本文整理汇总了Python中differential_privacy.multiple_teachers.aggregation.py方法的典型用法代码示例。如果您正苦于以下问题：Python aggregation.py方法的具体用法？Python aggregation.py怎么用？Python aggregation.py使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类differential_privacy.multiple_teachers.aggregation的用法示例。
在下文中一共展示了aggregation.py方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: ensemble_preds
# 需要导入模块: from differential_privacy.multiple_teachers import aggregation [as 别名]
# 或者: from differential_privacy.multiple_teachers.aggregation import py [as 别名]
def ensemble_preds(dataset, nb_teachers, stdnt_data):
"""
Given a dataset, a number of teachers, and some input data, this helper
function queries each teacher for predictions on the data and returns
all predictions in a single array. (That can then be aggregated into
one single prediction per input using aggregation.py (cf. function
prepare_student_data() below)
:param dataset: string corresponding to mnist, cifar10, or svhn
:param nb_teachers: number of teachers (in the ensemble) to learn from
:param stdnt_data: unlabeled student training data
:return: 3d array (teacher id, sample id, probability per class)
"""
# Compute shape of array that will hold probabilities produced by each
# teacher, for each training point, and each output class
result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)
# Create array that will hold result
result = np.zeros(result_shape, dtype=np.float32)
# Get predictions from each teacher
for teacher_id in xrange(nb_teachers):
# Compute path of checkpoint file for teacher model with ID teacher_id
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) #NOLINT(long-line)
else:
ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)
# Get predictions on our training data and store in result array
result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)
# This can take a while when there are a lot of teachers so output status
print("Computed Teacher " + str(teacher_id) + " softmax predictions")
return result