mygrad.subtract

class mygrad.subtract(x1: ArrayLike, x2: ArrayLike, out: Tensor | ndarray | None = None, *, where: Mask = True, dtype: DTypeLikeReals = None, constant: bool | None = None)

Subtract the arguments element-wise.

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

Parameters:

x1, x2ArrayLike

The arrays to be subtracted from each other. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output). Non-tensor array-likes are treated as constants.

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.

dtypeOptional[DTypeLikeReals]

The dtype of the resulting tensor.

outOptional[Union[ndarray, Tensor]]

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.

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.

Returns:

subtractTensor

The difference of x1 and x2, element-wise.

Notes

Equivalent to x1 - x2 in terms of tensor broadcasting.

References

[1]

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

Examples

>>> import mygrad as mg
>>> mg.subtract(1.0, 4.0)
Tensor(-3.0)

>>> x1 = mg.tensor([[0., 1., 2.],
...                 [3., 4., 5.],
...                 [6., 7., 8.]])
>>> x2 = mg.tensor([0., 1., 2.])
>>> mg.subtract(x1, x2)
Tensor([[ 0.,  0.,  0.],
        [ 3.,  3.,  3.],
        [ 6.,  6.,  6.]])

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