diff --git a/mindspore/nn/layer/basic.py b/mindspore/nn/layer/basic.py
index 5c7ce17d8cd..dc17bec715b 100644
--- a/mindspore/nn/layer/basic.py
+++ b/mindspore/nn/layer/basic.py
@@ -75,11 +75,12 @@ class Dropout(Cell):
     Examples:
         >>> x = Tensor(np.ones([2, 2, 3]), mindspore.float32)
         >>> net = nn.Dropout(keep_prob=0.8)
+        >>> net.set_train()
         >>> net(x)
-        [[[1.0, 1.0, 1.0],
-          [1.0, 1.0, 1.0]],
-         [[1.0, 1.0, 1.0],
-          [1.0, 1.0, 1.0]]]
+        [[[0., 1.25, 0.],
+          [1.25, 1.25, 1.25]],
+         [[1.25, 1.25, 1.25],
+          [1.25, 1.25, 1.25]]]
     """
 
     def __init__(self, keep_prob=0.5, dtype=mstype.float32):
@@ -287,7 +288,8 @@ class ClipByNorm(Cell):
         >>> net = nn.ClipByNorm()
         >>> input = Tensor(np.random.randint(0, 10, [4, 16]), mindspore.float32)
         >>> clip_norm = Tensor(np.array([100]).astype(np.float32))
-        >>> net(input, clip_norm)
+        >>> net(input, clip_norm).shape
+        (4, 16)
 
     """
 
diff --git a/mindspore/nn/layer/image.py b/mindspore/nn/layer/image.py
index 5f68413d4a4..ba7d7d6f4a0 100644
--- a/mindspore/nn/layer/image.py
+++ b/mindspore/nn/layer/image.py
@@ -447,6 +447,8 @@ class CentralCrop(Cell):
         >>> net = nn.CentralCrop(central_fraction=0.5)
         >>> image = Tensor(np.random.random((4, 3, 4, 4)), mindspore.float32)
         >>> output = net(image)
+        >>> output.shape
+        (4, 3, 2, 2)
     """
 
     def __init__(self, central_fraction):
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 956d4a669e1..5138bbd074e 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1941,7 +1941,7 @@ class Slice(PrimitiveWithInfer):
     """
     Slices a tensor in the specified shape.
 
-    Args:
+    Inputs:
         x (Tensor): The target tensor.
         begin (tuple): The beginning of the slice. Only constant value is allowed.
         size (tuple): The size of the slice. Only constant value is allowed.
@@ -2262,8 +2262,8 @@ class StridedSlice(PrimitiveWithInfer):
         validator.check_value_type("strides", strides_v, [tuple], self.name)
 
         if tuple(filter(lambda x: not isinstance(x, int), begin_v + end_v + strides_v)):
-            raise ValueError(f"For {self.name}, both the begins, ends, and strides must be a tuple of int, "
-                             f"but got begins: {begin_v}, ends: {end_v}, strides: {strides_v}.")
+            raise TypeError(f"For {self.name}, both the begins, ends, and strides must be a tuple of int, "
+                            f"but got begins: {begin_v}, ends: {end_v}, strides: {strides_v}.")
 
         if tuple(filter(lambda x: x == 0, strides_v)):
             raise ValueError(f"For '{self.name}', the strides cannot contain 0, but got strides: {strides_v}.")
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 7ae10528dbd..97323cbeb11 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -724,11 +724,29 @@ class BatchMatMul(MatMul):
         >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
         >>> batmatmul = P.BatchMatMul()
         >>> output = batmatmul(input_x, input_y)
+        [[[[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]]
+
+         [[[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]]]
         >>>
         >>> input_x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32)
         >>> input_y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
         >>> batmatmul = P.BatchMatMul(transpose_a=True)
         >>> output = batmatmul(input_x, input_y)
+        [[[[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]]
+
+         [[[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]
+          [[3. 3. 3. 3.]]]]
     """
 
     @prim_attr_register