lucid.arange¶

lucid.arange(stop: int | float | complex, *, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor¶
lucid.arange(start: int | float | complex, stop: int | float | complex, *, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor
lucid.arange(start: int | float | complex, stop: int | float | complex, step: int | float | complex, /, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor

The arange function creates a tensor with evenly spaced values within a specified interval.

Function Signature¶

def arange(
    start: _Scalar = 0.0,
    stop: _Scalar,
    step: _Scalar = 1.0,
    dtype: Any = np.float32,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters¶

start (_Scalar, optional): The starting value of the sequence. Defaults to 0.0 if only stop is provided.
stop (_Scalar): The end value of the sequence (exclusive).
step (_Scalar, optional): The spacing between values. Defaults to 1.0.
dtype (Any, optional): The data type of the elements in the tensor. Defaults to np.float32.
requires_grad (bool, optional): If True, the resulting tensor will be part of the computation graph and capable of tracking gradients. Defaults to False.
keep_grad (bool, optional): If True, the gradient history will be preserved even if the tensor does not require gradients. Defaults to False.

Returns¶

Tensor: A tensor containing evenly spaced values within the specified interval.

Example¶

>>> import lucid
>>> a = lucid.arange(5)
>>> print(a)
Tensor([0. 1. 2. 3. 4.])

>>> a = lucid.arange(2, 5)
>>> print(a)
Tensor([2. 3. 4.])

>>> a = lucid.arange(0, 1, 0.2)
>>> print(a)
Tensor([0.  0.2 0.4 0.6 0.8])

The arange function also allows specifying requires_grad:

>>> a = lucid.arange(0, 5, 1, requires_grad=True)
>>> print(a.requires_grad)
True

Note

This function is often used to generate sequences of numbers for indexing, iteration, or initialization purposes in neural networks and other numerical applications.
The requires_grad parameter is useful for differentiable operations in computation graphs.
If keep_grad is set to True, the tensor will retain its gradient history even if requires_grad is False.