new_contents: #!/usr/bin/env python3
  
  from gnt_model import model, IMAGE_HEIGHT, IMAGE_WIDTH, PIXEL_DEPTH
  
  import sys
  import numpy
  import png
  import skimage.io as io
  import tensorflow as tf
  
  _, model_path, image_filename = sys.argv
  
  with open(image_filename, 'rb') as f:
      direct = png.Reader(f).asDirect()
      print(direct)
  
      mapped = map(numpy.uint8, direct[2])
      print(mapped)
  
      image_2d = numpy.vstack(list(mapped))
  
  
  image_size_const = tf.constant((IMAGE_HEIGHT, IMAGE_WIDTH), dtype=tf.int32)
  x = tf.placeholder(tf.float32, shape=(1, None, None, 1))
  resized_x = tf.image.resize_images(images=x,
                                            size=image_size_const
                                            )
  resized_padded_x = tf.image.resize_image_with_crop_or_pad(image=resized_x,
                                             target_height=IMAGE_HEIGHT,
                                             target_width=IMAGE_WIDTH)
  
  scaled_x = resized_padded_x / PIXEL_DEPTH - 0.5
  
  
  logits = model(scaled_x)
  prediction = tf.nn.softmax(logits)
  
  saver = tf.train.Saver()
  
  with open('label_keys.list', encoding='utf8') as f:
      labels = f.readlines()
  
  with tf.Session() as sess:
      # Restore variables from disk.
      saver.restore(sess, model_path)
      print("Model restored.")
  
      height, width = image_2d.shape
      predictions = sess.run([prediction], feed_dict={x: image_2d.reshape((1, height, width, 1))})
  
      for index in numpy.argsort(predictions[0][0])[-11:]:
          print(labels[index])
  
  
  #images_batch / PIXEL_DEPTH - 0.5
  
subject: add a prediction script