Differences with torch.nn.functional.elu
torch.nn.functional.elu
torch.nn.functional.elu(input, alpha=1.0, inplace=False) -> Tensor
For more information, see torch.nn.functional.elu.
mindspore.ops.elu
mindspore.ops.elu(input_x, alpha=1.0) -> Tensor
For more information, see mindspore.ops.elu.
Differences
PyTorch: compute the exponential linear value of the input x. The result is \( \text{elu}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1)) \), with the inplace parameter to choose whether to operate in-place or not, and the default is False.
MindSpore: MindSpore API implements the same functions as PyTorch, but α currently only supports 1.0.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
input |
input_x |
Same function, different parameter names |
Parameter 2 |
alpha |
alpha |
α factor. MindSpore currently only supports alpha equal to 1.0 |
|
Parameter 3 |
inplace |
- |
MindSpore does not have this parameter |
Code Example 1
Both APIs implement the exponential linear unit function, but PyTorch can customize the α coefficient, and MindSpore only supports a coefficient of 1.0.
# PyTorch
import torch
from torch import tensor
from torch.nn.functional import elu
import numpy as np
x_ = np.array([[np.arange(-6,0).reshape(2, 3),np.arange(0,6).reshape(2, 3)]])
x = tensor(x_, dtype=torch.float32)
output = elu(x, alpha = 1).detach().numpy()
print(output)
# [[[[-0.9975212 -0.99326205 -0.9816844 ]
# [-0.95021296 -0.86466473 -0.63212055]]
#
# [[ 0. 1. 2. ]
# [ 3. 4. 5. ]]]]
# MindSpore
import mindspore as ms
from mindspore import ops
import numpy as np
x_ = np.array([[np.arange(-6,0).reshape(2, 3),np.arange(0,6).reshape(2, 3)]])
x = ms.Tensor(x_, ms.float32)
output = ops.elu(x)
print(output)
# [[[[-0.9975212 -0.99326205 -0.9816844 ]
# [-0.95021296 -0.86466473 -0.6321205 ]]
#
# [[ 0. 1. 2. ]
# [ 3. 4. 5. ]]]]