[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #15390: [Numpy fix-doc]modify numpy doc

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haojin2 commented on a change in pull request #15390: [Numpy  fix-doc]modify numpy doc
URL: https://github.com/apache/incubator-mxnet/pull/15390#discussion_r298929843
 
 

 ##########
 File path: python/mxnet/ndarray/numpy/_op.py
 ##########
 @@ -28,31 +28,65 @@
 __all__ = ['zeros', 'ones', 'maximum', 'minimum', 'stack', 'arange', 'argmax',
            'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'concatenate',
            'clip', 'split', 'swapaxes', 'expand_dims', 'tile', 'linspace',
-           'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt']
+           'sin', 'cos', 'sinh', 'cosh', 'log10', 'sqrt', 'ceil', 'log1p', 'tanh']
 
 
 @set_module('mxnet.ndarray.numpy')
 def zeros(shape, dtype=_np.float32, **kwargs):
     """Return a new array of given shape and type, filled with zeros.
-    This function currently only supports storing multi-dimensional data
-    in row-major (C-style).
 
     Parameters
     ----------
-    shape : int or tuple of int
-        The shape of the empty array.
-    dtype : str or numpy.dtype, optional
-        An optional value type. Default is `numpy.float32`. Note that this
-        behavior is different from NumPy's `ones` function where `float64`
-        is the default value, because `float32` is considered as the default
-        data type in deep learning.
+    shape : int , tuple of ints or list of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., `numpy.int8`.  Default is
+        `numpy.float32`.
     ctx : Context, optional
         An optional device context (default is the current default context).
 
     Returns
     -------
     out : ndarray
-        Array of zeros with the given shape, dtype, and ctx.
+        Array of zeros with the given shape, dtype, and order.
+
+    Notes
+    -----
+    - Not support ndarray type
+    - Not support custom dtype
+
+    >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
+    mxnet.base.MXNetError: Invalid Input.
+
+    See Also
+    --------
+    zeros_like : Return an array of zeros with shape and type of input.
+    ones : Return a new array setting values to one.
+
+    Examples
+    --------
+    >>> np.zeros(5)
+    array([0., 0., 0., 0., 0.])
+
+    >>> np.zeros((5,), dtype=int)
+    array([0, 0, 0, 0, 0])
+
+    >>> np.zeros((2, 1))
+    array([[0.],
+           [0.]])
+
+    >>> s = (2,2)
+    >>> np.zeros(s)
+    array([[0., 0.],
+           [0., 0.]])
+
+    if you install mxnet gpu version, you can use gpu.
 
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