diff --git a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst b/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst
deleted file mode 100644
index 5a61bf24248..00000000000
--- a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.UncertaintyEvaluation.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-mindspore.nn.probability.toolbox.UncertaintyEvaluation
-======================================================
-
-.. py:class:: mindspore.nn.probability.toolbox.UncertaintyEvaluation(model, train_dataset, task_type, num_classes=None, epochs=1, epi_uncer_model_path=None, ale_uncer_model_path=None, save_model=False)
-
-    包含数据不确定性和模型不确定性的评估工具箱。
-
-    参数：
-        - **model** (Cell) - 不确定性评估的模型。
-        - **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。
-        - **task_type** (str) - 模型任务类型的选项。
-          - regression：回归模型。
-          - classification：分类模型。
-        - **num_classes** (int) - 分类标签的数量。如果任务类型为分类，则必须设置；否则，它是不需要的。默认值：None。
-        - **epochs** (int) - 数据的迭代总数。默认值：1。
-        - **epi_uncer_model_path** (str) - 认知不确定性模型的保存或读取路径。默认值：None。
-        - **ale_uncer_model_path** (str) - 任意不确定性模型的保存或读取路径。默认值：None。
-        - **save_model** (bool) - 是否保存不确定性模型，如果为 true，`epi_uncer_model_path` 和 `ale_uncer_model_path` 不能为 None。
-          如果为 false，则从不确定性模型的路径中加载要评估的模型；如果未给出路径，则不会保存或加载不确定性模型。默认值：false。
-
-    .. py:method:: eval_aleatoric_uncertainty(eval_data)
-
-        评估推理结果的任意不确定性，也称为数据不确定性。
-
-        参数：
-            - **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。
-
-        返回：            
-            numpy.dtype，数据样本推断结果的任意不确定性。
-
-    .. py:method:: eval_epistemic_uncertainty(eval_data)
-
-        评估推理结果的认知不确定性，也称为模型不确定性。
-
-        参数：
-            - **eval_data** (Tensor) - 要评估的数据样本，shape 必须是 (N,C,H,W)。
-
-        返回：            
-            numpy.dtype，数据样本推断结果的任意不确定性。
\ No newline at end of file
diff --git a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst b/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst
deleted file mode 100644
index a74572669fc..00000000000
--- a/docs/api/api_python/probability/mindspore.nn.probability.toolbox.VAEAnomalyDetection.rst
+++ /dev/null
@@ -1,46 +0,0 @@
-mindspore.nn.probability.toolbox.VAEAnomalyDetection
-====================================================
-
-.. py:class:: mindspore.nn.probability.toolbox.VAEAnomalyDetection(encoder, decoder, hidden_size=400, latent_size=20)
-
-    使用 VAE 进行异常检测的工具箱。
-
-    变分自动编码器（VAE）可用于无监督异常检测。异常分数是 sample_x 与重建 sample_x 之间的误差。如果分数高，则 X 大多是异常值。
-
-    参数：
-        - **encoder** (Cell) - 定义为编码器的深度神经网络 (DNN) 模型。
-        - **decoder** (Cell) - 定义为解码器的深度神经网络 (DNN) 模型。
-        - **hidden_size** (int) - 编码器输出 Tensor 的大小。默认值：400。
-        - **latent_size** (int) - 潜在空间的大小。默认值：20。
-
-    .. py:method:: predict_outlier(sample_x, threshold=100.0)
-
-        预测样本是否为异常值。
-
-        参数：
-            - **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。
-            - **threshold** (float) - 异常值的阈值。默认值：100.0。
-
-        返回：
-            bool，样本是否为异常值。
-
-    .. py:method:: predict_outlier_score(sample_x)
-
-        预测异常值分数。
-
-        参数：
-            - **sample_x** (Tensor) - 待预测的样本，shape 为 (N, C, H, W)。
-
-        返回：
-            float，样本的预测异常值分数。
-
-    .. py:method:: train(train_dataset, epochs=5)
-
-        训练 VAE 模型。
-
-        参数：
-            - **train_dataset** (Dataset) - 用于训练模型的数据集迭代器。
-            - **epochs** (int) - 数据的迭代总数。默认值：5。
-
-        返回：               
-            Cell，训练完的模型。
\ No newline at end of file
diff --git a/mindspore/python/mindspore/nn/probability/toolbox/__init__.py b/mindspore/python/mindspore/nn/probability/toolbox/__init__.py
deleted file mode 100644
index 867a00a7245..00000000000
--- a/mindspore/python/mindspore/nn/probability/toolbox/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-Uncertainty toolbox.
