> For the complete documentation index, see [llms.txt](https://docs.tensorleap.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorleap.ai/tensorleap-integration/python-api/code_loader/decorators/tensorleap_custom_loss.md).
# @tensorleap\_custom\_loss
The purpose of the `tensorleap_custom_loss` decorator is to register the [**Custom Loss Function(s)**](/tensorleap-integration/writing-integration-code/custom-loss-function.md) to be used within the platform. This function adds the custom loss function to the selection list within the **CustomLoss** node.
```python
@tensorleap_custom_loss(name='weighted_ce')
def loss(tensor_1: np.ndarray, tensor_2: np.ndarray, ...) -> np.ndarray:
pass
```
Args
name
(str) with the given name of the custom loss.
#### Loss Function inputs:
np.ndarray tensors with a batch dimension
#### Loss Function outputs:
np.ndarray batched loss values
### Examples
#### Basic Usage
```python
from code_loader.contract.datasetclasses import PreprocessResponse
from code_loader.inner_leap_binder.leapbinder_decorators import tensorleap_custom_loss
import numpy as np
...
@tensorleap_custom_loss(name='weighted_ce')
def weighted_categorical_crossentropy(y_true :np.ndarray, y_pred: np.ndarray) -> np.ndarray:
# Normalize predictions so each sample's probabilities sum to 1
y_pred = y_pred / np.sum(y_pred, axis=-1, keepdims=True)
# Clip predictions to avoid log(0) and ensure numerical stability
epsilon = 1e-7 # Similar to K.epsilon()
y_pred = np.clip(y_pred, epsilon, 1 - epsilon)
# Define class weights
weights = np.array([0.5, 2.1, 3, 4, 4, 4, 4, 4])
# Compute weighted log loss
loss = y_true * np.log(y_pred) * weights
loss = -np.sum(loss, axis=-1)
return loss
```
---
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