lucid.zeros¶

lucid.zeros(*shape: int, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor¶
lucid.zeros(shape: list[int] | tuple[int], /, dtype: type[bool | int | float | complex] | Numeric | None = None, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor

The zeros function creates a tensor filled with zeros of the specified shape and data type.

Function Signature¶

def zeros(
    shape: _ShapeLike,
    dtype: Any = np.float32,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters¶

shape (_ShapeLike):
The shape of the output tensor. Can be a list or tuple of integers.
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 filled with zeros, having the specified shape and data type.

Example¶

>>> import lucid
>>> z = lucid.zeros((2, 3))
>>> print(z)
Tensor([[0. 0. 0.]
        [0. 0. 0.]])

The zeros function also allows specifying requires_grad:

>>> z = lucid.zeros((2, 3), requires_grad=True)
>>> print(z.requires_grad)
True

Note

This function is often used for 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.