Function Differences with torch.nn.init.normal_

torch.nn.init.normal_

torch.nn.init.normal_(tensor, mean=0.0, std=1.0)

For more information, see torch.nn.init.normal_.

mindspore.common.initializer.Normal

mindspore.common.initializer.Normal(sigma=0.01, mean=0.0)

For more information, see mindspore.common.initializer.Normal.

Differences

PyTorch: Obtain values N(std, mean) from the normal distribution. Default: std=1., mean=0.0.

MindSpore: Obtain values N(sigma, mean) from the normal distribution. Default: sigma=0.01, mean=0.0.

Code Example

The following code will generate random results.

import mindspore
from mindspore.common.initializer import Normal, initializer

w = initializer(Normal(sigma=1, mean=0.0), shape=[3, 4], dtype=mindspore.float32)
print(w)

# out
# [[ 1.154151   -2.0898762  -0.652796    1.4034489 ]
# [-1.415637    1.717648   -0.6167477  -1.2566634 ]
# [ 3.330741    0.49453223  1.9247946  -0.49406782]]

import torch
from torch import nn

w = nn.init.normal_(torch.empty(3, 4), mean=0., std=1.)
print(w)
# out
# tensor([[ 0.0305, -1.1593,  1.0516, -1.0172],
#         [-0.1539,  0.0793,  0.9397, -0.1186],
#         [ 2.6214,  0.5601,  0.7149, -0.4375]])