modify api

mindspore/nn/layer/container.py

@@ -149,13 +149,17 @@ class SequentialCell(Cell):
         """Appends a given cell to the end of the list.
 
         Examples:
-        >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid')
-        >>> bn = nn.BatchNorm2d(2)
-        >>> relu = nn.ReLU()
-        >>> seq = nn.SequentialCell([conv, bn])
-        >>> seq.append(relu)
-        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
-        >>> seq(x)
+            >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid')
+            >>> bn = nn.BatchNorm2d(2)
+            >>> relu = nn.ReLU()
+            >>> seq = nn.SequentialCell([conv, bn])
+            >>> seq.append(relu)
+            >>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
+            >>> seq(x)
+            [[[[0.12445523 0.12445523]
+               [0.12445523 0.12445523]]
+              [[0.         0.        ]
+               [0.         0.        ]]]]
         """
         if _valid_cell(cell):
             self._cells[str(len(self))] = cell

mindspore/nn/learning_rate_schedule.py

@@ -61,8 +61,13 @@ class ExponentialDecayLR(LearningRateSchedule):
     .. math::
         decayed\_learning\_rate[i] = learning\_rate * decay\_rate^{p}
 
-    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, the formula
-    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+    Where :
+    .. math::
+        p = \frac{current\_step}{decay\_steps}
+
+    If `is_stair` is True, the formula is :
+    .. math::
+        p = floor(\frac{current\_step}{decay\_steps})
 
     Args:
         learning_rate (float): The initial value of learning rate.
@@ -110,8 +115,13 @@ class NaturalExpDecayLR(LearningRateSchedule):
     .. math::
         decayed\_learning\_rate[i] = learning\_rate * e^{-decay\_rate * p}
 
-    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, the formula
-    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+    Where :
+    .. math::
+        p = \frac{current\_step}{decay\_steps}
+
+    If `is_stair` is True, the formula is :
+    .. math::
+        p = floor(\frac{current\_step}{decay\_steps})
 
     Args:
         learning_rate (float): The initial value of learning rate.
@@ -160,8 +170,13 @@ class InverseDecayLR(LearningRateSchedule):
     .. math::
         decayed\_learning\_rate[i] = learning\_rate / (1 + decay\_rate * p)
 
-    Where :math:`p = \frac{current\_step}{decay\_steps}`, if `is_stair` is True, The formula
-    is :math:`p = floor(\frac{current\_step}{decay\_steps})`.
+    Where :
+    .. math::
+        p = \frac{current\_step}{decay\_steps}
+
+    If `is_stair` is True, The formula is :
+    .. math::
+        p = floor(\frac{current\_step}{decay\_steps})
 
     Args:
         learning_rate (float): The initial value of learning rate.
@@ -264,12 +279,14 @@ class PolynomialDecayLR(LearningRateSchedule):
         (1 - tmp\_step / tmp\_decay\_steps)^{power} + end\_learning\_rate
 
     Where :
-    .. math::
-        `tmp\_step=min(current\_step, decay\_steps).
 
-    If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula is
     .. math::
-        `tmp\_decay\_steps = decay\_steps * ceil(current\_step / decay\_steps)`
+        tmp\_step=min(current\_step, decay\_steps)
+
+    If `update_decay_steps` is true, update the value of `tmp_decay_step` every `decay_steps`. The formula is :
+
+    .. math::
+        tmp\_decay\_steps = decay\_steps * ceil(current\_step / decay\_steps)
 
     Args:
         learning_rate (float): The initial value of learning rate.
@@ -338,7 +355,10 @@ class WarmUpLR(LearningRateSchedule):
     .. math::
         warmup\_learning\_rate[i] = learning\_rate * tmp\_step / warmup\_steps
 
-    Where :math:`tmp\_step=min(current\_step, warmup\_steps)`.
+    Where :
+
+    .. math:
+        tmp\_step=min(current\_step, warmup\_steps)
 
     Args:
         learning_rate (float): The initial value of learning rate.

mindspore/nn/probability/bijector/power_transform.py

@@ -41,8 +41,7 @@ class PowerTransform(Bijector):
         name (str): The name of the bijector. Default: 'PowerTransform'.
         param (dict): The parameters used to initialize the bijector. These parameters are only used when other
             Bijectors inherit from powertransform to pass in parameters. In this case the derived Bijector may overwrite
-            the argument `param`.
-          Default: None.
+            the argument `param`. Default: None.
 
     Examples:
         >>> # To initialize a PowerTransform bijector of power 0.5.

mindspore/ops/operations/math_ops.py

@@ -127,8 +127,8 @@ class TensorAdd(_MathBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is number or bool.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,
-          or a bool when the first input is a tensor, or a tensor whose data type is number or bool.
+        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,  or a bool when the first input
+          is a tensor, or a tensor whose data type is number or bool.
 
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,
@@ -1081,8 +1081,8 @@ class Sub(_MathBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is number or bool.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,
-        or a bool when the first input is a tensor, or a tensor whose data type is number or bool.
+        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number, or a bool when the first input
+          is a tensor, or a tensor whose data type is number or bool.
 
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,
@@ -1159,9 +1159,8 @@ class SquaredDifference(_MathBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is float16, float32, int32 or bool.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,
-          or a bool when the first input is a tensor or a tensor whose data type is
-          float16, float32, int32 or bool.
+        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number, or a bool when the first input
+          is a tensor or a tensor whose data type isfloat16, float32, int32 or bool.
 
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,
@@ -1865,8 +1864,8 @@ class TruncateDiv(_MathBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is number or bool.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,
-          or a bool when the first input is a tensor, or a tensor whose data type is number or bool.
+        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number, or a bool when the first input
+          is a tensor, or a tensor whose data type is number or bool.
 
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,
@@ -1895,8 +1894,8 @@ class TruncateMod(_MathBinaryOp):
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is number or bool.
-        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number,
-          or a bool when the first input is a tensor, or a tensor whose data type is number or bool.
+        - **input_y** (Union[Tensor, Number, bool]) - The second input is a number, or a bool when the first input
+          is a tensor, or a tensor whose data type is number or bool.
 
     Outputs:
         Tensor, the shape is the same as the one after broadcasting,