From c421fc1badd7a48291d2ca4c0a16ab2139a20de4 Mon Sep 17 00:00:00 2001
From: lihongkang <[lihongkang1@huawei.com]>
Date: Fri, 25 Sep 2020 10:20:33 +0800
Subject: [PATCH] fix bugs

---
 mindspore/nn/layer/activation.py      | 4 +++-
 mindspore/ops/operations/array_ops.py | 5 +++--
 mindspore/ops/operations/math_ops.py  | 2 +-
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py
index 11205b064c..d30d3c2a0a 100644
--- a/mindspore/nn/layer/activation.py
+++ b/mindspore/nn/layer/activation.py
@@ -22,6 +22,7 @@ from mindspore.common.tensor import Tensor
 from mindspore._extends import cell_attr_register
 from mindspore.ops import _selected_ops
 from ..cell import Cell
+from ..._checkparam import Validator as validator
 
 
 __all__ = ['Softmax',
@@ -228,7 +229,7 @@ class LeakyReLU(Cell):
     See https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf
 
     Args:
-        alpha (float): Slope of the activation function at x < 0. Default: 0.2.
+        alpha (Union[int, float]): Slope of the activation function at x < 0. Default: 0.2.
 
     Inputs:
         - **input_x** (Tensor) - The input of LeakyReLU.
@@ -246,6 +247,7 @@ class LeakyReLU(Cell):
 
     def __init__(self, alpha=0.2):
         super(LeakyReLU, self).__init__()
+        validator.check_value_type('alpha', alpha, [float, int], self.cls_name)
         self.greater_equal = P.GreaterEqual()
         self.mul = P.Mul()
         self.alpha = alpha
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index be7e0ccb77..9c8fcce4eb 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1185,6 +1185,7 @@ class Argmax(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32)
         >>> index = P.Argmax(output_type=mindspore.int32)(input_x)
+        1
     """
 
     @prim_attr_register
@@ -1192,7 +1193,7 @@ class Argmax(PrimitiveWithInfer):
         """Initialize Argmax"""
         self.init_prim_io_names(inputs=['x'], outputs=['output'])
         validator.check_value_type("axis", axis, [int], self.name)
-        validator.check_type_same({'output': output_type}, [mstype.int32, mstype.int64], self.name)
+        validator.check_type_same({'output': output_type}, [mstype.int32], self.name)
         self.axis = axis
         self.add_prim_attr('output_type', output_type)
 
@@ -1996,7 +1997,7 @@ class Select(PrimitiveWithInfer):
     and :math:`y`.
 
     Inputs:
-        - **input_x** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N)`.
+        - **input_x** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The condition tensor, decides which element is chosen.
         - **input_y** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The first input tensor.
diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py
index 2d6dbc8b75..335c8c68b8 100644
--- a/mindspore/ops/operations/math_ops.py
+++ b/mindspore/ops/operations/math_ops.py
@@ -404,7 +404,7 @@ class ReduceAll(_Reduce):
 
         - If axis is (), and keep_dims is False,
           the output is a 0-D tensor representing the "logical and" of all elements in the input tensor.
-        - If axis is int, set as 2, and keep_dims is alse,
+        - If axis is int, set as 2, and keep_dims is False,
           the shape of output is :math:`(x_1, x_3, ..., x_R)`.
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.