# Differences with torch.nn.functional.kl_div

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## torch.nn.functional.kl_div

```text
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](https://pytorch.org/docs/1.8.1/nn.functional.html#torch.nn.functional.kl_div).

## mindspore.ops.kl_div

```text
mindspore.ops.kl_div(logits, labels, reduction='mean')
```

For more information, see [mindspore.ops.kl_div](https://www.mindspore.cn/docs/en/r2.3.0rc1/api_python/ops/mindspore.ops.kl_div.html#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

```python
# 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
```