!12888 fix doc errors in mindspore.numpy API

From: @yanglf1121
Reviewed-by: @guoqi1024,@liangchenghui
Signed-off-by: @liangchenghui

mindspore/numpy/array_creations.py

@@ -530,11 +530,12 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0):
     start) and ends with base ** stop (see endpoint below).
 
     Args:
-        start (Union[int, list(int), tuple(int), tensor]): The starting value of the sequence.
-        stop (Union[int, list(int), tuple(int), tensor]): The end value of the sequence,
-            unless `endpoint` is set to False. In that case, the sequence consists
-            of all but the last of `num + 1` evenly spaced samples, so that `stop`
-            is excluded.  Note that the step size changes when `endpoint` is False.
+        start (Union[int, list(int), tuple(int), tensor]): ``base ** start`` is the starting
+            value of the sequence.
+        stop (Union[int, list(int), tuple(int), tensor]): ``base ** stop`` is the final value of
+            the sequence, unless `endpoint` is False. In that case, ``num + 1`` values are spaced
+            over the interval in log-space, of which all but the last (a sequence of length num)
+            are returned.
         num (int, optional): Number of samples to generate. Default is 50.
         endpoint (bool, optional): If True, `stop` is the last sample. Otherwise, it is
             not included. Default is True.

mindspore/numpy/array_ops.py

@@ -809,13 +809,13 @@ def atleast_1d(*arys):
         >>> c = np.ones(5)
         >>> output = np.atleast_1d(a, b, c)
         >>> print(output)
-            (Tensor(shape=[2, 3], dtype=Float32, value=
+            [Tensor(shape=[2, 3], dtype=Float32, value=
             [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
             [1.00000000e+000, 1.00000000e+000, 1.00000000e+000]]),
             Tensor(shape=[1], dtype=Float32, value= [1.00000000e+000]),
             Tensor(shape=[5], dtype=Float32,
             value= [1.00000000e+000, 1.00000000e+000, 1.00000000e+000,
-            1.00000000e+000, 1.00000000e+000]))
+            1.00000000e+000, 1.00000000e+000])]
     """
     return _atleast_xd(1, arys)
 
@@ -846,13 +846,13 @@ def atleast_2d(*arys):
         >>> c = np.ones(5)
         >>> output = np.atleast_2d(a, b, c)
         >>> print(output)
-            (Tensor(shape=[2, 3], dtype=Float32, value=
+            [Tensor(shape=[2, 3], dtype=Float32, value=
             [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
             [1.00000000e+000, 1.00000000e+000, 1.00000000e+000]]),
             Tensor(shape=[1, 1], dtype=Float32, value= [[1.00000000e+000]]),
             Tensor(shape=[1, 5], dtype=Float32,
             value= [[1.00000000e+000, 1.00000000e+000, 1.00000000e+000,
-            1.00000000e+000, 1.00000000e+000]]))
+            1.00000000e+000, 1.00000000e+000]])]
     """
     return _atleast_xd(2, arys)
 
@@ -886,13 +886,13 @@ def atleast_3d(*arys):
         >>> c = np.ones(5)
         >>> output = np.atleast_3d(a, b, c)
         >>> print(output)
-            (Tensor(shape=[2, 3, 1], dtype=Float32, value=
+            [Tensor(shape=[2, 3, 1], dtype=Float32, value=
             [[[1.00000000e+000], [1.00000000e+000], [1.00000000e+000]],
             [[1.00000000e+000], [1.00000000e+000], [1.00000000e+000]]]),
             Tensor(shape=[1, 1, 1], dtype=Float32, value= [[[1.00000000e+000]]]),
             Tensor(shape=[1, 5, 1], dtype=Float32,
             value= [[[1.00000000e+000], [1.00000000e+000], [1.00000000e+000],
-            [1.00000000e+000], [1.00000000e+000]]]))
+            [1.00000000e+000], [1.00000000e+000]]])]
     """
     res = []
     for arr in arys:
@@ -1378,7 +1378,7 @@ def split(x, indices_or_sections, axis=0):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> input_x = np.arange(9)
+        >>> input_x = np.arange(9).astype('float32')
         >>> output = np.split(input_x, 3)
         >>> print(output)
         (Tensor(shape=[3], dtype=Float32,
@@ -1455,7 +1455,7 @@ def vsplit(x, indices_or_sections):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> input_x = np.arange(9).reshape((3, 3))
+        >>> input_x = np.arange(9).reshape((3, 3)).astype('float32')
         >>> output = np.vsplit(input_x, 3)
         >>> print(output)
         (Tensor(shape=[1, 3], dtype=Float32,
@@ -1497,7 +1497,7 @@ def hsplit(x, indices_or_sections):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> input_x = np.arange(6).reshape((2, 3))
+        >>> input_x = np.arange(6).reshape((2, 3)).astype('float32')
         >>> output = np.hsplit(input_x, 3)
         >>> print(output)
         (Tensor(shape=[2, 1], dtype=Float32,
@@ -1542,7 +1542,7 @@ def dsplit(x, indices_or_sections):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> input_x = np.arange(6).reshape((1, 2, 3))
+        >>> input_x = np.arange(6).reshape((1, 2, 3)).astype('float32')
         >>> output = np.dsplit(input_x, 3)
         >>> print(output)
         (Tensor(shape=[1, 2, 1], dtype=Float32,

mindspore/numpy/math_ops.py

@@ -1478,10 +1478,10 @@ def log(x, out=None, where=True, dtype=None):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
-        >>> x = np.array([1, 2, 3]).astype('float32')
+        >>> x = np.array([2, 3, 4]).astype('float32')
         >>> output = np.log(x)
         >>> print(output)
-        [1.09861   1.3862929 1.6094407]
+        [0.69314575 1.09861    1.3862929 ]
     """
     return _apply_tensor_op(F.log, x, out=out, where=where, dtype=dtype)
 
@@ -1726,8 +1726,8 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True):
         [0. 1.]
         >>> output = np.amin(a, axis=1)
         >>> print(output)
-        [1. 3.]
-        >>> output = np.amax(a, where=np.array([False, True]), initial=10, axis=0)
+        [0, 2]
+        >>> output = np.amin(a, where=np.array([False, True]), initial=10, axis=0)
         >>> print(output)
         [10.  1.]
     """
@@ -2296,10 +2296,10 @@ def positive(a, out=None, where=True, dtype=None):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> a = np.asarray([1, -1])
+        >>> a = np.asarray([1, -1]).astype('float32')
         >>> output = np.positive(a)
         >>> print(output)
-        [1, -1]
+        [1. -1.]
     """
     _check_input_tensor(a)
     neg_tensor = F.neg_tensor(a)
@@ -2336,10 +2336,10 @@ def negative(a, out=None, where=True, dtype=None):
 
     Examples:
         >>> import mindspore.numpy as np
-        >>> a = np.asarray([1, -1])
+        >>> a = np.asarray([1, -1]).astype('float32')
         >>> output = np.negative(a)
         >>> print(output)
-        [-1, 1]
+        [-1. 1.]
     """
     _check_input_tensor(a)
     return _apply_tensor_op(F.neg_tensor, a, out=out, where=where, dtype=dtype)