diff --git a/mindspore/nn/probability/toolbox/__init__.py b/mindspore/nn/probability/toolbox/__init__.py
new file mode 100644
index 0000000000..8391cd9185
--- /dev/null
+++ b/mindspore/nn/probability/toolbox/__init__.py
@@ -0,0 +1,21 @@
+# 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
+
+__all__ = ['UncertaintyEvaluation']
diff --git a/mindspore/nn/probability/toolbox/uncertainty_evaluation.py b/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
new file mode 100644
index 0000000000..173f641a05
--- /dev/null
+++ b/mindspore/nn/probability/toolbox/uncertainty_evaluation.py
@@ -0,0 +1,298 @@
+# 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 Uncertainty Evaluation."""
+import numpy as np
+
+from mindspore._checkparam import check_int_positive
+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
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from ...cell import Cell
+from ...layer.basic import Dense, Flatten, Dropout
+from ...layer.conv import Conv2d
+from ...layer.container import SequentialCell
+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.
+        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; if not classification, it need not to be set.
+                      Default: None.
+        epochs (int): Total number of iterations on the data. Default: None.
+        uncertainty_model_path (str): The save or read path of the uncertainty model.
+
+    Examples:
+        >>> network = LeNet()
+        >>> param_dict = load_checkpoint('checkpoint_lenet.ckpt')
+        >>> load_param_into_net(network, param_dict)
+        >>> ds_train = create_dataset('workspace/mnist/train')
+        >>> evaluation = UncertaintyEvaluation(model=network,
+        >>>                                    train_dataset=ds_train,
+        >>>                                    task_type='classification',
+        >>>                                    num_classes=10,
+        >>>                                    epochs=5,
+        >>>                                    uncertainty_model_path=None)
+        >>> epistemic_uncertainty = evaluation.eval_epistemic(eval_data)
+        >>> aleatoric_uncertainty = evaluation.eval_aleatoric(eval_data)
+    """
+
+    def __init__(self, model, train_dataset, task_type, num_classes=None, epochs=None,
+                 uncertainty_model_path=None):
+        self.model = model
+        self.train_dataset = train_dataset
+        self.task_type = task_type
+        self.num_classes = check_int_positive(num_classes)
+        self.epochs = epochs
+        self.uncer_model_path = uncertainty_model_path
+        self.uncer_model = None
+        self.concat = P.Concat(axis=0)
+        self.sum = P.ReduceSum()
+        self.pow = P.Pow()
+        if self.task_type not in ('regression', 'classification'):
+            raise ValueError('The task should be regression or classification.')
+        if self.task_type == 'classification':
+            if self.num_classes is None:
+                raise ValueError("Classification task needs to input labels.")
+
+    def _uncertainty_normalize(self, data):
+        area = np.max(data) - np.min(data)
+        return (data - np.min(data)) / area
+
+    def _get_epistemic_uncertainty_model(self):
+        """
+        Get the model which can obtain the epistemic uncertainty.
+        """
+        if self.uncer_model and self.uncer_model_path is None:
+            self.uncer_model = EpistemicUncertaintyModel(self.model)
+            if self.uncer_model.drop_count == 0:
+                if self.task_type == 'classification':
+                    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+                    net_opt = Adam(self.uncer_model.trainable_params())
+                    model = Model(self.uncer_model, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+                else:
+                    net_loss = MSELoss()
+                    net_opt = Adam(self.uncer_model.trainable_params())
+                    model = Model(self.uncer_model, net_loss, net_opt, metrics={"MSE": MSE()})
+                model.train(self.epochs, self.train_dataset, callbacks=[LossMonitor()])
+        elif self.uncer_model is None:
+            uncer_param_dict = load_checkpoint(self.uncer_model_path)
+            load_param_into_net(self.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.uncer_model.set_train(True)
+        outputs = [None] * mc
+        for i in range(mc):
+            pred = self.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 = self._uncertainty_normalize(np.array(epi_uncertainty))
+        return epi_uncertainty
+
+    def _get_aleatoric_uncertainty_model(self):
+        """
+        Get the model which can obtain the aleatoric uncertainty.
