From d3df5c8424cd3d2a1e69363ca9660a1c04e2fe1e Mon Sep 17 00:00:00 2001 From: shu-kun-zhang Date: Wed, 8 Jun 2022 11:45:16 +0800 Subject: [PATCH] Fix API Examples Import Issue --- .../mindarmour.adv_robustness.detectors.rst | 1 + .../mindarmour.adv_robustness.evaluations.rst | 1 + .../api_python/mindarmour.privacy.sup_privacy.rst | 9 ++-- docs/api/api_python/mindarmour.reliability.rst | 7 +-- docs/api/api_python/mindarmour.rst | 9 ++-- .../attacks/black/hop_skip_jump_attack.py | 13 ++++- .../attacks/black/natural_evolutionary_strategy.py | 17 +++++-- .../attacks/black/pointwise_attack.py | 13 ++++- .../attacks/black/salt_and_pepper_attack.py | 15 +++++- .../adv_robustness/attacks/carlini_wagner.py | 7 ++- mindarmour/adv_robustness/attacks/deep_fool.py | 3 +- .../adv_robustness/attacks/gradient_method.py | 30 +++++------- .../attacks/iterative_gradient_method.py | 25 ++++------ mindarmour/adv_robustness/attacks/jsma.py | 3 +- mindarmour/adv_robustness/attacks/lbfgs.py | 13 ++++- .../adv_robustness/defenses/adversarial_defense.py | 55 +++++++++++++++++----- .../defenses/natural_adversarial_defense.py | 19 ++++++-- .../defenses/projected_adversarial_defense.py | 19 ++++++-- .../adv_robustness/detectors/ensemble_detector.py | 3 +- mindarmour/adv_robustness/detectors/mag_net.py | 3 +- .../detectors/region_based_detector.py | 3 +- .../adv_robustness/detectors/spatial_smoothing.py | 3 +- .../privacy/diff_privacy/optimizer/optimizer.py | 13 +++-- mindarmour/privacy/diff_privacy/train/model.py | 3 +- .../privacy/evaluation/membership_inference.py | 3 +- .../privacy/sup_privacy/mask_monitor/masker.py | 3 +- mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py | 6 ++- mindarmour/privacy/sup_privacy/train/model.py | 3 +- .../concept_drift/concept_drift_check_images.py | 3 +- .../concept_drift_check_time_series.py | 3 +- .../model_fault_injection/fault_injection.py | 3 +- 31 files changed, 212 insertions(+), 99 deletions(-) diff --git a/docs/api/api_python/mindarmour.adv_robustness.detectors.rst b/docs/api/api_python/mindarmour.adv_robustness.detectors.rst index 1eb53d3..058fa3b 100644 --- a/docs/api/api_python/mindarmour.adv_robustness.detectors.rst +++ b/docs/api/api_python/mindarmour.adv_robustness.detectors.rst @@ -306,6 +306,7 @@ mindarmour.adv_robustness.detectors - **max_k_neighbor** (int) - 最近邻的最大数量。默认值：1000。 - **chunk_size** (int) - 缓冲区大小。默认值：1000。 - **max_buffer_size** (int) - 最大缓冲区大小。默认值：10000。默认值：False。 + - **tuning** (bool) - 计算k个最近邻的平均距离，如果'tuning'为true，k=K。如果为False，k=1,...,K。默认值：False。 - **fpr** (float) - 合法查询序列上的误报率。默认值：0.001 .. py:method:: clear_buffer() diff --git a/docs/api/api_python/mindarmour.adv_robustness.evaluations.rst b/docs/api/api_python/mindarmour.adv_robustness.evaluations.rst index 7628ed9..78ccf49 100644 --- a/docs/api/api_python/mindarmour.adv_robustness.evaluations.rst +++ b/docs/api/api_python/mindarmour.adv_robustness.evaluations.rst @@ -179,6 +179,7 @@ mindarmour.adv_robustness.evaluations **参数：** - **metrics_name** (Union[tuple, list]) - 要显示的度量名称数组。每组值对应一条雷达曲线。 + - **metrics_data** (numpy.ndarray) - 多个雷达曲线的每个度量的（归一化）值，如[[0.5, 0.8, ...], [0.2,0.6,...], ...]。 - **labels** (Union[tuple, list]) - 所有雷达曲线的图例。 - **title** (str) - 图表的标题。 - **scale** (str) - 用于调整轴刻度的标量，如'hide'、'norm'、'sparse'、'dense'。默认值：'hide'。 diff --git a/docs/api/api_python/mindarmour.privacy.sup_privacy.rst b/docs/api/api_python/mindarmour.privacy.sup_privacy.rst index eacab67..ea35551 100644 --- a/docs/api/api_python/mindarmour.privacy.sup_privacy.rst +++ b/docs/api/api_python/mindarmour.privacy.sup_privacy.rst @@ -7,7 +7,8 @@ mindarmour.privacy.sup_privacy 周期性检查抑制隐私功能状态和切换（启动/关闭）抑制操作。 - 详情请查看： `教程 `_。 + 详情请查看： `应用抑制隐私机制保护用户隐私 + `_。 **参数：** @@ -26,7 +27,7 @@ mindarmour.privacy.sup_privacy 完整的模型训练功能。抑制隐私函数嵌入到重载的mindspore.train.model.Model中。 - 有关详细信息，请查看： `教程 `_。 + 有关详细信息，请查看： `应用抑制隐私机制保护用户隐私 `_。 **参数：** @@ -47,7 +48,7 @@ mindarmour.privacy.sup_privacy SuppressCtrl机制的工厂类。 - 详情请查看： `教程 `_。 + 详情请查看： `应用抑制隐私机制保护用户隐私 `_。 .. py:method:: create(networks, mask_layers, policy='local_train', end_epoch=10, batch_num=20, start_epoch=3, mask_times=1000, lr=0.05, sparse_end=0.9, sparse_start=0.0) @@ -72,7 +73,7 @@ mindarmour.privacy.sup_privacy 完成抑制隐私操作，包括计算抑制比例，找到应该抑制的参数，并永久抑制这些参数。 - 详情请查看： `教程 `_。 + 详情请查看： `应用抑制隐私机制保护用户隐私 `_。 **参数：** diff --git a/docs/api/api_python/mindarmour.reliability.rst b/docs/api/api_python/mindarmour.reliability.rst index 688bad5..4a22a7e 100644 --- a/docs/api/api_python/mindarmour.reliability.rst +++ b/docs/api/api_python/mindarmour.reliability.rst @@ -7,7 +7,7 @@ MindArmour的可靠性方法。 