Differences with torch.nn.Softshrink
torch.nn.Softshrink
class torch.nn.Softshrink(lambd=0.5)(input) -> Tensor
For more information, see torch.nn.Softshrink.
mindspore.nn.SoftShrink
class mindspore.nn.SoftShrink(lambd=0.5)(input_x) -> Tensor
For more information, see mindspore.nn.SoftShrink.
Differences
PyTorch: Used to calculate the Softshrink activation function.
MindSpore: The interface name is different from PyTorch. MindSpore is SoftShrink, while PyTorch is Softshrink, and the function is the same.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter 1 |
lambd |
lambd |
- |
Input |
Single input |
input |
input_x |
Same function, different parameter names |
Code Example 1
Compute the SoftShrink activation function for lambd=0.3.
# 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]]
Code Example 2
SoftShrink defaults to
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]]