modify format

docs/api/api_python/nn/mindspore.nn.TripletMarginLoss.rst

@@ -1,7 +1,7 @@
 mindspore.nn.TripletMarginLoss
 ===============================
 
-.. py:class:: class TripletMarginLoss(p=2, swap=False, eps=1e-06, reduction='mean')
+.. py:class:: mindspore.nn.TripletMarginLoss(p=2, swap=False, eps=1e-06, reduction='mean')
 
     执行三元组损失函数的操作。
 

docs/api/api_python/ops/mindspore.ops.func_dropout1d.rst

@@ -22,5 +22,5 @@ mindspore.ops.dropout1d
     异常：
         - **TypeError** - `x` 不是Tensor。
         - **TypeError** - `p` 的数据类型不是float。
-        - **ValueError** - `p` 值不在 `[0.0，1.0]` 之间。
+        - **ValueError** - `p` 值不在 `[0.0, 1.0]` 之间。
         - **ValueError** - `x` 的维度不是 `2D` 或 `3D`。

mindspore/python/mindspore/dataset/engine/datasets.py

@@ -590,7 +590,7 @@ class Dataset:
             >>> # and drops the last incomplete batch if there is one.
             >>> dataset = dataset.batch(100, True)
             >>>
-            >>> # 2）resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
+            >>> # 2) resize image according to its batch number, if it's 5-th batch, resize to (5^2, 5^2) = (25, 25)
             >>> def np_resize(col, BatchInfo):
             ...     output = col.copy()
             ...     s = (BatchInfo.get_batch_num() + 1) ** 2
@@ -603,7 +603,7 @@ class Dataset:
             ...     return (output,)
             >>> dataset = dataset.batch(batch_size=8, input_columns=["image"], per_batch_map=np_resize)
             >>>
-            >>> # 3）Create a dataset where its batch size is dynamic
+            >>> # 3) Create a dataset where its batch size is dynamic
             >>> # Define a callable batch size function and let batch size increase 1 each time.
             >>> def add_one(BatchInfo):
             ...     return BatchInfo.get_batch_num() + 1
@@ -654,7 +654,7 @@ class Dataset:
             >>> # and drops the last incomplete batch if there is one.
             >>> dataset = dataset.padded_batch(100, True)
             >>>
-            >>> # 3）Create a dataset where its batch size is dynamic
+            >>> # 3) Create a dataset where its batch size is dynamic
             >>> # Define a callable batch size function and let batch size increase 1 each time.
             >>> def add_one(BatchInfo):
             ...     return BatchInfo.get_batch_num() + 1

mindspore/python/mindspore/ops/function/array_func.py

@@ -471,7 +471,7 @@ def ravel(x):
     Args:
         x (Tensor): A tensor to be flattened.
 
-    Outputs:
+    Returns:
         Tensor, a 1-D tensor, containing the same elements of the input.
 
     Raises:

mindspore/python/mindspore/ops/function/nn_func.py

@@ -1172,7 +1172,6 @@ def dropout1d(x, p=0.5, training=True):
 
     Raises:
         TypeError: If `x` is not a Tensor.
-        TypeError: If dtype of `x` is not int8, int16, int32, int64, float16, float32 or float64.
         TypeError: If the data type of `p` is not float.
         ValueError: If `p` is out of the range `[0.0, 1.0]`.
         ValueError: If `x` shape is not `2D` or `3D`.