-"""
-
-from .uncertainty_evaluation import UncertaintyEvaluation
-from .anomaly_detection import VAEAnomalyDetection
-
-__all__ = ['UncertaintyEvaluation', 'VAEAnomalyDetection']
diff --git a/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py b/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py
deleted file mode 100644
index b58885d18c6..00000000000
--- a/mindspore/python/mindspore/nn/probability/toolbox/anomaly_detection.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Toolbox for anomaly detection by using VAE."""
-import numpy as np
-
-from mindspore._checkparam import Validator
-from mindspore.common.tensor import Tensor
-from ..dpn import VAE
-from ..infer import ELBO, SVI
-from ...optim import Adam
-from ...wrap.cell_wrapper import WithLossCell
-
-
-class VAEAnomalyDetection:
-    r"""
-    Toolbox for anomaly detection by using VAE.
-
-    Variational Auto-Encoder(VAE) can be used for Unsupervised Anomaly Detection. The anomaly score is the error
-    between the X and the reconstruction of X. If the score is high, the X is mostly outlier.
-
-    Args:
-        encoder(Cell): The Deep Neural Network (DNN) model defined as encoder.
-        decoder(Cell): The DNN model defined as decoder.
-        hidden_size(int): The size of encoder's output tensor. Default: 400.
-        latent_size(int): The size of the latent space. Default: 20.
-
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
-    """
-
-    def __init__(self, encoder, decoder, hidden_size=400, latent_size=20):
-        self.vae = VAE(encoder, decoder, hidden_size, latent_size)
-
-    def train(self, train_dataset, epochs=5):
-        """
-        Train the VAE model.
-
-        Args:
-            train_dataset (Dataset): A dataset iterator to train model.
-            epochs (int): Total number of iterations on the data. Default: 5.
-
-        Returns:
-            Cell, the trained model.
-        """
-        net_loss = ELBO()
-        optimizer = Adam(params=self.vae.trainable_params(), learning_rate=0.001)
-        net_with_loss = WithLossCell(self.vae, net_loss)
-        vi = SVI(net_with_loss=net_with_loss, optimizer=optimizer)
-        self.vae = vi.run(train_dataset, epochs)
-        return self.vae
-
-    def predict_outlier_score(self, sample_x):
-        """
-        Predict the outlier score.
-
-        Args:
-            sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
-
-        Returns:
-            float, the predicted outlier score of the sample.
-        """
-        if not isinstance(sample_x, Tensor):
-            raise TypeError("The sample_x must be Tensor type.")
-        reconstructed_sample = self.vae.reconstruct_sample(sample_x)
-        return self._calculate_euclidean_distance(sample_x.asnumpy(), reconstructed_sample.asnumpy())
-
-    def predict_outlier(self, sample_x, threshold=100.0):
-        """
-        Predict whether the sample is an outlier.
-
-        Args:
-            sample_x (Tensor): The sample to be predicted, the shape is (N, C, H, W).
-            threshold (float): the threshold of the outlier. Default: 100.0.
-
-        Returns:
-            Bool, whether the sample is an outlier.
