parameters: (wav_data, labels, input_layer_name, output_layer_name,
                num_top_predictions)
docstring: Runs the audio data through the graph and prints predictions.
docstring_summary: Runs the audio data through the graph and prints predictions.
docstring_tokens: Runs the audio data through the graph and prints predictions .
function: def run_graph(wav_data, labels, input_layer_name, output_layer_name,
                num_top_predictions):
    """Runs the audio data through the graph and prints predictions."""
    with tf.Session() as sess:
      # Feed the audio data as input to the graph.
      #   predictions  will contain a two-dimensional array, where one
      #   dimension represents the input image count, and the other has
      #   predictions per class
      softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
      predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
  
      # Sort to show labels in order of confidence
      top_k = predictions.argsort()[-num_top_predictions:][::-1]
      for node_id in top_k:
        human_string = labels[node_id]
        score = predictions[node_id]
        print('%s (score = %.5f)' % (human_string, score))
  
      return 0
function_tokens: def run_graph ( wav_data , labels , input_layer_name , output_layer_name , num_top_predictions ) : with tf . Session ( ) as sess : # Feed the audio data as input to the graph. #   predictions  will contain a two-dimensional array, where one #   dimension represents the input image count, and the other has #   predictions per class softmax_tensor = sess . graph . get_tensor_by_name ( output_layer_name ) predictions , = sess . run ( softmax_tensor , { input_layer_name : wav_data } ) # Sort to show labels in order of confidence top_k = predictions . argsort ( ) [ - num_top_predictions : ] [ : : - 1 ] for node_id in top_k : human_string = labels [ node_id ] score = predictions [ node_id ] print ( '%s (score = %.5f)' % ( human_string , score ) ) return 0