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 Tensor with the same number of elements as input.
When exponent is a scalar value, the operation applied is:
out i = x i exponent \text{out}_i = x_i ^ \text{exponent} outi=xiexponent
When exponent is a tensor, the operation applied is:
out i = x i exponent i \text{out}_i = x_i ^ {\text{exponent}_i} outi=xiexponenti
When exponent is a tensor, the shapes of input and exponent must be broadcastable.
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.])
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:
out i = self exponent i \text{out}_i = \text{self} ^ {\text{exponent}_i} outi=selfexponenti
out (Tensor, optional) – the output tensor.
Example:
>>> exp = torch.arange(1., 5.) >>> base = 2 >>> torch.pow(base, exp) tensor([ 2., 4., 8., 16.])
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4