torch.Tensor.Tensor.float_power
Tensor.float_power(exponent: number): Tensor<S, D, Dev>Tensor.float_power<O extends Shape>(exponent: Tensor<O>): Tensor<DynamicShape, D, Dev>Raises this tensor to the power of exponent element-wise, with results always in floating-point.
Computes self^exponent, promoting the result to floating-point. Unlike pow(), float_power always returns a float tensor even for integer inputs. Useful for:
- Exponentiation when float results are guaranteed
- Operations like computing x^0.5 (square root) on integers
- Avoiding integer overflow in power operations
- Consistent floating-point output regardless of input type
- Always returns float tensor, even for integer inputs
- When exponent is a tensor, it must have compatible shape with this tensor
Parameters
exponentnumber- The exponent (scalar or tensor)
Returns
Tensor<S, D, Dev>– Tensor with element-wise self^exponent in floating-pointExamples
const x = torch.tensor([1, 2, 3, 4]);
const y = torch.tensor([2, 2, 2, 2]);
x.float_power(y); // [1.0, 4.0, 9.0, 16.0]
// With scalar exponent
x.float_power(0.5); // [1.0, 1.414..., 1.732..., 2.0]
// Fractional exponents
const bases = torch.tensor([8, 27, 64]);
bases.float_power(1/3); // [2.0, 3.0, 4.0] (cube roots)See Also
- PyTorch torch.float_power()
- pow - Similar operation (may return integers for integer inputs)
- sqrt - Optimized square root