mygrad.arange#

mygrad.arange([start, ]stop, [step, ]dtype=None, *, constant=None)[source]#

Return a Tensor with evenly-spaced values within a given interval.

Values are generated within [start, stop). Note that for non-integer steps, results may be inconsistent; you are better off using linspace instead.

This docstring was adapted from numpy.arange [1]

Parameters:

startReal, optional, default=0

The start of the interval, inclusive.

stopReal

The end of the interval, exclusive.

stepint, optional (default=1)

The spacing between successive values.

dtypeOptional[DTypeLikeReals]

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

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: A Tensor of evenly-spaced values in [start, end).

References

[1]

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

Examples

>>> import mygrad as mg
>>> mg.arange(3)
Tensor([0, 1, 2])
>>> mg.arange(3.0, constant=True)  # resulting tensor will not back-propagate a gradient
Tensor([ 0.,  1.,  2.])
>>> mg.arange(3,7)
Tensor([3, 4, 5, 6])
>>> mg.arange(3,7,2)
Tensor([3, 5])