- paddle. renorm ( x, p, axis, max_norm )
This operator is used to calculate the p-norm along the axis, suppose the input-shape on axis dimension has the value of T, then the tensor is split into T parts, the p-norm should be calculated for each part, if the p-norm for part i is larger than max-norm, then each element in part i should be re-normalized at the same scale so that part-i’ p-norm equals max-norm exactly, otherwise part-i stays unchanged.
x (Tensor) – The input Tensor
p (float) – The power of the norm operation.
axis (int) – the dimension to slice the tensor.
max-norm (float) – the maximal norm limit.
the renorm Tensor.
- Return type
import paddle input = [[[2.0,2,-2],[3,0.3,3]],[[2,-8,2],[3.1,3.7,3]]] x = paddle.to_tensor(input,dtype='float32') y = paddle.renorm(x, 1.0, 2, 2.05) print(y)
# [[[ 0.40594056, 0.29285714, -0.41000000], # [ 0.60891086, 0.04392857, 0.61500001]], # [[ 0.40594056, -1.17142856, 0.41000000], # [ 0.62920785, 0.54178572, 0.61500001]]])