-        """
-        threshold = Validator.check_positive_float(threshold)
-        score = self.predict_outlier_score(sample_x)
-        return score >= threshold
-
-    def _calculate_euclidean_distance(self, sample_x, reconstructed_sample):
-        """
-        Calculate the euclidean distance of the sample_x and reconstructed_sample.
-        """
-        return np.sqrt(np.sum(np.square(sample_x - reconstructed_sample)))
diff --git a/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py b/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
deleted file mode 100644
index 91ad1fe24f2..00000000000
--- a/mindspore/python/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# Copyright 2020-2021 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Toolbox for Uncertainty Evaluation."""
-from copy import deepcopy
-
-import numpy as np
-from mindspore._checkparam import Validator
-from mindspore.ops import composite as C
-from mindspore.ops import operations as P
-from mindspore.train import Model
-from mindspore.train.callback import LossMonitor, ModelCheckpoint, CheckpointConfig
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.common import dtype as mstype
-
-from ...cell import Cell
-from ...layer.basic import Dense, Flatten, Dropout
-from ...layer.container import SequentialCell
-from ...layer.conv import Conv2d
-from ...loss import SoftmaxCrossEntropyWithLogits, MSELoss
-from ...metrics import Accuracy, MSE
-from ...optim import Adam
-
-
-class UncertaintyEvaluation:
-    r"""
-    Toolbox for Uncertainty Evaluation.
-
-    Args:
-        model (Cell): The model for uncertainty evaluation.
-        train_dataset (Dataset): A dataset iterator to train model.
-        task_type (str): Option for the task types of model
-
-            - regression: A regression model.
-            - classification: A classification model.
-
-        num_classes (int): The number of labels of classification.
-                      If the task type is classification, it must be set; otherwise, it is not needed.
-                      Default: None.
-        epochs (int): Total number of iterations on the data. Default: 1.
-        epi_uncer_model_path (str): The save or read path of the epistemic uncertainty model. Default: None.
-        ale_uncer_model_path (str): The save or read path of the aleatoric uncertainty model. Default: None.
-        save_model (bool): Whether to save the uncertainty model or not, if true, the epi_uncer_model_path
-                        and ale_uncer_model_path must not be None. If false, the model to evaluate will be loaded from
-                        the the path of the uncertainty model; if the path is not given , it will not save or load the
-                        uncertainty model. Default: False.
-
-    Supported Platforms:
-        ``Ascend`` ``GPU``
-
-    Examples:
-        >>> network = LeNet()
-        >>> ds_train = create_dataset('workspace/mnist/train')   # handle train data
-        >>> ds_eval = create_dataset('workspace/mnist/test')   # handle test data
-        >>> evaluation = UncertaintyEvaluation(model=network,
-        ...                                    train_dataset=ds_train,
-        ...                                    task_type='classification',
-        ...                                    num_classes=10,
-        ...                                    epochs=1,
-        ...                                    epi_uncer_model_path=None,
-        ...                                    ale_uncer_model_path=None,
-        ...                                    save_model=False)
-        >>> for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1):
-        ...    eval_data = Tensor(eval_data['image'], mstype.float32)
-        ...    epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data)
-        ...    aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data)
-        >>> output = epistemic_uncertainty.shape
-        >>> print(output)
-        (32, 10)
-        >>> output = aleatoric_uncertainty.shape
-        >>> print(output)
-        (32,)
-    """
-
-    def __init__(self, model, train_dataset, task_type, num_classes=None, epochs=1,
-                 epi_uncer_model_path=None, ale_uncer_model_path=None, save_model=False):
-        self.epi_model = deepcopy(model)
-        self.ale_model = deepcopy(model)
-        self.epi_train_dataset = train_dataset
-        self.ale_train_dataset = deepcopy(train_dataset)
-        self.task_type = task_type
-        self.epochs = Validator.check_positive_int(epochs)
-        self.epi_uncer_model_path = epi_uncer_model_path
-        self.ale_uncer_model_path = ale_uncer_model_path
-        self.save_model = Validator.check_bool(save_model)
-        self.epi_uncer_model = None
-        self.ale_uncer_model = None
-        self.concat = P.Concat(axis=0)
-        self.sum = P.ReduceSum()
-        self.pow = P.Pow()
-        if not isinstance(model, Cell):
-            raise TypeError('The model must be Cell type.')
