diff --git a/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py b/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
index 3aef08133e271a32252bc8540a16103bb4e78a52..38e88646d0d5511298402d52f26890c5c339b863 100644
--- a/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
+++ b/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
@@ -65,9 +65,9 @@ class WithBNNLossCell:
     """
 
     def __init__(self, backbone, loss_fn, dnn_factor=1, bnn_factor=1):
-        if not isinstance(dnn_factor, (int, float)):
+        if type(dnn_factor) not in [int, float]:
             raise TypeError('The type of `dnn_factor` should be `int` or `float`')
-        if not isinstance(bnn_factor, (int, float)):
+        if type(bnn_factor) not in [int, float]:
             raise TypeError('The type of `bnn_factor` should be `int` or `float`')
 
         self.backbone = backbone
diff --git a/mindspore/nn/probability/bnn_layers/conv_variational.py b/mindspore/nn/probability/bnn_layers/conv_variational.py
index 6389ea9b2faeb2934c3a8af5fade435b095345cc..3e85b879d88145de437c1210ac20b1cd1d1fe97f 100644
--- a/mindspore/nn/probability/bnn_layers/conv_variational.py
+++ b/mindspore/nn/probability/bnn_layers/conv_variational.py
@@ -179,7 +179,7 @@ class ConvReparam(_ConvVariational):
     Args:
         in_channels (int): The number of input channel :math:`C_{in}`.
         out_channels (int): The number of output channel :math:`C_{out}`.
-            kernel_size (Union[int, tuple[int]]): The data type is int or
+        kernel_size (Union[int, tuple[int]]): The data type is int or
             tuple with 2 integers. Specifies the height and width of the 2D
             convolution window. Single int means the value if for both
             height and width of the kernel. A tuple of 2 ints means the
diff --git a/mindspore/nn/probability/bnn_layers/layer_distribution.py b/mindspore/nn/probability/bnn_layers/layer_distribution.py
index 262af94403226345cc8ba5d31860267f4fc1c35d..f43f628793623c0174a2dc7f241b528d0645058f 100644
--- a/mindspore/nn/probability/bnn_layers/layer_distribution.py
+++ b/mindspore/nn/probability/bnn_layers/layer_distribution.py
@@ -75,19 +75,19 @@ class NormalPosterior(Cell):
         if not isinstance(name, str):
             raise TypeError('The type of `name` should be `str`')
 
-        if not isinstance(shape, (tuple, list)):
+        if type(shape) not in [tuple, list]:
             raise TypeError('The type of `shape` should be `tuple` or `list`')
 
-        if not isinstance(loc_mean, (int, float)):
+        if type(loc_mean) not in [int, float]:
             raise TypeError('The type of `loc_mean` should be `int` or `float`')
 
-        if not isinstance(untransformed_scale_mean, (int, float)):
+        if type(untransformed_scale_mean) not in [int, float]:
             raise TypeError('The type of `untransformed_scale_mean` should be `int` or `float`')
 
-        if not (isinstance(loc_std, (int, float)) and loc_std >= 0):
+        if not (type(loc_std) in [int, float] and loc_std >= 0):
             raise TypeError('The type of `loc_std` should be `int` or `float` and its value should > 0')
 
-        if not (isinstance(untransformed_scale_std, (int, float)) and untransformed_scale_std >= 0):
+        if not (type(untransformed_scale_std) in [int, float] and untransformed_scale_std >= 0):
             raise TypeError('The type of `untransformed_scale_std` should be `int` or `float` and '
                             'its value should > 0')
 
diff --git a/mindspore/nn/probability/transforms/transform_bnn.py b/mindspore/nn/probability/transforms/transform_bnn.py
index 3853c48b2395d20a254afacb3b752dd308bc3b50..49e8f7777346b2734eb5cd38c5cd33c9a5223d66 100644
--- a/mindspore/nn/probability/transforms/transform_bnn.py
+++ b/mindspore/nn/probability/transforms/transform_bnn.py
@@ -61,9 +61,9 @@ class TransformToBNN:
     """
 
     def __init__(self, trainable_dnn, dnn_factor=1, bnn_factor=1):
-        if not isinstance(dnn_factor, (int, float)):
+        if type(dnn_factor) not in [int, float]:
             raise TypeError('The type of `dnn_factor` should be `int` or `float`')
-        if not isinstance(bnn_factor, (int, float)):
+        if type(bnn_factor) not in [int, float]:
             raise TypeError('The type of `bnn_factor` should be `int` or `float`')
 
