = tf.placeholder(tf.float32, [None, imageDim], name="input")
# 784-dimensional image vectors by it to produce 10-dimensional vectors
W = tf.Variable(tf.zeros([imageDim, outputDim]), dtype=tf.float32, name="Weight")
# a shape of [10]
b = tf.Variable(tf.zeros([outputDim]), dtype=tf.float32, name="bias")
# softmax
y = tf.nn.softmax(tf.matmul(x, W)+b, name="softmax")

....

# Add ops to save and restore all the variables
saver = tf.train.Saver()

with tf.Session() as sess:
 sess.run(tf.initialize_all_variables())
#Training
    for i in range(1000):
        if i % 100 == 0:
            print "iteration num :", i
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step,feed_dict={x: batch_xs, y_: batch_ys})

    # Save checkpoint, graph.pb and tensorboard
    saver.save(sess, "models/model.ckpt") 
    tf.train.write_graph(sess.graph.as_graph_def(), "models/", "graph.pb")
    tf.train.SummaryWriter("board", sess.graph)
#Testing
.....    

Creating a simple library

cout << "preparing input data..." << endl;
  // config setting
  int imageDim = 784;
  int nTests = 10000;
  
  // Setup inputs and outputs:
  Tensor x(DT_FLOAT, TensorShape({nTests, imageDim}));

  MNIST mnist = MNIST("./MNIST_data/");
  auto dst = x.flat<float>().data();
  for (int i = 0; i < nTests; i++) {
    auto img = mnist.testData.at(i).pixelData;
    std::copy_n(img.begin(), imageDim, dst);
    dst += imageDim;
  }

  cout << "data is ready" << endl;
  vector<pair<string, Tensor>> inputs = {
    { "input", x}
  };

  // The session will initialize the outputs
  vector<Tensor> outputs;
  // Run the session, evaluating our "softmax" operation from the graph
  status = session->Run(inputs, {"softmax"}, {}, &outputs);
  if (!status.ok()) {
    cout << status.ToString() << "\n";
    return 1;
  }else{
   cout << "Success load graph !! " << "\n";
  }

// start compute the accuracy,
  // arg_max is to record which index is the largest value after 
  // computing softmax, and if arg_max is equal to testData.label,
  // means predict correct.
  int nHits = 0;
  for (vector<Tensor>::iterator it = outputs.begin() ; it != outputs.end(); ++it) {
   auto items = it->shaped<float, 2>({nTests, 10}); // 10 represent number of class
 for(int i = 0 ; i < nTests ; i++){
      int arg_max = 0;
            float val_max = items(i, 0);
            for (int j = 0; j < 10; j++) {
         if (items(i, j) > val_max) {
               arg_max = j;
               val_max = items(i, j);
                }
      }
      if (arg_max == mnist.testData.at(i).label) {
          nHits++;
            } 
 }
  }
  float accuracy = (float)nHits/nTests;
  cout << "accuracy is : " << accuracy << ", and Done!!" << 

cc_binary(
    name = "mnistpredict",
    srcs = ["mnist.cc", "MNIST.h"],
    deps = [
        "//tensorflow/core:tensorflow",
    ],
);

Compile and Run

Note and Following tutorial