-        if task_type not in ('regression', 'classification'):
-            raise ValueError(
-                'The task should be regression or classification.')
-        if task_type == 'classification':
-            self.num_classes = Validator.check_positive_int(num_classes)
-        else:
-            self.num_classes = num_classes
-        if save_model:
-            if epi_uncer_model_path is None or ale_uncer_model_path is None:
-                raise ValueError("If save_model is True, the epi_uncer_model_path and "
-                                 "ale_uncer_model_path should not be None.")
-
-    def _get_epistemic_uncertainty_model(self):
-        """
-        Get the model which can obtain the epistemic uncertainty.
-        """
-        if self.epi_uncer_model is None:
-            self.epi_uncer_model = EpistemicUncertaintyModel(self.epi_model)
-            if self.epi_uncer_model.drop_count == 0 and self.epi_train_dataset is not None:
-                if self.task_type == 'classification':
-                    net_loss = SoftmaxCrossEntropyWithLogits(
-                        sparse=True, reduction="mean")
-                    net_opt = Adam(self.epi_uncer_model.trainable_params())
-                    model = Model(self.epi_uncer_model, net_loss,
-                                  net_opt, metrics={"Accuracy": Accuracy()})
-                else:
-                    net_loss = MSELoss()
-                    net_opt = Adam(self.epi_uncer_model.trainable_params())
-                    model = Model(self.epi_uncer_model, net_loss,
-                                  net_opt, metrics={"MSE": MSE()})
-                if self.save_model:
-                    config_ck = CheckpointConfig(
-                        keep_checkpoint_max=self.epochs)
-                    ckpoint_cb = ModelCheckpoint(prefix='checkpoint_epi_uncer_model',
-                                                 directory=self.epi_uncer_model_path,
-                                                 config=config_ck)
-                    model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False,
-                                callbacks=[ckpoint_cb, LossMonitor()])
-                elif self.epi_uncer_model_path is None:
-                    model.train(self.epochs, self.epi_train_dataset, dataset_sink_mode=False,
-                                callbacks=[LossMonitor()])
-                else:
-                    uncer_param_dict = load_checkpoint(
-                        self.epi_uncer_model_path)
-                    load_param_into_net(self.epi_uncer_model, uncer_param_dict)
-
-    def _eval_epistemic_uncertainty(self, eval_data, mc=10):
-        """
-        Evaluate the epistemic uncertainty of classification and regression models using MC dropout.
-        """
-        self._get_epistemic_uncertainty_model()
-        self.epi_uncer_model.set_train(True)
-        outputs = [None] * mc
-        for i in range(mc):
-            pred = self.epi_uncer_model(eval_data)
-            outputs[i] = pred.asnumpy()
-        if self.task_type == 'classification':
-            outputs = np.stack(outputs, axis=2)
-            epi_uncertainty = outputs.var(axis=2)
-        else:
-            outputs = np.stack(outputs, axis=1)
-            epi_uncertainty = outputs.var(axis=1)
-        epi_uncertainty = np.array(epi_uncertainty)
-        return epi_uncertainty
-
-    def _get_aleatoric_uncertainty_model(self):
-        """
-        Get the model which can obtain the aleatoric uncertainty.
-        """
-        if self.ale_train_dataset is None:
-            raise ValueError(
-                'The train dataset should not be None when evaluating aleatoric uncertainty.')