         net_with_loss = trainable_dnn.network
diff --git a/tests/st/probability/dataset.py b/tests/st/probability/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..cef69734839d1efeefefcc9dc31cd620c451917a
--- /dev/null
+++ b/tests/st/probability/dataset.py
@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+"""
+Produce the dataset
+"""
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.common import dtype as mstype
+
+
+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
diff --git a/tests/st/probability/test_bnn_layer.py b/tests/st/probability/test_bnn_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b135d0bf085630fff3145b58ae5211fcd7ec2772
--- /dev/null
+++ b/tests/st/probability/test_bnn_layer.py
@@ -0,0 +1,145 @@
+# 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 bnn layers"""
+
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import TrainOneStepCell
+from mindspore.nn.probability import bnn_layers
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, 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 BNNLeNet5(nn.Cell):
+    """
+    bayesian Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> BNNLeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(BNNLeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = bnn_layers.ConvReparam(1, 6, 5, stride=1, padding=0, has_bias=False, pad_mode="valid")
+        self.conv2 = conv(6, 16, 5)
+        self.fc1 = bnn_layers.DenseReparam(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()
+        self.reshape = P.Reshape()
+
+    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 train_model(train_net, net, dataset):
+    accs = []
+    loss_sum = 0
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        loss = train_net(train_x, label)
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+        loss_sum += loss.asnumpy()
+
+    loss_sum = loss_sum / len(accs)
+    acc_mean = np.mean(accs)
+    return loss_sum, acc_mean
+
+
+def validate_model(net, dataset):
+    accs = []
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+
+    acc_mean = np.mean(accs)
+    return acc_mean
+
+
+if __name__ == "__main__":
+    network = BNNLeNet5()
+
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
+
+    net_with_loss = bnn_layers.WithBNNLossCell(network, criterion, 60000, 0.000001)
+    train_bnn_network = TrainOneStepCell(net_with_loss, optimizer)
+    train_bnn_network.set_train()
+
+    train_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/train', 64, 1)
+    test_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/test', 64, 1)
+
+    epoch = 100
+
+    for i in range(epoch):
+        train_loss, train_acc = train_model(train_bnn_network, network, test_set)
+
+        valid_acc = validate_model(network, test_set)
+
+        print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format(
+            i, train_loss, train_acc, valid_acc))
diff --git a/tests/st/probability/test_transform_bnn_layer.py b/tests/st/probability/test_transform_bnn_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..590fee8e8111f6684916ee3243b0c505dd3d7ce3
--- /dev/null
+++ b/tests/st/probability/test_transform_bnn_layer.py
@@ -0,0 +1,150 @@
+# 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 transform_to_bnn_layer"""
+
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.probability import transforms, bnn_layers
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, 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):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(1, 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()
+        self.reshape = P.Reshape()
+
+    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 train_model(train_net, net, dataset):
+    accs = []
+    loss_sum = 0
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        loss = train_net(train_x, label)
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+        loss_sum += loss.asnumpy()
+
+    loss_sum = loss_sum / len(accs)
+    acc_mean = np.mean(accs)
+    return loss_sum, acc_mean
+
+
+def validate_model(net, dataset):
+    accs = []
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+
+    acc_mean = np.mean(accs)
+    return acc_mean
+
+
+if __name__ == "__main__":
+    network = LeNet5()
+
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
+
+    net_with_loss = WithLossCell(network, criterion)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+
+    bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001)
+    train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Conv2d, bnn_layers.ConvReparam)
+    # train_bnn_network = bnn_transformer.transform_to_bnn_layer(nn.Dense, bnn_layers.DenseReparam)
+    train_bnn_network.set_train()
+
+    train_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/train', 64, 1)
+    test_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/test', 64, 1)
+
+    epoch = 100
+
+    for i in range(epoch):
+        train_loss, train_acc = train_model(train_bnn_network, network, test_set)
+
+        valid_acc = validate_model(network, test_set)
+
+        print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format(
+            i, train_loss, train_acc, valid_acc))
diff --git a/tests/st/probability/test_transform_bnn_model.py b/tests/st/probability/test_transform_bnn_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..015a1f41d761625f1ef1d5c13376c8c02cfa3235
--- /dev/null
+++ b/tests/st/probability/test_transform_bnn_model.py
@@ -0,0 +1,149 @@
+# 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 transform_to_bnn_model"""
+import numpy as np
+from mindspore import Tensor
+from mindspore.common.initializer import TruncatedNormal
+import mindspore.nn as nn
+from mindspore.nn import WithLossCell, TrainOneStepCell
+from mindspore.nn.probability import transforms
+from mindspore.ops import operations as P
+from mindspore import context
+from dataset import create_dataset
+
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, 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):
+    """
+    Lenet network
+
+    Args:
+        num_class (int): Num classes. Default: 10.
+
+    Returns:
+        Tensor, output tensor
+    Examples:
+        >>> LeNet5(num_class=10)
+
+    """
+    def __init__(self, num_class=10):
+        super(LeNet5, self).__init__()
+        self.num_class = num_class
+        self.conv1 = conv(1, 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()
+        self.reshape = P.Reshape()
+
+    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 train_model(train_net, net, dataset):
+    accs = []
+    loss_sum = 0
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        loss = train_net(train_x, label)
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+        loss_sum += loss.asnumpy()
+
+    loss_sum = loss_sum / len(accs)
+    acc_mean = np.mean(accs)
+    return loss_sum, acc_mean
+
+
+def validate_model(net, dataset):
+    accs = []
+    for _, data in enumerate(dataset.create_dict_iterator()):
+        train_x = Tensor(data['image'].astype(np.float32))
+        label = Tensor(data['label'].astype(np.int32))
+        output = net(train_x)
+        log_output = P.LogSoftmax(axis=1)(output)
+        acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
+        accs.append(acc)
+
+    acc_mean = np.mean(accs)
+    return acc_mean
+
+
+if __name__ == "__main__":
+    network = LeNet5()
+
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
+    optimizer = nn.AdamWeightDecay(params=network.trainable_params(), learning_rate=0.0001)
+
+    net_with_loss = WithLossCell(network, criterion)
+    train_network = TrainOneStepCell(net_with_loss, optimizer)
+
+    bnn_transformer = transforms.TransformToBNN(train_network, 60000, 0.000001)
+
+    train_bnn_network = bnn_transformer.transform_to_bnn_model()
+    train_bnn_network.set_train()
+
+    train_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/train', 64, 1)
+    test_set = create_dataset('/home/workspace/mindspore_dataset/mnist_data/test', 64, 1)
+
+    epoch = 500
+
+    for i in range(epoch):
+        train_loss, train_acc = train_model(train_bnn_network, network, test_set)
+
+        valid_acc = validate_model(network, test_set)
+
+        print('Epoch: {} \tTraining Loss: {:.4f} \tTraining Accuracy: {:.4f} \tvalidation Accuracy: {:.4f}'.format(
+            i, train_loss, train_acc, valid_acc))