parameters: (image)
docstring: Runs inference on an image.
  
    Args:
      image: Image file name.
  
    Returns:
      Nothing
docstring_summary: Runs inference on an image.
docstring_tokens: Runs inference on an image .
function: def run_inference_on_image(image):
    """Runs inference on an image.
  
    Args:
      image: Image file name.
  
    Returns:
      Nothing
    """
    if not tf.gfile.Exists(image):
      tf.logging.fatal('File does not exist %s', image)
    image_data = tf.gfile.FastGFile(image, 'rb').read()
  
    # Creates graph from saved GraphDef.
    create_graph()
  
    with tf.Session() as sess:
      # Some useful tensors:
      # 'softmax:0': A tensor containing the normalized prediction across
      #   1000 labels.
      # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
      #   float description of the image.
      # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
      #   encoding of the image.
      # Runs the softmax tensor by feeding the image_data as input to the graph.
      softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
      predictions = sess.run(softmax_tensor,
                             {'DecodeJpeg/contents:0': image_data})
      predictions = np.squeeze(predictions)
  
      # Creates node ID --> English string lookup.
      node_lookup = NodeLookup()
  
      top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
      for node_id in top_k:
        human_string = node_lookup.id_to_string(node_id)
        score = predictions[node_id]
        print('%s (score = %.5f)' % (human_string, score))
function_tokens: def run_inference_on_image ( image ) : if not tf . gfile . Exists ( image ) : tf . logging . fatal ( 'File does not exist %s' , image ) image_data = tf . gfile . FastGFile ( image , 'rb' ) . read ( ) # Creates graph from saved GraphDef. create_graph ( ) with tf . Session ( ) as sess : # Some useful tensors: # 'softmax:0': A tensor containing the normalized prediction across #   1000 labels. # 'pool_3:0': A tensor containing the next-to-last layer containing 2048 #   float description of the image. # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG #   encoding of the image. # Runs the softmax tensor by feeding the image_data as input to the graph. softmax_tensor = sess . graph . get_tensor_by_name ( 'softmax:0' ) predictions = sess . run ( softmax_tensor , { 'DecodeJpeg/contents:0' : image_data } ) predictions = np . squeeze ( predictions ) # Creates node ID --> English string lookup. node_lookup = NodeLookup ( ) top_k = predictions . argsort ( ) [ - FLAGS . num_top_predictions : ] [ : : - 1 ] for node_id in top_k : human_string = node_lookup . id_to_string ( node_id ) score = predictions [ node_id ] print ( '%s (score = %.5f)' % ( human_string , score ) )