-        if self.ale_uncer_model is None:
-            self.ale_uncer_model = AleatoricUncertaintyModel(
-                self.ale_model, self.num_classes, self.task_type)
-            net_loss = AleatoricLoss(self.task_type)
-            net_opt = Adam(self.ale_uncer_model.trainable_params())
-            if self.task_type == 'classification':
-                model = Model(self.ale_uncer_model, net_loss,
-                              net_opt, metrics={"Accuracy": Accuracy()})
-            else:
-                model = Model(self.ale_uncer_model, net_loss,
-                              net_opt, metrics={"MSE": MSE()})
-            if self.save_model:
-                config_ck = CheckpointConfig(keep_checkpoint_max=self.epochs)
-                ckpoint_cb = ModelCheckpoint(prefix='checkpoint_ale_uncer_model',
-                                             directory=self.ale_uncer_model_path,
-                                             config=config_ck)
-                model.train(self.epochs, self.ale_train_dataset, dataset_sink_mode=False,
-                            callbacks=[ckpoint_cb, LossMonitor()])
-            elif self.ale_uncer_model_path is None:
-                model.train(self.epochs, self.ale_train_dataset, dataset_sink_mode=False,
-                            callbacks=[LossMonitor()])
-            else:
-                uncer_param_dict = load_checkpoint(self.ale_uncer_model_path)
-                load_param_into_net(self.ale_uncer_model, uncer_param_dict)
-
-    def _eval_aleatoric_uncertainty(self, eval_data):
-        """
-        Evaluate the aleatoric uncertainty of classification and regression models.
-        """
-        self._get_aleatoric_uncertainty_model()
-        _, var = self.ale_uncer_model(eval_data)
-        ale_uncertainty = self.sum(self.pow(var, 2), 1)
-        ale_uncertainty = ale_uncertainty.asnumpy()
-        return ale_uncertainty
-
-    def eval_epistemic_uncertainty(self, eval_data):
-        """
-        Evaluate the epistemic uncertainty of inference results, which also called model uncertainty.
-
-        Args:
-            eval_data (Tensor): The data samples to be evaluated, the shape must be (N,C,H,W).
-
-        Returns:
-            numpy.dtype, the epistemic uncertainty of inference results of data samples.
-        """
-        uncertainty = self._eval_epistemic_uncertainty(eval_data)
-        return uncertainty
-
-    def eval_aleatoric_uncertainty(self, eval_data):
-        """
-        Evaluate the aleatoric uncertainty of inference results, which also called data uncertainty.
-
-        Args:
-            eval_data (Tensor): The data samples to be evaluated, the shape must be (N,C,H,W).
-
-        Returns:
-            numpy.dtype, the aleatoric uncertainty of inference results of data samples.
-        """
-        uncertainty = self._eval_aleatoric_uncertainty(eval_data)
-        return uncertainty
-
-
-class EpistemicUncertaintyModel(Cell):
-    """
-    Using dropout during training and eval time which is approximate bayesian inference. In this way,
-    we can obtain the epistemic uncertainty (also called model uncertainty).
-
-    If the original model has Dropout layer, just use dropout when eval time, if not, add dropout layer
-    after Dense layer or Conv layer, then use dropout during train and eval time.
-
-    See more details in `Dropout as a Bayesian Approximation: Representing Model uncertainty in Deep Learning
-    <https://arxiv.org/abs/1506.02142>`_.
-    """
-
-    def __init__(self, epi_model):
-        super(EpistemicUncertaintyModel, self).__init__()
-        self.drop_count = 0
-        if not self._make_epistemic(epi_model):
-            raise ValueError("The model has not Dense Layer or Convolution Layer, "
-                             "it can not evaluate epistemic uncertainty so far.")
-        self.epi_model = self._make_epistemic(epi_model)
-
-    def construct(self, x):
-        x = self.epi_model(x)
-        return x
-
-    def _make_epistemic(self, epi_model, keep_prob=0.5):
-        """
-        The dropout rate is set to 0.5 by default.
