nn.functional.dropout3d¶

lucid.nn.functional.dropout3d(input_: Tensor, p: float = 0.5, training: bool = True) → Tensor¶

The dropout3d function applies a 3D dropout regularization operation to the input tensor.

It randomly zeroes out entire channels with a specified probability during training, helping to prevent overfitting in 3D data such as volumetric data or video frames.

Function Signature¶

def dropout3d(
    input_: Tensor,
    p: float = 0.5,
    training: bool = True
) -> Tensor

Parameters¶

input_ (Tensor):
The input tensor of shape (N, C, D, H, W), where N is the batch size, C is the number of channels, D is the depth, H is the height, and W is the width.
p (float, optional):
Probability of an element to be zeroed out. Default: 0.5.
training (bool, optional):
If True, apply dropout. If False, return the input unchanged. Default: True.

Returns¶

Tensor:
The output tensor after applying dropout, with the same shape as the input.

Examples¶

Applying 3D dropout in training mode:

>>> import lucid.nn.functional as F
>>> input_ = Tensor([[[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]]])  # Shape: (1, 1, 2, 2, 2)
>>> out = F.dropout3d(input_, p=0.5, training=True)
>>> print(out)
Tensor([...])  # Randomly zeroed-out channels