nn.functional.dropout¶
The dropout function randomly zeroes some elements of the input tensor with a probability p during training to prevent overfitting.
Function Signature¶
def dropout(input_: Tensor, p: float = 0.5, training: bool = True) -> Tensor
Parameters¶
- input_ (Tensor):
The input tensor of any shape.
- p (float, optional):
Probability of an element to be zeroed. Default: 0.5.
- training (bool, optional):
Apply dropout if True; do nothing if False. Default: True.
Returns¶
- Tensor:
A tensor of the same shape as the input, with elements randomly zeroed with probability p if training is True.
Examples¶
Applying dropout during training:
>>> import lucid.nn.functional as F
>>> input_ = Tensor([1.0, 2.0, 3.0])
>>> out = F.dropout(input_, p=0.5, training=True)
>>> print(out)
# Example output: Tensor([1.0, 0.0, 3.0])