Differences with torch.nn.functional.softmax
torch.nn.functional.softmax
torch.nn.functional.softmax(
input,
dim=None,
_stacklevel=3,
dtype=None
)
For more information, see torch.nn.functional.softmax.
mindspore.ops.softmax
class mindspore.ops.softmax(
x,
axis=-1
)
For more information, see mindspore.ops.softmax.
Differences
PyTorch: Supports to implement the function with the dim
parameter and input
, scaling the specified dimension elements between [0, 1] and the total to 1.
MindSpore: Supports to implement the function with the axis
parameter and x
, scaling the specified dimension elements between [0, 1] and the total to 1.
Classification |
Subclass |
PyTorch |
MindSpore |
difference |
---|---|---|---|---|
parameter |
parameter1 |
input |
x |
Same functions, different parameter names |
parameter2 |
dim |
axis |
Same functions, different parameter names |
|
parameter3 |
dtype |
- |
Uesd to specify the type of data for the output Tensor in PyTorch. This parameter does not exist in MindSpore |
Code Example
import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np
# In MindSpore, we can define an instance of this class first, and the default value of the parameter axis is -1.
x = ms.Tensor(np.array([1, 2, 3, 4, 5]), ms.float32)
output1 = ops.softmax(x)
print(output1)
# Out:
# [0.01165623 0.03168492 0.08612853 0.23412165 0.63640857]
x = ms.Tensor(np.array([[1, 2, 3, 4, 5], [5, 4, 3, 2, 1]]), ms.float32)
output2 = ops.softmax(x, axis=0)
print(output2)
# Out:
# [[0.01798621 0.11920292 0.5 0.880797 0.98201376]
# [0.98201376 0.880797 0.5 0.11920292 0.01798621]]
# In torch, the input and dim should be input at the same time to implement the function.
input = torch.tensor(np.array([1.0, 2.0, 3.0, 4.0, 5.0]))
output3 = torch.nn.functional.softmax(input, dim=0)
print(output3)
# Out:
# tensor([0.0117, 0.0317, 0.0861, 0.2341, 0.6364], dtype=torch.float64)