def ensemble_preds(dataset, nb_teachers, stdnt_data):
 result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)
 result = np.zeros(result_shape, dtype=np.float32)
 for teacher_id in xrange(nb_teachers):
  if FLAGS.deeper:
   ckpt_path = ((((((((FLAGS.teachers_dir + '/') + str(dataset)) + '_') + str(nb_teachers)) + '_teachers_') + str(teacher_id)) + '_deep.ckpt-') + str((FLAGS.teachers_max_steps - 1)))
  else:
   ckpt_path = ((((((((FLAGS.teachers_dir + '/') + str(dataset)) + '_') + str(nb_teachers)) + '_teachers_') + str(teacher_id)) + '.ckpt-') + str((FLAGS.teachers_max_steps - 1)))
  result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)
  print((('Computed Teacher ' + str(teacher_id)) + ' softmax predictions'))
 return result

def ensemble_preds ( dataset , nb_teachers , stdnt_data ) : result_shape = ( nb_teachers , len ( stdnt_data ) , FLAGS . nb_labels ) result = np . zeros ( result_shape , dtype = np . float32 ) for teacher_id in xrange ( nb_teachers ) : if FLAGS . deeper : ckpt_path = ( ( ( ( ( ( ( ( FLAGS . teachers_dir + '/' ) + str ( dataset ) ) + '_' ) + str ( nb_teachers ) ) + '_teachers_' ) + str ( teacher_id ) ) + '_deep.ckpt-' ) + str ( ( FLAGS . teachers_max_steps - 1 ) ) ) else : ckpt_path = ( ( ( ( ( ( ( ( FLAGS . teachers_dir + '/' ) + str ( dataset ) ) + '_' ) + str ( nb_teachers ) ) + '_teachers_' ) + str ( teacher_id ) ) + '.ckpt-' ) + str ( ( FLAGS . teachers_max_steps - 1 ) ) ) result [ teacher_id ] = deep_cnn . softmax_preds ( stdnt_data , ckpt_path ) print ( ( ( 'Computed Teacher ' + str ( teacher_id ) ) + ' softmax predictions' ) ) return result
given a dataset .