Differences with torch.nn.functional.kl_div
torch.nn.functional.kl_div
torch.nn.functional.kl_div(input, target, size_average=None, reduce=None, reduction='mean', log_target=False)
For more information, see torch.nn.functional.kl_div.
mindspore.ops.kl_div
mindspore.ops.kl_div(logits, labels, reduction='mean')
For more information, see mindspore.ops.kl_div.
Differnnces
PyTorch: Compute the KL divergence of logits
and labels
, log_target
is the flag Indicates whether the target
is passed to the log space.
MindSpore: MindSpore API basically implements the same function as PyTorch, but the log_target
is not defined.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameters |
Parameter 1 |
input |
logits |
both are input Tensors |
Parameter 2 |
target |
labels |
both are input Tensors |
|
Parameter 3 |
size_average |
- |
Same function. PyTorch has deprecated this parameter, and MindSpore does not have this parameter. |
|
Parameter 4 |
reduce |
- |
Same function. PyTorch has deprecated this parameter, and MindSpore does not have this parameter. |
|
Parameter 5 |
reduction |
reduction |
Same function, different default values. |
|
Parameter 6 |
log_target |
- |
parameter not defined |
Code Example
# PyTorch
import torch
import numpy as np
logits = torch.tensor(np.array([0.2, 0.7, 0.1]))
labels = torch.tensor(np.array([0., 1., 0.]))
output = torch.nn.functional.kl_div(logits, labels)
print(output)
# tensor(-0.2333, dtype=torch.float64)
# MindSpore
import mindspore
from mindspore import Tensor
import numpy as np
logits = Tensor(np.array([0.2, 0.7, 0.1]), mindspore.float32)
labels = Tensor(np.array([0., 1., 0.]), mindspore.float32)
output = mindspore.ops.kl_div(logits, labels, 'mean')
print(output)
# -0.23333333