title: Wrong labels in the inception v3 network (Tensorflow)
body: <p>I am using build_image_data.py script from here:<a href="https://github.com/tensorflow/models/blob/master/inception/inception/data/build_image_data.py" rel="nofollow noreferrer">https://github.com/tensorflow/models/blob/master/inception/inception/data/build_image_data.py</a> 
  exactly like the documentation to convert my dataset to TFRecords format. In the inception_train.py script, when I print images and labels, labels do not correspond to the images so I can't proceed to right training. The dataset I am using is unbalanced (different number of images between classes). I also made a test using the same number of images between classes and labels are still wrong. The tensorflow code is untouched, the only change I made is not applying distortions in the image_processing.py script. I don't know if labels are wrong because of my TFR conversion or because of image_processing.py script which returns images and labels. Any ideas?</p>
  
  <p>Tensorflow version: 0.10 
  OS: Ubuntu 14.04</p>
  
  <p>The snippet of code in the inception_train.py script to check it is:</p>
  
  <pre><code>labs = sess.run(labels)
  imgs = sess.run(images)
  
  
  for i in range(FLAGS.batch_size):
    print('Label ' + str(labs[i]))
    plt.imshow(imgs[i, :, :, :])
    plt.show()
  </code></pre>