title: My tensorboard events appear many charts I did not summary
body: <p>I only summary my loss as 'xentropy_mean' in training() ,but in tensorboard ,I had not find the 'xentropy_mean' chart but many other charts I did not defined. I don't know where I wrote wrong, and what's the matter indeed. Is it because I use thread in my code? If I don't use thread, how should I wrote it?</p>
  
  <p><a href="https://i.stack.imgur.com/ELM2s.png" rel="nofollow noreferrer">The tensorboard screenshot</a> 
  There are 6 charts under the queue,I don't know what are the meanings either</p>
  
  <p>I create the model in the file below</p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import math
  
  import tensorflow.python.platform
  import tensorflow as tf
  
  # The MNIST dataset has 10 classes, representing the digits 0 through 9.
  NUM_CLASSES = 16
  
  # The MNIST images are always 28x28 pixels.
  IMAGE_SIZE = 28
  IMAGE_PIXELS = 784
  
  
  def inference(images, hidden1_units, hidden2_units):
    """Build the MNIST model up to where it may be used for inference.
  
    Args:
      images: Images placeholder, from inputs().
      hidden1_units: Size of the first hidden layer.
      hidden2_units: Size of the second hidden layer.
  
    Returns:
      softmax_linear: Output tensor with the computed logits.
    """
    # Hidden 1
    with tf.name_scope('hidden1'):
      weights = tf.Variable(
          tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                              stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
          name='weights')
      biases = tf.Variable(tf.zeros([hidden1_units]),
                           name='biases')
      hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
    # Hidden 2
    with tf.name_scope('hidden2'):
      weights = tf.Variable(
          tf.truncated_normal([hidden1_units, hidden2_units],
                              stddev=1.0 / math.sqrt(float(hidden1_units))),
          name='weights')
      biases = tf.Variable(tf.zeros([hidden2_units]),
                           name='biases')
      hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
    # Linear
    with tf.name_scope('softmax_linear'):
      weights = tf.Variable(
          tf.truncated_normal([hidden2_units, NUM_CLASSES],
                              stddev=1.0 / math.sqrt(float(hidden2_units))),
          name='weights')
      biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                           name='biases')
      logits = tf.matmul(hidden2, weights) + biases
    return logits
  
  
  def loss(logits, labels):
  
    batch_size = tf.size(labels)
    #print('batch size %d' %(batch_size))
    labels = tf.expand_dims(labels, 1)
    indices = tf.expand_dims(tf.range(0, batch_size), 1)
    concated = tf.concat(1, [indices, labels])
    #print('Done2')
    onehot_labels = tf.sparse_to_dense(
        concated, tf.pack([batch_size, 16]), 1.0, 0.0)
    #print('Done1')
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                            onehot_labels,
                                                            name='xentropy')
    loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
    tf.summary.scalar(loss.op.name, loss)
  
    return loss
  
  
  def training(loss, learning_rate):
    optimizer=tf.train.GradientDescentOptimizer(learning_rate)
    global_step=tf.Variable(0,name='global_step',trainable=False)
    train_op = optimizer.minimize(loss, global_step=global_step)
  
    return train_op
  
  
  def evaluation(logits, labels):
  
  
    correct = tf.nn.in_top_k(logits, labels, 1)
    # Return the number of true entries.
    return tf.reduce_sum(tf.cast(correct, tf.int32))
  </code></pre>
  
  <p>and train the model in this file:</p>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import os.path
  import sys
  import time
  import numpy as np
  
  import tensorflow as tf
  
  import mnist
  
  
  # Basic model parameters as external flags.
  #FLAGS = None
  
  # Constants used for dealing with the files, matches convert_to_records.
  TRAIN_FILE = 'train.tfrecords'
  VALIDATION_FILE = 'validation.tfrecords'
  TEST_FILE='test.tfrecords'
  
  
  flags = tf.app.flags
  FLAGS = flags.FLAGS
  #FLAGS = None
  flags.DEFINE_string('train_dir', '/home/queenie/image2tfrecord/tfrecords-28-gray/', 'Directory to put the training data.')
  flags.DEFINE_string('filename', 'train.tfrecords', 'Directory to put the training data.')
  flags.DEFINE_integer('batch_size', 100, 'Batch size.  '
                       'Must divide evenly into the dataset sizes.')
  flags.DEFINE_integer('num_epochs', None, 'Batch size.  '
                       'Must divide evenly into the dataset sizes.')
  flags.DEFINE_integer('hidden1', 128,'balabala')
  flags.DEFINE_integer('hidden2', 32,'balabala')
  flags.DEFINE_integer('learning_rate', 0.01,'balabala')
  flags.DEFINE_integer('max_steps', 50000,'balabala')
  
  
  def placeholder_inputs(batch_size):
    images_placeholder=tf.placeholder(tf.float32,shape=(batch_size,mnist.IMAGE_PIXELS))
    labels_placeholder=tf.placeholder(tf.int32,shape=(batch_size))
    return images_placeholder,labels_placeholder
  
  def fill_feed_dict(images_feed,labels_feed,images_pl,labels_pl):
  
    feed_dict={
    images_pl:images_feed,
    labels_pl:labels_feed,
    }
    return feed_dict
  
  def read_and_decode(filename_queue):
    reader = tf.TFRecordReader()
    _, serialized_example = reader.read(filename_queue)
    features = tf.parse_single_example(
        serialized_example,
        # Defaults are not specified since both keys are required.
        features={
            'image_raw': tf.FixedLenFeature([], tf.string),
            'label': tf.FixedLenFeature([], tf.int64),
        })
  
    # Convert from a scalar string tensor (whose single string has
    # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape
    # [mnist.IMAGE_PIXELS].
    image = tf.decode_raw(features['image_raw'], tf.uint8)
    image.set_shape([mnist.IMAGE_PIXELS])
  
    # OPTIONAL: Could reshape into a 28x28 image and apply distortions
    # here.  Since we are not applying any distortions in this
    # example, and the next step expects the image to be flattened
    # into a vector, we don't bother.
  
