Differences with torch.nn.init.uniform_
torch.nn.init.uniform_
torch.nn.init.uniform_(
tensor,
a=0.0,
b=1.0
) -> Tensor
For more information, see torch.nn.init.uniform_.
mindspore.ops.uniform
mindspore.ops.uniform(shape, minval, maxval, seed=None, dtype=mstype.float32) -> Tensor
For more information, see mindspore.ops.uniform.
Differences
PyTorch: The upper and lower bounds of uniform distribution are specified by parameters a
and b
, i.e. U(-a, b).
MindSpore: The upper and lower bounds of uniform distribution are specified by parameters minval
and maxval
, i.e. U(minval, maxval),seed
is used for random number generator to generate pseudorandom numbers.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
tensor |
shape |
The function is similar, but the input format is not. |
Parameter 2 |
a |
minval |
They have different names and similar functions, which are used to specify the minimum value of random values generated. |
|
Parameter 3 |
b |
maxval |
They have different names and similar functions, which are used to specify the maximum value of random values generated. |
|
Parameter 4 |
- |
seed |
Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. |
|
Parameter 5 |
- |
dtype |
Specify the type of input data, and determine whether the data generated by uniform distribution is discrete or continuous according to the data type. |
Code Example
# PyTorch
import torch
from torch import nn
w = torch.empty(3, 2)
output = nn.init.uniform_(w, a=1, b=4)
print(tuple(output.shape))
# (3, 2)
# MindSpore
import numpy as np
import mindspore
from mindspore import ops
from mindspore import Tensor
shape = (3,2)
minval = Tensor(1, mindspore.float32)
maxval = Tensor(4, mindspore.float32)
output = ops.uniform(shape, minval, maxval, dtype=mindspore.float32)
print(output.shape)
# Out:
# (3, 2)