Differences with torch.distributions.laplace.Laplace
torch.distributions.laplace.Laplace
torch.distributions.laplace.Laplace(loc, scale) -> Class Instance
For more information, see torch.distributions.laplace.Laplace.
mindspore.ops.standard_laplace
mindspore.ops.standard_laplace(shape, seed=None) -> Tensor
For more information, see mindspore.ops.standard_laplace.
Differences
PyTorch: Create a Laplace distribution instance and call the sample interface of the instance to generate random values that match the Laplace distribution.
MindSpore: Generates random numbers that match the standard Laplace (mean=0, lambda=1) distribution. When loc=0, scale=1 in PyTorch and the sample function input shape is the same as MindSpore, the two APIs achieve the same function.
Categories |
Subcategories |
PyTorch |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
loc |
- |
MindSpore does not have this parameter and implements loc=0 in PyTorch by default |
Parameter 2 |
scale |
- |
MindSpore does not have this parameter and implements scale=1 in PyTorch by default |
|
Parameter 3 |
- |
shape |
This parameter in PyTorch is passed in when the sample interface is called |
|
Parameter 4 |
- |
seed |
Random seeds for the operator layer. PyTorch does not have this parameter |
Code Example
Each randomly generated value in PyTorch occupies one dimension, so the innermost layer of the shape passed in MindSpore adds a dimension of length 1, and the two APIs achieve the same function.
# PyTorch
import torch
m = torch.distributions.laplace.Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
shape = (4, 4)
sample = m.sample(shape)
print(tuple(sample.shape))
# (4, 4, 1)
# MindSpore
import mindspore
from mindspore import ops
shape = (4, 4, 1)
output = ops.standard_laplace(shape)
result = output.shape
print(result)
# (4, 4, 1)