torch.pow — PyTorch 2.7 documentation (original) (raw)

torch.pow(input, exponent, *, out=None) → Tensor¶

Takes the power of each element in input with exponent and returns a tensor with the result.

exponent can be either a single float number or a Tensorwith the same number of elements as input.

When exponent is a scalar value, the operation applied is:

outi=xiexponent\text{out}_i = x_i ^ \text{exponent}

When exponent is a tensor, the operation applied is:

outi=xiexponenti\text{out}_i = x_i ^ {\text{exponent}_i}

When exponent is a tensor, the shapes of inputand exponent must be broadcastable.

Parameters

• input (Tensor) – the input tensor.
• exponent (float or tensor) – the exponent value

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

a = torch.randn(4) a tensor([ 0.4331, 1.2475, 0.6834, -0.2791]) torch.pow(a, 2) tensor([ 0.1875, 1.5561, 0.4670, 0.0779]) exp = torch.arange(1., 5.)

a = torch.arange(1., 5.) a tensor([ 1., 2., 3., 4.]) exp tensor([ 1., 2., 3., 4.]) torch.pow(a, exp) tensor([ 1., 4., 27., 256.])

torch.pow(self, exponent, *, out=None) → Tensor

self is a scalar float value, and exponent is a tensor. The returned tensor out is of the same shape as exponent

The operation applied is:

outi=selfexponenti\text{out}_i = \text{self} ^ {\text{exponent}_i}

Parameters

• self (float) – the scalar base value for the power operation
• exponent (Tensor) – the exponent tensor

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

exp = torch.arange(1., 5.) base = 2 torch.pow(base, exp) tensor([ 2., 4., 8., 16.])