mygrad.logspace#

mygrad.logspace(start: ArrayLike, stop: ArrayLike, num: int = 50, endpoint: bool = True, base: int | float = 10, dtype: DTypeLikeReals | None = None, axis: int = 0, *, constant: bool | None = None) → Tensor[source]#

Return a Tensor with evenly-spaced numbers over a specified interval on a log scale. This is not a differentiable function - it does not propagate gradients to its inputs.

In linear space, values are generated within [base**start, base**stop], with the endpoint optionally excluded.

This docstring was adapted from numpy.logspace [1]

Parameters:

startArrayLike

The starting value of the sequence, inclusive; start at base ** start.

stopArrayLike

The ending value of the sequence, inclusive unless include_endpoint is False; end at base ** stop.

numint, optional (default=50)

The number of values to generate. Must be non-negative.

endpointbool, optional (default=True)

Whether to include the endpoint in the Tensor. Note that if False, the step size changes to accommodate the sequence excluding the endpoint.

baseReal, optional (default=10)

The base of the log space.

dtypeOptional[DTypeLikeReals]

The data type of the output Tensor, or None to infer from the inputs.

axisint, optional (default=0)

The axis in the result to store the samples - for array-like start/stop.

constantOptional[bool]

If True, this tensor is a constant, and thus does not facilitate back propagation.

Defaults to False for float-type data. Defaults to True for integer-type data.

Integer-type tensors must be constant.

Returns:

Tensor

See also

arange: 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.
linspace: Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.
geomspace: Similar to logspace, but with endpoints specified directly.

References

[1]

Retrieved from https://numpy.org/doc/stable/reference/generated/numpy.logspace.html

Examples

>>> import mygrad as mg
>>> mg.logspace(2.0, 3.0, num=4)
Tensor([  100.        ,   215.443469  ,   464.15888336,  1000.        ])
>>> mg.logspace(2.0, 3.0, num=4, endpoint=False)
Tensor([ 100.        ,  177.827941  ,  316.22776602,  562.34132519])
>>> mg.logspace(2.0, 3.0, num=4, base=2.0)
Tensor([ 4.        ,  5.0396842 ,  6.34960421,  8.        ])