故障注入模块模拟深度神经网络的各种故障场景，并评估模型的性能和可靠性。 - 详情请查看 `教程 `_。 + 详情请查看 `实现模型故障注入评估模型容错性 `_。 **参数：** @@ -41,7 +41,8 @@ MindArmour的可靠性方法。 .. py:class:: mindarmour.reliability.ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10, step=10, threshold_index=1.5, need_label=False) 概念漂移检查时间序列（ConceptDriftCheckTimeSeries）用于样本序列分布变化检测。 - 有关详细信息，请查看 `教程 `_。 + 有关详细信息，请查看 `实现时序数据概念漂移检测应用 + `_。 **参数：** @@ -106,7 +107,7 @@ MindArmour的可靠性方法。 训练OOD检测器。提取训练数据特征，得到聚类中心。测试数据特征与聚类中心之间的距离确定图像是否为分布外（OOD）图像。 - 有关详细信息，请查看 `教程 `_。 + 有关详细信息，请查看 `实现图像数据概念漂移检测应用 `_。 **参数：** diff --git a/docs/api/api_python/mindarmour.rst b/docs/api/api_python/mindarmour.rst index 226a7b2..3a3a5a1 100644 --- a/docs/api/api_python/mindarmour.rst +++ b/docs/api/api_python/mindarmour.rst @@ -223,7 +223,7 @@ MindArmour是MindSpore的工具箱，用于增强模型可信，实现隐私保 这个类就是重载Mindpore.train.model.Model。 - 详情请查看： `教程 `_。 + 详情请查看： `应用差分隐私机制保护用户隐私 `_。 **参数：** @@ -241,7 +241,7 @@ MindArmour是MindSpore的工具箱，用于增强模型可信，实现隐私保 成员推理是由Shokri、Stronati、Song和Shmatikov提出的一种用于推测用户隐私数据的灰盒攻击。它需要训练样本的loss或logits结果。（隐私是指单个用户的一些敏感属性）。 - 有关详细信息，请参见：`教程 `_。 + 有关详细信息，请参见：`使用成员推理测试模型安全性 `_。 参考文献：`Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov. Membership Inference Attacks against Machine Learning Models. 2017. `_。 @@ -340,7 +340,8 @@ MindArmour是MindSpore的工具箱，用于增强模型可信，实现隐私保 根据target_features重建图像。 **参数：** - + - **target_features** (numpy.ndarray) - 原始图像的深度表示。 `target_features` 的第一个维度应该是img_num。 + 需要注意的是，如果img_num等于1，则target_features的形状应该是(1, dim2, dim3, ...)。 - **iters** (int) - 逆向攻击的迭代次数，应为正整数。默认值：100。 **返回：** @@ -356,7 +357,7 @@ MindArmour是MindSpore的工具箱，用于增强模型可信，实现隐私保 概念漂移检查时间序列（ConceptDriftCheckTimeSeries）用于样本序列分布变化检测。 - 有关详细信息，请查看： `教程 `_。 + 有关详细信息，请查看： `实现时序数据概念漂移检测应用 `_。 **参数：** diff --git a/mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py b/mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py index 22e41e1..863a69b 100644 --- a/mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py +++ b/mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py @@ -77,8 +77,19 @@ class HopSkipJumpAttack(Attack): Examples: >>> from mindspore import Tensor >>> from mindarmour import BlackModel + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import HopSkipJumpAttack - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._reduce = P.ReduceSum() + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._reduce(out, 2) + ... out = self._squeeze(out) + ... return out >>> class ModelToBeAttacked(BlackModel): ... def __init__(self, network): ... super(ModelToBeAttacked, self).__init__() diff --git a/mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py b/mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py index e7d7402..9fa9314 100644 --- a/mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py +++ b/mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py @@ -81,15 +81,26 @@ class NES(Attack): Examples: >>> from mindspore import Tensor >>> from mindarmour import BlackModel + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import NES - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._reduce = P.ReduceSum() + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._reduce(out, 2) + ... out = self._squeeze(out) + ... return out >>> class ModelToBeAttacked(BlackModel): ... def __init__(self, network): ... super(ModelToBeAttacked, self).__init__() ... self._network = network ... def predict(self, inputs): - ... if len(inputs.shape) == 3: - ... inputs = inputs[np.newaxis, :] + ... if len(inputs.shape) == 1: + ... inputs = np.expand_dims(inputs, axis=0) ... result = self._network(Tensor(inputs.astype(np.float32))) ... return result.asnumpy() >>> net = Net() diff --git a/mindarmour/adv_robustness/attacks/black/pointwise_attack.py b/mindarmour/adv_robustness/attacks/black/pointwise_attack.py index 1f33a6e..5cf0b10 100644 --- a/mindarmour/adv_robustness/attacks/black/pointwise_attack.py +++ b/mindarmour/adv_robustness/attacks/black/pointwise_attack.py @@ -49,8 +49,19 @@ class PointWiseAttack(Attack): Examples: >>> from mindspore import Tensor >>> from mindarmour import BlackModel + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import PointWiseAttack - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._reduce = P.ReduceSum() + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._reduce(out, 2) + ... out = self._squeeze(out) + ... return out >>> class ModelToBeAttacked(BlackModel): ... def __init__(self, network): ... super(ModelToBeAttacked, self).