diff --git a/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py b/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
index 06f6a01370..073bd80985 100644
--- a/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
+++ b/mindspore/nn/probability/bnn_layers/bnn_cell_wrapper.py
@@ -43,8 +43,7 @@ class WithBNNLossCell(Cell):
     Examples:
         >>> net = Net()
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
-        >>> net_with_criterion_object = WithBNNLossCell(net, loss_fn)
-        >>> net_with_criterion = net_with_criterion_object()
+        >>> net_with_criterion = WithBNNLossCell(net, loss_fn)
         >>>
         >>> batch_size = 2
         >>> data = Tensor(np.ones([batch_size, 3, 64, 64]).astype(np.float32) * 0.01)
diff --git a/mindspore/nn/probability/transforms/transform_bnn.py b/mindspore/nn/probability/transforms/transform_bnn.py
index 0d1a08de08..eca6aec1b2 100644
--- a/mindspore/nn/probability/transforms/transform_bnn.py
+++ b/mindspore/nn/probability/transforms/transform_bnn.py
@@ -58,7 +58,7 @@ class TransformToBNN:
         >>> net = Net()
         >>> criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
         >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
-        >>> net_with_loss = WithLossCell(network, criterion)
+        >>> net_with_loss = WithLossCell(net, criterion)
         >>> train_network = TrainOneStepCell(net_with_loss, optim)
         >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.0001)
     """
@@ -115,7 +115,7 @@ class TransformToBNN:
             >>> net = Net()
             >>> criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
             >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
-            >>> net_with_loss = WithLossCell(network, criterion)
+            >>> net_with_loss = WithLossCell(net, criterion)
             >>> train_network = TrainOneStepCell(net_with_loss, optim)
             >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.1)
             >>> train_bnn_network = bnn_transformer.transform_to_bnn_model()
@@ -160,7 +160,7 @@ class TransformToBNN:
             >>> net = Net()
             >>> criterion = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
             >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
-            >>> net_with_loss = WithLossCell(network, criterion)
+            >>> net_with_loss = WithLossCell(net, criterion)
             >>> train_network = TrainOneStepCell(net_with_loss, optim)
             >>> bnn_transformer = TransformToBNN(train_network, 60000, 0.1)
             >>> train_bnn_network = bnn_transformer.transform_to_bnn_layer(Dense, DenseReparam)