ndarray¶

ones_like¶

格式:

mxnet.ndarray.ones_like(data=None, out=None, name=None, **kwargs)

说明:

Return an array of ones with the same shape and type as the input array.

实例:

x = [[ 0.,  0.,  0.],
     [ 0.,  0.,  0.]]

ones_like(x) = [[ 1.,  1.,  1.],
                [ 1.,  1.,  1.]]

ones¶

格式:

mxnet.ndarray.ones(shape, ctx=None, dtype=None, **kwargs)

说明:

Returns a new array filled with all ones, with the given shape and type.

实例:

>>> mx.nd.ones(1).asnumpy()
array([ 1.], dtype=float32)
>>> mx.nd.ones((1,2), mx.gpu(0))
<NDArray 1x2 @gpu(0)>
>>> mx.nd.ones((1,2), dtype='float16').asnumpy()
array([[ 1.,  1.]], dtype=float16)
>>> a = mx.nd.ones(shape=10)
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
<NDArray 10 @cpu(0)>

zeros¶

格式:

mxnet.ndarray.zeros(shape, ctx=None, dtype=None, stype=None, **kwargs)

说明:

Return a new array of given shape and type, filled with zeros.

Parameters:

shape (int or tuple of int)
    – The shape of the empty array
ctx (Context, optional)
    – An optional device context (default is the current default context)
dtype (str or numpy.dtype, optional)
    – An optional value type (default is float32)
stype (string, optional)
    – The storage type of the empty array, 如:‘row_sparse’, ‘csr’, etc.

实例:

>>> mx.nd.zeros((1,2), mx.cpu(), stype='csr')
<CSRNDArray 1x2 @cpu(0)>
>>> mx.nd.zeros((1,2), mx.cpu(), 'float16', stype='row_sparse').asnumpy()
array([[ 0.,  0.]], dtype=float16)