比较与torch.nn.Softshrink的差异
torch.nn.Softshrink
class torch.nn.Softshrink(lambd=0.5)(input) -> Tensor
更多内容详见torch.nn.Softshrink。
mindspore.nn.SoftShrink
class mindspore.nn.SoftShrink(lambd=0.5)(input_x) -> Tensor
更多内容详见mindspore.nn.SoftShrink。
差异对比
PyTorch:用于计算Softshrink激活函数。
MindSpore:接口名称与PyTorch有差异,MindSpore为SoftShrink,PyTorch为Softshrink,功能一致。
分类 |
子类 |
PyTorch |
MindSpore |
差异 |
---|---|---|---|---|
参数 |
参数1 |
lambd |
lambd |
- |
输入 |
单输入 |
input |
input_x |
功能一致,参数名不同 |
代码示例1
计算lambd=0.3的SoftShrink激活函数。
# PyTorch
import numpy as np
import torch
from torch import tensor, nn
m = nn.Softshrink(lambd=0.3)
input_ = np.array([[0.5297, 0.7871, 1.1754], [0.7836, 0.6218, -1.1542]], dtype=np.float32)
input_t = tensor(input_)
output = m(input_t)
print(output.numpy())
# [[ 0.22969997 0.4871 0.8754 ]
# [ 0.48359996 0.3218 -0.85419995]]
# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor, nn
m = nn.SoftShrink(lambd=0.3)
input_ = np.array([[0.5297, 0.7871, 1.1754], [0.7836, 0.6218, -1.1542]], dtype=np.float32)
input_t = Tensor(input_, mindspore.float32)
output = m(input_t)
print(output)
# [[ 0.22969997 0.4871 0.8754 ]
# [ 0.48359996 0.3218 -0.85419995]]
代码示例2
SoftShrink默认
lambd=0.5
。
# PyTorch
import numpy as np
import torch
from torch import tensor, nn
m = nn.Softshrink()
input_ = np.array([[0.5297, 0.7871, 1.1754], [0.7836, 0.6218, -1.1542]], dtype=np.float32)
input_t = tensor(input_)
output = m(input_t)
print(output.numpy())
# [[ 0.02969998 0.28710002 0.6754 ]
# [ 0.28359997 0.12180001 -0.65419996]]
# MindSpore
import numpy as np
import mindspore
from mindspore import Tensor, nn
m = nn.SoftShrink()
input_ = np.array([[0.5297, 0.7871, 1.1754], [0.7836, 0.6218, -1.1542]], dtype=np.float32)
input_t = Tensor(input_, mindspore.float32)
output = m(input_t)
print(output)
# [[ 0.02969998 0.28710002 0.6754 ]
# [ 0.28359997 0.12180001 -0.65419996]]