def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle):
 num_preprocess_threads = 16
 if shuffle:
  (images, label_batch) = tf.train.shuffle_batch([image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=(min_queue_examples + (3 * batch_size)), min_after_dequeue=min_queue_examples)
 else:
  (images, label_batch) = tf.train.batch([image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=(min_queue_examples + (3 * batch_size)))
 tf.image_summary('images', images)
 return (images, tf.reshape(label_batch, [batch_size]))

def _generate_image_and_label_batch ( image , label , min_queue_examples , batch_size , shuffle ) : num_preprocess_threads = 16 if shuffle : ( images , label_batch ) = tf . train . shuffle_batch ( [ image , label ] , batch_size = batch_size , num_threads = num_preprocess_threads , capacity = ( min_queue_examples + ( 3 * batch_size ) ) , min_after_dequeue = min_queue_examples ) else : ( images , label_batch ) = tf . train . batch ( [ image , label ] , batch_size = batch_size , num_threads = num_preprocess_threads , capacity = ( min_queue_examples + ( 3 * batch_size ) ) ) tf . image_summary ( 'images' , images ) return ( images , tf . reshape ( label_batch , [ batch_size ] ) )
construct a queued batch of images and labels .