sigmoid¶

nvtripy.sigmoid(input: Tensor) → Tensor[source]¶

Applies a logistic sigmoid function to each element of the input tensor:

\(\text{sigmoid}(x)_i = \frac{1}{1 + \exp{-x_i}}\)

Parameters:: input (Tensor) – [dtype=T1] The input tensor.
Returns:: [dtype=T1] A tensor of the same shape as the input.
Return type:: Tensor

TYPE CONSTRAINTS:

T1: float16, bfloat16, float32

Example

input = tp.Tensor([1.0, 2.0, 3.0, 4.0], dtype=tp.float32)
output = tp.sigmoid(input)

Local Variables¶

>>> input
tensor([1.0000, 2.0000, 3.0000, 4.0000], dtype=float32, loc=gpu:0, shape=(4,))

>>> output
tensor([0.7311, 0.8808, 0.9526, 0.9820], dtype=float32, loc=gpu:0, shape=(4,))