+        """
+        if self.uncer_model and self.uncer_model_path is None:
+            self.uncer_model = AleatoricUncertaintyModel(self.model, self.num_classes, self.task_type)
+            net_loss = AleatoricLoss(self.task_type)
+            net_opt = Adam(self.uncer_model.trainable_params())
+            if self.task_type == 'classification':
+                model = Model(self.uncer_model, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+            else:
+                model = Model(self.uncer_model, net_loss, net_opt, metrics={"MSE": MSE()})
+            model.train(self.epochs, self.train_dataset, callbacks=[LossMonitor()])
+        elif self.uncer_model is None:
+            uncer_param_dict = load_checkpoint(self.uncer_model_path)
+            load_param_into_net(self.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.uncer_model(eval_data)
+        ale_uncertainty = self.sum(self.pow(var, 2), 1)
+        ale_uncertainty = self._uncertainty_normalize(ale_uncertainty.asnumpy())
+        return ale_uncertainty
+
+    def eval_epistemic(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 should be (N,C,H,W).
+
+        Returns:
+            numpy.dtype, the epistemic uncertainty of inference results of data samples.
+        """
+        uncertainty = self._eval_aleatoric_uncertainty(eval_data)
+        return uncertainty
+
+    def eval_aleatoric(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 should be (N,C,H,W).
+
+        Returns:
+            numpy.dtype, the aleatoric uncertainty of inference results of data samples.
+        """
+        uncertainty = self._eval_epistemic_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, model):
+        super(EpistemicUncertaintyModel, self).__init__()
+        self.drop_count = 0
+        self.model = self._make_epistemic(model)
+
+    def construct(self, x):
+        x = self.model(x)
+        return x
+
+    def _make_epistemic(self, model, dropout_rate=0.5):
+        """
+        The dropout rate is set to 0.5 by default.
+        """
+        for (name, layer) in model.name_cells().items():
+            if isinstance(layer, Dropout):
+                self.drop_count += 1
+                return model
+        for (name, layer) in model.name_cells().items():
+            if isinstance(layer, (Conv2d, Dense)):
+                uncertainty_layer = layer
+                uncertainty_name = name
+                drop = Dropout(keep_prob=dropout_rate)
+                bnn_drop = SequentialCell([uncertainty_layer, drop])
+                setattr(model, uncertainty_name, bnn_drop)
+                return model
+        raise ValueError("The model has not Dense Layer or Convolution Layer, "
+                         "it can not evaluate epistemic uncertainty so far.")
+
+
+class AleatoricUncertaintyModel(Cell):
+    """
+    The aleatoric uncertainty (also called data uncertainty) is caused by input data, to obtain this
+    uncertainty, the loss function should 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, model, labels, task):
+        super(AleatoricUncertaintyModel, self).__init__()
+        self.task = task
+        if task == 'classification':
+            self.model = model
+            self.var_layer = Dense(labels, labels)
+        else:
+            self.model, self.var_layer, self.pred_layer = self._make_aleatoric(model)
+
+    def construct(self, x):
+        if self.task == 'classification':
+            pred = self.model(x)
+            var = self.var_layer(pred)
+        else:
+            x = self.model(x)
+            pred = self.pred_layer(x)
+            var = self.var_layer(x)
+        return pred, var
+
+    def _make_aleatoric(self, 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 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(model, dense_name, Flatten())
+        var_layer = Dense(dense_layer.in_channels, dense_layer.out_channels)
+        return 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.ScalarToArray()
+            self.entropy = SoftmaxCrossEntropyWithLogits(is_grad=False, 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), self.to_tensor(1.0), 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/test_uncertainty.py b/tests/st/probability/test_uncertainty.py
new file mode 100644
index 0000000000..1037669f92
--- /dev/null
+++ b/tests/st/probability/test_uncertainty.py
@@ -0,0 +1,133 @@
+# 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.
+# ============================================================================
+""" test uncertainty toolbox """
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as C
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.nn as nn
+from mindspore import context, Tensor
+from mindspore.common import dtype as mstype
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
+from mindspore.train.serialization 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(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, 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=5,
+                                       uncertainty_model_path=None)
+    for eval_data in ds_eval.create_dict_iterator():
+        eval_data = Tensor(eval_data['image'], mstype.float32)
+        epistemic_uncertainty = evaluation.eval_epistemic(eval_data)
+        aleatoric_uncertainty = evaluation.eval_aleatoric(eval_data)