mygrad.log1p

class mygrad.log1p(x1: ArrayLike, out: Tensor | ndarray | None = None, *, where: Mask = True, dtype: DTypeLikeReals = None, constant: bool | None = None)

Return the natural logarithm of one plus the input tensor, element-wise.

Calculates log(1 + x).

This docstring was adapted from that of numpy.log1p [1]

Parameters:

x1ArrayLike

Input values.

outOptional[Union[Tensor, ndarray]]

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned.

constantOptional[bool]

If True, this tensor is treated as a constant, and thus does not facilitate back propagation (i.e. constant.grad will always return None).

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

Integer-type tensors must be constant.

whereMask

This condition is broadcast over the input. At locations where the condition is True, the out tensor will be set to the ufunc result. Elsewhere, the out tensor will retain its original value. Note that if an uninitialized out tensor is created via the default out=None, locations within it where the condition is False will remain uninitialized.

dtypeOptional[DTypeLikeReals]

The dtype of the resulting tensor.

Returns:

log1pTensor

f(x1) computed element-wise

See also

expm1

exp(x) - 1, the inverse of log1p.

Notes

For real-valued input, log1p is accurate also for x so small that 1 + x == 1 in floating-point accuracy.

For real-valued input data types, log1p always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag.

References

[1]

Retrieved from https://numpy.org/doc/stable/reference/generated/numpy.log1p.html

Examples

>>> import mygrad as mg
>>> mg.log1p(1e-99)
1e-99
>>> mg.log(1 + 1e-99)
0.0

Attributes:

identity

signature

Methods

accumulate([axis, dtype, out, constant])	Not implemented
at(indices[, b, constant])	Not implemented
outer(b, *[, dtype, out])	Not Implemented
reduce([axis, dtype, out, keepdims, ...])	Not Implemented
reduceat(indices[, axis, dtype, out])	Not Implemented
resolve_dtypes(dtypes, *[, signature, ...])	Find the dtypes NumPy will use for the operation.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
accumulate([axis, dtype, out, constant])	Not implemented
at(indices[, b, constant])	Not implemented
outer(b, *[, dtype, out])	Not Implemented
reduce([axis, dtype, out, keepdims, ...])	Not Implemented
reduceat(indices[, axis, dtype, out])	Not Implemented
resolve_dtypes(dtypes, *[, signature, ...])	Find the dtypes NumPy will use for the operation.

Attributes

identity
nargs
nin
nout
ntypes
signature
types