-        """
-        for (name, layer) in epi_model.name_cells().items():
-            if isinstance(layer, (Conv2d, Dense, Dropout)):
-                if isinstance(layer, Dropout):
-                    self.drop_count += 1
-                    return epi_model
-                uncertainty_layer = layer
-                uncertainty_name = name
-                drop = Dropout(keep_prob=keep_prob)
-                bnn_drop = SequentialCell([uncertainty_layer, drop])
-                setattr(epi_model, uncertainty_name, bnn_drop)
-                return epi_model
-            if self._make_epistemic(layer):
-                return epi_model
-        return None
-
-
-class AleatoricUncertaintyModel(Cell):
-    """
-    The aleatoric uncertainty (also called data uncertainty) is caused by input data, to obtain this
-    uncertainty, the loss function must be modified in order to add variance into loss.
-
-    See more details in `What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
-    <https://arxiv.org/abs/1703.04977>`_.
-    """
-
-    def __init__(self, ale_model, num_classes, task):
-        super(AleatoricUncertaintyModel, self).__init__()
-        self.task = task
-        if task == 'classification':
-            self.ale_model = ale_model
-            self.var_layer = Dense(num_classes, num_classes)
-        else:
-            self.ale_model, self.var_layer, self.pred_layer = self._make_aleatoric(
-                ale_model)
-
-    def construct(self, x):
-        if self.task == 'classification':
-            pred = self.ale_model(x)
-            var = self.var_layer(pred)
-        else:
-            x = self.ale_model(x)
-            pred = self.pred_layer(x)
-            var = self.var_layer(x)
-        return pred, var
-
-    def _make_aleatoric(self, ale_model):
-        """
-        In order to add variance into original loss, add var Layer after the original network.
-        """
-        dense_layer = dense_name = None
-        for (name, layer) in ale_model.name_cells().items():
-            if isinstance(layer, Dense):
-                dense_layer = layer
-                dense_name = name
-        if dense_layer is None:
-            raise ValueError("The model has not Dense Layer, "
-                             "it can not evaluate aleatoric uncertainty so far.")
-        setattr(ale_model, dense_name, Flatten())
-        var_layer = Dense(dense_layer.in_channels, dense_layer.out_channels)
-        return ale_model, var_layer, dense_layer
-
-
-class AleatoricLoss(Cell):
-    """
-    The loss function of aleatoric model, different modification methods are adopted for
-    classification and regression.
-    """
-
-    def __init__(self, task):
-        super(AleatoricLoss, self).__init__()
-        self.task = task
-        if self.task == 'classification':
-            self.sum = P.ReduceSum()
-            self.exp = P.Exp()
-            self.normal = C.normal
-            self.to_tensor = P.ScalarToTensor()
-            self.entropy = SoftmaxCrossEntropyWithLogits(
-                sparse=True, reduction="mean")
-        else:
-            self.mean = P.ReduceMean()
-            self.exp = P.Exp()
-            self.pow = P.Pow()
-
-    def construct(self, data_pred, y):
-        y_pred, var = data_pred
-        if self.task == 'classification':
-            sample_times = 10
-            epsilon = self.normal((1, sample_times), self.to_tensor(0.0, mstype.float32),
-                                  self.to_tensor(1.0, mstype.float32), 0)
-            total_loss = 0
-            for i in range(sample_times):
-                y_pred_i = y_pred + epsilon[0][i] * var
-                loss = self.entropy(y_pred_i, y)
-                total_loss += loss
-            avg_loss = total_loss / sample_times
-            return avg_loss
-        loss = self.mean(0.5 * self.exp(-var) *
-                         self.pow(y - y_pred, 2) + 0.5 * var)
-        return loss
diff --git a/tests/st/probability/toolbox/test_uncertainty.py b/tests/st/probability/toolbox/test_uncertainty.py
deleted file mode 100644
index b5e0cb1ff6c..00000000000
--- a/tests/st/probability/toolbox/test_uncertainty.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright 2020-2022 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-""" test uncertainty toolbox """
