logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, device=None)

Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

Non-scalar start and stop are now supported.

  • start (int or float) – base ** start is the starting value of the sequence.

  • stop (int or float) – 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 (integer, optional) – Number of samples to generate. Default is 50.

  • endpoint (boolean, optional) – If true, stop is the last sample. Otherwise, it is not included. Default is True.

  • base (float, optional) – The base of the log space. The step size between the elements in ln(samples) / ln(base) (or log_base(samples)) is uniform. Default is 10.0.

  • dtype (dtype) – The type of the output array. If dtype is not given, infer the data type from the other input arguments.

  • axis (int, optional) – The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Now, axis only support axis = 0.

  • device (Device, optional) – Device context on which the memory is allocated. Default is mxnet.device.current_device().


samplesnum samples, equally spaced on a log scale.

Return type


See also


Similar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included.


Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.


Logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
>>> power(base, y).astype(dtype)


>>> np.logspace(2.0, 3.0, num=4)
array([ 100.     ,  215.44347,  464.15887, 1000.     ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100.     , 177.82794, 316.22775, 562.3413 ])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4.       , 5.0396843, 6.349604 , 8.       ])
>>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
array([4, 5, 6, 8], dtype=int32)
>>> np.logspace(2.0, 3.0, num=4, device=npx.gpu(0))
array([ 100.     ,  215.44347,  464.15887, 1000.     ], device=gpu(0))