__init__() diff --git a/mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py b/mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py index d6e1b1f..e9cc61c 100644 --- a/mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py +++ b/mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py @@ -42,13 +42,26 @@ class SaltAndPepperNoiseAttack(Attack): Examples: >>> from mindspore import Tensor >>> from mindarmour import BlackModel + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import SaltAndPepperNoiseAttack - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._reduce = P.ReduceSum() + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._reduce(out, 2) + ... out = self._squeeze(out) + ... return out >>> class ModelToBeAttacked(BlackModel): ... def __init__(self, network): ... super(ModelToBeAttacked, self).__init__() ... self._network = network ... def predict(self, inputs): + ... if len(inputs.shape) == 1: + ... inputs = np.expand_dims(inputs, axis=0) ... result = self._network(Tensor(inputs.astype(np.float32))) ... return result.asnumpy() >>> net = Net() diff --git a/mindarmour/adv_robustness/attacks/carlini_wagner.py b/mindarmour/adv_robustness/attacks/carlini_wagner.py index b02ab33..224b7d8 100644 --- a/mindarmour/adv_robustness/attacks/carlini_wagner.py +++ b/mindarmour/adv_robustness/attacks/carlini_wagner.py @@ -97,13 +97,12 @@ class CarliniWagnerL2Attack(Attack): input labels are onehot-coded. Default: True. Examples: - >>> import mindspore.ops.operations as M - >>> from mindspore.nn import Cell + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import CarliniWagnerL2Attack - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() - ... self._softmax = M.Softmax() + ... self._softmax = P.Softmax() ... def construct(self, inputs): ... out = self._softmax(inputs) ... return out diff --git a/mindarmour/adv_robustness/attacks/deep_fool.py b/mindarmour/adv_robustness/attacks/deep_fool.py index 60eefa2..d4e4c3a 100644 --- a/mindarmour/adv_robustness/attacks/deep_fool.py +++ b/mindarmour/adv_robustness/attacks/deep_fool.py @@ -118,10 +118,9 @@ class DeepFool(Attack): Examples: >>> import mindspore.ops.operations as P - >>> from mindspore.nn import Cell >>> from mindspore import Tensor >>> from mindarmour.adv_robustness.attacks import DeepFool - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() diff --git a/mindarmour/adv_robustness/attacks/gradient_method.py b/mindarmour/adv_robustness/attacks/gradient_method.py index 2c789e1..9a154c3 100644 --- a/mindarmour/adv_robustness/attacks/gradient_method.py +++ b/mindarmour/adv_robustness/attacks/gradient_method.py @@ -149,9 +149,8 @@ class FastGradientMethod(GradientMethod): is already equipped with loss function. Default: None. Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import FastGradientMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -162,7 +161,7 @@ class FastGradientMethod(GradientMethod): >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) >>> net = Net() - >>> attack = FastGradientMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = FastGradientMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ @@ -230,9 +229,8 @@ class RandomFastGradientMethod(FastGradientMethod): ValueError: eps is smaller than alpha! Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import RandomFastGradientMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -243,7 +241,7 @@ class RandomFastGradientMethod(FastGradientMethod): >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) - >>> attack = RandomFastGradientMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = RandomFastGradientMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ @@ -283,9 +281,8 @@ class FastGradientSignMethod(GradientMethod): is already equipped with loss function. Default: None. Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -296,7 +293,7 @@ class FastGradientSignMethod(GradientMethod): >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) - >>> attack = FastGradientSignMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = FastGradientSignMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ @@ -361,9 +358,8 @@ class RandomFastGradientSignMethod(FastGradientSignMethod): ValueError: eps is smaller than alpha! Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import RandomFastGradientSignMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -374,7 +370,7 @@ class RandomFastGradientSignMethod(FastGradientSignMethod): >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) - >>> attack = RandomFastGradientSignMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = RandomFastGradientSignMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ @@ -410,9 +406,8 @@ class LeastLikelyClassMethod(FastGradientSignMethod): is already equipped with loss function. Default: None. Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import LeastLikelyClassMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -423,7 +418,7 @@ class LeastLikelyClassMethod(FastGradientSignMethod): >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) >>> net = Net() - >>> attack = LeastLikelyClassMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = LeastLikelyClassMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ @@ -462,9 +457,8 @@ class RandomLeastLikelyClassMethod(FastGradientSignMethod): ValueError: eps is smaller than alpha! Examples: - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import RandomLeastLikelyClassMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() @@ -475,7 +469,7 @@ class RandomLeastLikelyClassMethod(FastGradientSignMethod): >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) >>> net = Net() - >>> attack = RandomLeastLikelyClassMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = RandomLeastLikelyClassMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> adv_x = attack.generate(inputs, labels) """ diff --git a/mindarmour/adv_robustness/attacks/iterative_gradient_method.py b/mindarmour/adv_robustness/attacks/iterative_gradient_method.py index 6567630..4ec509c 100644 --- a/mindarmour/adv_robustness/attacks/iterative_gradient_method.py +++ b/mindarmour/adv_robustness/attacks/iterative_gradient_method.py @@ -179,9 +179,8 @@ class BasicIterativeMethod(IterativeGradientMethod): Examples: >>> from mindspore.ops import operations as P - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import BasicIterativeMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() @@ -189,7 +188,7 @@ class BasicIterativeMethod(IterativeGradientMethod): ... out = self._softmax(inputs) ... return out >>> net = Net() - >>> attack = BasicIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = BasicIterativeMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) @@ -284,9 +283,8 @@ class MomentumIterativeMethod(IterativeGradientMethod): Examples: >>> from mindspore.ops import operations as P - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import MomentumIterativeMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() @@ -294,7 +292,7 @@ class MomentumIterativeMethod(IterativeGradientMethod): ... out = self._softmax(inputs) ... return out >>> net = Net() - >>> attack = MomentumIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = MomentumIterativeMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) @@ -428,9 +426,8 @@ class ProjectedGradientDescent(BasicIterativeMethod): Examples: >>> from mindspore.ops import operations as P - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() @@ -438,7 +435,7 @@ class ProjectedGradientDescent(BasicIterativeMethod): ... out = self._softmax(inputs) ... return out >>> net = Net() - >>> attack = ProjectedGradientDescent(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = ProjectedGradientDescent(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) @@ -526,9 +523,8 @@ class DiverseInputIterativeMethod(BasicIterativeMethod): Examples: >>> from mindspore.ops import operations as P - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import DiverseInputIterativeMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() @@ -536,7 +532,7 @@ class DiverseInputIterativeMethod(BasicIterativeMethod): ... out = self._softmax(inputs) ... return out >>> net = Net() - >>> attack = DiverseInputIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = DiverseInputIterativeMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) @@ -584,9 +580,8 @@ class MomentumDiverseInputIterativeMethod(MomentumIterativeMethod): Examples: >>> from mindspore.ops import operations as P - >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits >>> from mindarmour.adv_robustness.attacks import MomentumDiverseInputIterativeMethod - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() @@ -594,7 +589,7 @@ class