Differences with torch.nn.Softmin
torch.nn.Softmin
torch.nn.Softmin(
dim=None
)
For more information, see torch.nn.Softmin.
mindspore.nn.Softmin
class mindspore.nn.Softmin(
axis=-1
)
For more information, see mindspore.nn.Softmin.
Differences
PyTorch: Supports instantiation with the dim
parameter, which scales the specified dimension elements between [0, 1] and sums to 1. Default value: None.
MindSpore: Supports instantiation with the axis
parameter, which scales the specified dimension elements between [0, 1] and sums to 1. Default value: -1.
Classification |
Subclass |
PyTorch |
MindSpore |
difference |
---|---|---|---|---|
Parameter |
Parameter 1 |
dim |
axis |
Same function, different parameter names |
Code Example
import mindspore as ms
import mindspore.ops as ops
import mindspore.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
# MindSpore
x = ms.Tensor(np.array([1, 2, 3, 4, 5]), ms.float32)
softmin = nn.Softmin()
output1 = softmin(x)
print(output1)
# Out:
# [0.6364086 0.23412167 0.08612854 0.03168492 0.01165623]
x = ms.Tensor(np.array([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]), ms.float32)
softmin == nn.Softmin(axis=0)
output2 = softmin(x)
print(output2)
# Out:
# [ [0.63640857 0.23412165 0.08612853 0.03168492 0.01165623]
# [0.01165623 0.03168492 0.08612853 0.23412165 0.63640857]]
# PyTorch
input = torch.tensor(np.array([1.0, 2.0, 3.0, 4.0, 5.0]))
output3 = F.softmin(input, dim=0)
print(output3)
# Out:
# tensor([0.6364, 0.2341, 0.0861, 0.0317, 0.0117], dtype=torch.float64)