-import mindspore.dataset as ds
-import mindspore.dataset.transforms as C
-import mindspore.dataset.vision as CV
-import mindspore.nn as nn
-from mindspore import context, Tensor
-from mindspore import dtype as mstype
-from mindspore.common.initializer import TruncatedNormal
-from mindspore.dataset.vision import Inter
-from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
-from mindspore.train import load_checkpoint, load_param_into_net
-
-context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
-
-def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
-    """weight initial for conv layer"""
-    weight = weight_variable()
-    return nn.Conv2d(in_channels, out_channels,
-                     kernel_size=kernel_size, stride=stride, padding=padding,
-                     weight_init=weight, has_bias=False, pad_mode="valid")
-
-
-def fc_with_initialize(input_channels, out_channels):
-    """weight initial for fc layer"""
-    weight = weight_variable()
-    bias = weight_variable()
-    return nn.Dense(input_channels, out_channels, weight, bias)
-
-
-def weight_variable():
-    """weight initial"""
-    return TruncatedNormal(0.02)
-
-
-class LeNet5(nn.Cell):
-    def __init__(self, num_class=10, channel=1):
-        super(LeNet5, self).__init__()
-        self.num_class = num_class
-        self.conv1 = conv(channel, 6, 5)
-        self.conv2 = conv(6, 16, 5)
-        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
-        self.fc2 = fc_with_initialize(120, 84)
-        self.fc3 = fc_with_initialize(84, self.num_class)
-        self.relu = nn.ReLU()
-        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.flatten = nn.Flatten()
-
-    def construct(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.conv2(x)
-        x = self.relu(x)
-        x = self.max_pool2d(x)
-        x = self.flatten(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.relu(x)
-        x = self.fc3(x)
-        return x
-
-
-def create_dataset(data_path, batch_size=32, repeat_size=1,
-                   num_parallel_workers=1):
-    """
-    create dataset for train or test
-    """
-    # define dataset
-    mnist_ds = ds.MnistDataset(data_path)
-
-    resize_height, resize_width = 32, 32
-    rescale = 1.0 / 255.0
-    shift = 0.0
-    rescale_nml = 1 / 0.3081
-    shift_nml = -1 * 0.1307 / 0.3081
-
-    # define map operations
-    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # Bilinear mode
-    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
-    rescale_op = CV.Rescale(rescale, shift)
-    hwc2chw_op = CV.HWC2CHW()
-    type_cast_op = C.TypeCast(mstype.int32)
-
-    # apply map operations on images
-    mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
-    mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
-
-    # apply DatasetOps
-    buffer_size = 10000
-    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 10000 as in LeNet train script
-    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
-    mnist_ds = mnist_ds.repeat(repeat_size)
-
-    return mnist_ds
-
-
-if __name__ == '__main__':
-    # get trained model
-    network = LeNet5()
-    param_dict = load_checkpoint('checkpoint_lenet.ckpt')
-    load_param_into_net(network, param_dict)
-    # get train and eval dataset
-    ds_train = create_dataset('workspace/mnist/train')
-    ds_eval = create_dataset('workspace/mnist/test')
-    evaluation = UncertaintyEvaluation(model=network,
-                                       train_dataset=ds_train,
-                                       task_type='classification',
-                                       num_classes=10,
-                                       epochs=1,
-                                       epi_uncer_model_path=None,
-                                       ale_uncer_model_path=None,
-                                       save_model=False)
-    for eval_data in ds_eval.create_dict_iterator(output_numpy=True, num_epochs=1):
-        eval_data = Tensor(eval_data['image'], mstype.float32)
-        epistemic_uncertainty = evaluation.eval_epistemic_uncertainty(eval_data)
-        aleatoric_uncertainty = evaluation.eval_aleatoric_uncertainty(eval_data)