MomentumDiverseInputIterativeMethod(MomentumIterativeMethod): ... out = self._softmax(inputs) ... return out >>> net = Net() - >>> attack = MomentumDiverseInputIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False)) + >>> attack = MomentumDiverseInputIterativeMethod(net, loss_fn=nn.SoftmaxCrossEntropyWithLogits(sparse=False)) >>> inputs = np.asarray([[0.1, 0.2, 0.7]], np.float32) >>> labels = np.asarray([2],np.int32) >>> labels = np.eye(3)[labels].astype(np.float32) diff --git a/mindarmour/adv_robustness/attacks/jsma.py b/mindarmour/adv_robustness/attacks/jsma.py index 5f04967..629d77c 100644 --- a/mindarmour/adv_robustness/attacks/jsma.py +++ b/mindarmour/adv_robustness/attacks/jsma.py @@ -56,9 +56,8 @@ class JSMAAttack(Attack): input labels are onehot-coded. Default: True. Examples: - >>> from mindspore.nn import Cell >>> from mindarmour.adv_robustness.attacks import JSMAAttack - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._relu = nn.ReLU() diff --git a/mindarmour/adv_robustness/attacks/lbfgs.py b/mindarmour/adv_robustness/attacks/lbfgs.py index 5fae6ed..4f823a4 100644 --- a/mindarmour/adv_robustness/attacks/lbfgs.py +++ b/mindarmour/adv_robustness/attacks/lbfgs.py @@ -56,7 +56,18 @@ class LBFGS(Attack): Examples: >>> from mindarmour.adv_robustness.attacks import LBFGS - >>> from tests.ut.python.utils.mock_net import Net + >>> import mindspore.ops.operations as P + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._reduce = P.ReduceSum() + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._reduce(out, 2) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> classes = 10 >>> attack = LBFGS(net, is_targeted=True) diff --git a/mindarmour/adv_robustness/defenses/adversarial_defense.py b/mindarmour/adv_robustness/defenses/adversarial_defense.py index 9cd133d..520baeb 100644 --- a/mindarmour/adv_robustness/defenses/adversarial_defense.py +++ b/mindarmour/adv_robustness/defenses/adversarial_defense.py @@ -37,17 +37,28 @@ class AdversarialDefense(Defense): Examples: >>> from mindspore.nn.optim.momentum import Momentum + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.defenses import AdversarialDefense - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._dense = nn.Dense(10, 10) + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._dense(out) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> lr = 0.001 >>> momentum = 0.9 - >>> batch_size = 32 + >>> batch_size = 16 >>> num_classes = 10 >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False) >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) >>> adv_defense = AdversarialDefense(net, loss_fn, optimizer) - >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32) + >>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32) >>> labels = np.random.randint(10, size=batch_size).astype(np.int32) >>> labels = np.eye(num_classes)[labels].astype(np.float32) >>> adv_defense.defense(inputs, labels) @@ -106,14 +117,25 @@ class AdversarialDefenseWithAttacks(AdversarialDefense): Examples: >>> from mindspore.nn.optim.momentum import Momentum + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod >>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent >>> from mindarmour.adv_robustness.defenses import AdversarialDefenseWithAttacks - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._dense = nn.Dense(10, 10) + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._dense(out) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> lr = 0.001 >>> momentum = 0.9 - >>> batch_size = 32 + >>> batch_size = 16 >>> num_classes = 10 >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False) >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) @@ -121,8 +143,8 @@ class AdversarialDefenseWithAttacks(AdversarialDefense): >>> pgd = ProjectedGradientDescent(net, loss_fn=loss_fn) >>> ead = AdversarialDefenseWithAttacks(net, [fgsm, pgd], loss_fn=loss_fn, ... optimizer=optimizer) - >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32) - >>> labels = np.random.randint(num_classes, size=batch_size).astype(np.int32) + >>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32) + >>> labels = np.random.randint(10, size=batch_size).astype(np.int32) >>> labels = np.eye(num_classes)[labels].astype(np.float32) >>> loss = ead.defense(inputs, labels) """ @@ -193,14 +215,25 @@ class EnsembleAdversarialDefense(AdversarialDefenseWithAttacks): Examples: >>> from mindspore.nn.optim.momentum import Momentum + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod >>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent >>> from mindarmour.adv_robustness.defenses import EnsembleAdversarialDefense - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._dense = nn.Dense(10, 10) + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._dense(out) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> lr = 0.001 >>> momentum = 0.9 - >>> batch_size = 32 + >>> batch_size = 16 >>> num_classes = 10 >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False) >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) @@ -208,8 +241,8 @@ class EnsembleAdversarialDefense(AdversarialDefenseWithAttacks): >>> pgd = ProjectedGradientDescent(net, loss_fn=loss_fn) >>> ead = EnsembleAdversarialDefense(net, [fgsm, pgd], loss_fn=loss_fn, ... optimizer=optimizer) - >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32) - >>> labels = np.random.randint(num_classes, size=batch_size).astype(np.int32) + >>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32) + >>> labels = np.random.randint(10, size=batch_size).astype(np.int32) >>> labels = np.eye(num_classes)[labels].astype(np.float32) >>> loss = ead.defense(inputs, labels) """ diff --git a/mindarmour/adv_robustness/defenses/natural_adversarial_defense.py b/mindarmour/adv_robustness/defenses/natural_adversarial_defense.py index 111ddf6..7d16a3f 100644 --- a/mindarmour/adv_robustness/defenses/natural_adversarial_defense.py +++ b/mindarmour/adv_robustness/defenses/natural_adversarial_defense.py @@ -37,18 +37,29 @@ class NaturalAdversarialDefense(AdversarialDefenseWithAttacks): Examples: >>> from mindspore.nn.optim.momentum import Momentum + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._dense = nn.Dense(10, 10) + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._dense(out) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> lr = 0.001 >>> momentum = 0.9 - >>> batch_size = 32 + >>> batch_size = 16 >>> num_classes = 10 >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False) >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) >>> nad = NaturalAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer) - >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32) - >>> labels = np.random.randint(num_classes, size=batch_size).astype(np.int32) + >>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32) + >>> labels = np.random.randint(10, size=batch_size).astype(np.int32) >>> labels = np.eye(num_classes)[labels].astype(np.float32) >>> loss = nad.defense(inputs, labels) """ diff --git a/mindarmour/adv_robustness/defenses/projected_adversarial_defense.py b/mindarmour/adv_robustness/defenses/projected_adversarial_defense.py index de688d3..8d928ed 100644 --- a/mindarmour/adv_robustness/defenses/projected_adversarial_defense.py +++ b/mindarmour/adv_robustness/defenses/projected_adversarial_defense.py @@ -43,18 +43,29 @@ class ProjectedAdversarialDefense(AdversarialDefenseWithAttacks): Examples: >>> from mindspore.nn.optim.momentum import Momentum + >>> import mindspore.ops.operations as P >>> from mindarmour.adv_robustness.defenses import ProjectedAdversarialDefense - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._softmax = P.Softmax() + ... self._dense = nn.Dense(10, 10) + ... self._squeeze = P.Squeeze(1) + ... def construct(self, inputs): + ... out = self._softmax(inputs) + ... out = self._dense(out) + ... out = self._squeeze(out) + ... return out >>> net = Net() >>> lr = 0.001 >>> momentum = 0.9 - >>> batch_size = 32 + >>> batch_size = 16 >>> num_classes = 10 >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False) >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum) >>> pad = ProjectedAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer) - >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32) - >>> labels = np.random.randint(num_classes, size=batch_size).astype(np.int32) + >>> inputs = np.random.rand(batch_size, 1, 10).astype(np.float32) + >>> labels = np.random.randint(10, size=batch_size).astype(np.int32) >>> labels = np.eye(num_classes)[labels].astype(np.float32) >>> loss = pad.defense(inputs, labels) """ diff --git a/mindarmour/adv_robustness/detectors/ensemble_detector.py b/mindarmour/adv_robustness/detectors/ensemble_detector.py index 3a42e55..7dcf36f 100644 --- a/mindarmour/adv_robustness/detectors/ensemble_detector.py +++ b/mindarmour/adv_robustness/detectors/ensemble_detector.py @@ -36,12 +36,11 @@ class