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]]