    # Convert from [0, 255] -&gt; [-0.5, 0.5] floats.
    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
  
    # Convert label from a scalar uint8 tensor to an int32 scalar.
    label = tf.cast(features['label'], tf.int32)
  
    return image, label
  
  
  def do_eval(sess,eval_correct):
      true_count=0
      for step in xrange(FLAGS.batch_size):
          #print(sess.run(eval_correct))
          true_count+=sess.run(eval_correct)
  
      precision=float(true_count)/FLAGS.batch_size/FLAGS.batch_size
      print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %
  (FLAGS.batch_size, true_count, precision))
      return precision
  
  
  def inputs(train, batch_size, num_epochs):
  
    if not num_epochs: num_epochs = None
    if train=='train':
      filename=os.path.join(FLAGS.train_dir,TRAIN_FILE)
    elif train=='validation':
      filename=os.path.join(FLAGS.train_dir,VALIDATION_FILE)
    else:
      filename=os.path.join(FLAGS.train_dir,TEST_FILE)
  
  
    # filename = os.path.join(FLAGS.train_dir,
    #                         TRAIN_FILE if train else VALIDATION_FILE)
  
    with tf.name_scope('input'):
      filename_queue = tf.train.string_input_producer(
          [filename], num_epochs=None)
  
      # Even when reading in multiple threads, share the filename
      # queue.
      image, label = read_and_decode(filename_queue)
  
      # Shuffle the examples and collect them into batch_size batches.
      # (Internally uses a RandomShuffleQueue.)
      # We run this in two threads to avoid being a bottleneck.
      images, sparse_labels = tf.train.shuffle_batch(
          [image, label], batch_size=batch_size, num_threads=2,
          capacity=1000 + 3 * batch_size,
          # Ensures a minimum amount of shuffling of examples.
          min_after_dequeue=1000)
  
      return images, sparse_labels
  
  
  def run_training():
  
  
  
    with tf.Graph().as_default():
  
  
  
      # Build a Graph that computes predictions from the inference model.
      images, labels = inputs(train='train', batch_size=FLAGS.batch_size,
                              num_epochs=FLAGS.num_epochs)
  
      images_valid,labels_valid=inputs(train='validation', batch_size=FLAGS.batch_size,
                               num_epochs=FLAGS.num_epochs)
  
      images_test,labels_test=inputs(train='test', batch_size=FLAGS.batch_size,
                               num_epochs=FLAGS.num_epochs)
  
      logits = mnist.inference(images,
                               FLAGS.hidden1,
                               FLAGS.hidden2)
      # Add to the Graph the loss calculation.
  
      valid_prediction=mnist.inference(images_valid,FLAGS.hidden1,FLAGS.hidden2)
  
      test_prediction=mnist.inference(images_test,FLAGS.hidden1,FLAGS.hidden2)
  
  
  
      loss = mnist.loss(logits, labels)
  
      # Add to the Graph operations that train the model.
      train_op = mnist.training(loss, FLAGS.learning_rate)
  
      eval_correct=mnist.evaluation(logits,labels)
  
      eval_correct_valid=mnist.evaluation(valid_prediction,labels_valid)
  
      eval_correct_test=mnist.evaluation(test_prediction,labels_test)
  
      summary_op=tf.merge_all_summaries()
      # The op for initializing the variables.
      init_op = tf.group(tf.initialize_all_variables(),
                         tf.initialize_local_variables())
  
      saver = tf.train.Saver()
      # Create a session for running operations in the Graph.
      sess = tf.Session()
  
  
      # Initialize the variables (the trained variables and the
      # epoch counter).
      sess.run(init_op)
  
      summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
  
      # Start input enqueue threads.
      coord = tf.train.Coordinator()
      threads = tf.train.start_queue_runners(sess=sess, coord=coord)
  
      try:
        step = 0
        train_precision=0
        validation_precision=0
        test_precision=0
        #while not coord.should_stop():
        while not coord.should_stop():
          start_time = time.time()
  
  
          _, loss_value,images_see,labels_see = sess.run([train_op, loss,images,labels])
          #print('run done')
  
          duration = time.time() - start_time
  
  
          # Print an overview fairly often.
          if step % 100 == 0:
            print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,
                                                       duration))
            precision_tr=do_eval(sess,eval_correct)
            summary_str=sess.run(summary_op)
            summary_writer.add_summary(summary_str,step)
  
  
          if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
            checkpoint_file = os.path.join(FLAGS.train_dir, 'model.ckpt')
            saver.save(sess, checkpoint_file, global_step=step)
            print('Train:')
            do_eval(sess,eval_correct)
            print('Validation:')
            do_eval(sess,eval_correct_valid)
            print('Test:')
            do_eval(sess,eval_correct_test)
  
          step += 1
  
      except tf.errors.OutOfRangeError:
        print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
      finally:
        # When done, ask the threads to stop.
        coord.request_stop()
  
      # Wait for threads to finish.
      coord.join(threads)
      sess.close()
  
  
  run_training()
  </code></pre>
  
  <p>then I get the tensorboard like these,6 charts about queue.
  <a href="https://i.stack.imgur.com/asWIH.png" rel="nofollow noreferrer">The tensorboard screenshot</a></p>