EnsembleDetector(Detector): Default: 'vote' Examples: >>> from mindspore.ops.operations import Add - >>> from mindspore.nn import Cell >>> from mindspore import Model >>> from mindarmour.adv_robustness.detectors import ErrorBasedDetector >>> from mindarmour.adv_robustness.detectors import RegionBasedDetector >>> from mindarmour.adv_robustness.detectors import EnsembleDetector - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.add = Add() diff --git a/mindarmour/adv_robustness/detectors/mag_net.py b/mindarmour/adv_robustness/detectors/mag_net.py index f26f583..b4eda97 100644 --- a/mindarmour/adv_robustness/detectors/mag_net.py +++ b/mindarmour/adv_robustness/detectors/mag_net.py @@ -49,10 +49,9 @@ class ErrorBasedDetector(Detector): Examples: >>> from mindspore.ops.operations import Add - >>> from mindspore.nn import Cell >>> from mindspore import Model >>> from mindarmour.adv_robustness.detectors import ErrorBasedDetector - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.add = Add() diff --git a/mindarmour/adv_robustness/detectors/region_based_detector.py b/mindarmour/adv_robustness/detectors/region_based_detector.py index 91af5f4..57976e9 100644 --- a/mindarmour/adv_robustness/detectors/region_based_detector.py +++ b/mindarmour/adv_robustness/detectors/region_based_detector.py @@ -55,10 +55,9 @@ class RegionBasedDetector(Detector): Examples: >>> from mindspore.ops.operations import Add - >>> from mindspore.nn import Cell >>> from mindspore import Model >>> from mindarmour.adv_robustness.detectors import RegionBasedDetector - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.add = Add() diff --git a/mindarmour/adv_robustness/detectors/spatial_smoothing.py b/mindarmour/adv_robustness/detectors/spatial_smoothing.py index a415612..52ff34e 100644 --- a/mindarmour/adv_robustness/detectors/spatial_smoothing.py +++ b/mindarmour/adv_robustness/detectors/spatial_smoothing.py @@ -54,10 +54,9 @@ class SpatialSmoothing(Detector): Examples: >>> import mindspore.ops.operations as P - >>> from mindspore.nn import Cell >>> from mindspore import Model >>> from mindarmour.adv_robustness.detectors import SpatialSmoothing - >>> class Net(Cell): + >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self._softmax = P.Softmax() diff --git a/mindarmour/privacy/diff_privacy/optimizer/optimizer.py b/mindarmour/privacy/diff_privacy/optimizer/optimizer.py index 92c5d9a..75d81f9 100644 --- a/mindarmour/privacy/diff_privacy/optimizer/optimizer.py +++ b/mindarmour/privacy/diff_privacy/optimizer/optimizer.py @@ -63,7 +63,13 @@ class DPOptimizerClassFactory: Examples: >>> from mindarmour.privacy.diff_privacy import DPOptimizerClassFactory - >>> from tests.ut.python.utils.mock_net import Net + >>> class Net(nn.Cell): + ... def __init__(self): + ... super(Net, self).__init__() + ... self._relu = nn.ReLU() + ... def construct(self, inputs): + ... out = self._relu(inputs) + ... return out >>> network = Net() >>> GaussianSGD = DPOptimizerClassFactory(micro_batches=2) >>> GaussianSGD.set_mechanisms('Gaussian', norm_bound=1.0, initial_noise_multiplier=1.5) @@ -79,9 +85,8 @@ class DPOptimizerClassFactory: def set_mechanisms(self, policy, *args, **kwargs): """ - Get noise mechanism object. Policies can be 'sgd', 'momentum' - or 'adam'. Candidate args and kwargs can be seen in class - NoiseMechanismsFactory of mechanisms.py. + Get noise mechanism object. Policies can be 'Gaussian' or 'AdaGaussian'. + Candidate args and kwargs can be seen in class NoiseMechanismsFactory of mechanisms.py. Args: policy (str): Choose mechanism type. diff --git a/mindarmour/privacy/diff_privacy/train/model.py b/mindarmour/privacy/diff_privacy/train/model.py index 130694a..a3feec7 100644 --- a/mindarmour/privacy/diff_privacy/train/model.py +++ b/mindarmour/privacy/diff_privacy/train/model.py @@ -70,7 +70,8 @@ class DPModel(Model): DPModel is used for constructing a model for differential privacy training. This class is overload mindspore.train.model.Model. - For details, please check `Tutorial `_. + For details, please check `Protecting User Privacy with Differential Privacy Mechanism + `_. Args: micro_batches (int): The number of small batches split from an original diff --git a/mindarmour/privacy/evaluation/membership_inference.py b/mindarmour/privacy/evaluation/membership_inference.py index cc98927..9cd37a4 100644 --- a/mindarmour/privacy/evaluation/membership_inference.py +++ b/mindarmour/privacy/evaluation/membership_inference.py @@ -98,7 +98,8 @@ class MembershipInference: for inferring user's privacy data. It requires loss or logits results of the training samples. (Privacy refers to some sensitive attributes of a single user). - For details, please refer to the `Tutorial `_. + For details, please refer to the `Using Membership Inference to Test Model Security + `_. References: `Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov. Membership Inference Attacks against Machine Learning Models. 2017. diff --git a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py index 910b07b..85f1f4a 100644 --- a/mindarmour/privacy/sup_privacy/mask_monitor/masker.py +++ b/mindarmour/privacy/sup_privacy/mask_monitor/masker.py @@ -27,7 +27,8 @@ TAG = 'suppress masker' class SuppressMasker(Callback): """ Periodicity check suppress privacy function status and toggle suppress operation. - For details, please check `Tutorial `_. + For details, please check `Protecting User Privacy with Suppression Privacy + `_. Args: model (SuppressModel): SuppressModel instance. diff --git a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py index 5812277..684b507 100644 --- a/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py +++ b/mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py @@ -35,7 +35,8 @@ class SuppressPrivacyFactory: """ Factory class of SuppressCtrl mechanisms. - For details, please check `Tutorial `_. + For details, please check `Protecting User Privacy with Suppress Privacy + `_. """ def __init__(self): @@ -118,7 +119,8 @@ class SuppressCtrl(Cell): finding the parameters that should be suppressed, and suppress these parameters permanently. - For details, please check `Tutorial `_. + For details, please check `Protecting User Privacy with Suppress Privacy + `_. Args: networks (Cell): The training network. diff --git a/mindarmour/privacy/sup_privacy/train/model.py b/mindarmour/privacy/sup_privacy/train/model.py index f10a6cf..01e8a9a 100644 --- a/mindarmour/privacy/sup_privacy/train/model.py +++ b/mindarmour/privacy/sup_privacy/train/model.py @@ -59,7 +59,8 @@ class SuppressModel(Model): Complete model train function. The suppress privacy function is embedded into the overload mindspore.train.model.Model. - For details, please check `Tutorial `_. + For details, please check `Protecting User Privacy with Suppress Privacy + `_. Args: network (Cell): The training network. diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_images.py b/mindarmour/reliability/concept_drift/concept_drift_check_images.py index eaed220..36fabd8 100644 --- a/mindarmour/reliability/concept_drift/concept_drift_check_images.py +++ b/mindarmour/reliability/concept_drift/concept_drift_check_images.py @@ -89,7 +89,8 @@ class OodDetectorFeatureCluster(OodDetector): the testing data features and the clustering centers determines whether an image is an out-of-distribution(OOD) image or not. - For details, please check `Tutorial `_. + For details, please check `Implementing the Concept Drift Detection Application of Image Data + `_. Args: model (Model):The training model. diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py index c7ab194..2e10d55 100644 --- a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py +++ b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py @@ -23,7 +23,8 @@ from mindarmour.utils._check_param import check_param_type, check_param_in_range class ConceptDriftCheckTimeSeries: r""" ConceptDriftCheckTimeSeries is used for example series distribution change detection. - For details, please check `Tutorial `_. + For details, please check `Implementing the Concept Drift Detection Application of Time Series Data + `_. Args: window_size(int): Size of a concept window, no less than 10. If given the input data, diff --git a/mindarmour/reliability/model_fault_injection/fault_injection.py b/mindarmour/reliability/model_fault_injection/fault_injection.py index e98efc2..380885a 100644 --- a/mindarmour/reliability/model_fault_injection/fault_injection.py +++ b/mindarmour/reliability/model_fault_injection/fault_injection.py @@ -31,7 +31,8 @@ class FaultInjector: Fault injection module simulates various fault scenarios for deep neural networks and evaluates performance and reliability of the model. - For details, please check `Tutorial `_. + For details, please check `Implementing the Model Fault Injection and Evaluation + `_. Args: model (Model): The model need to be evaluated.