# @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
```

<table><thead><tr><th width="158.46928201888204">Args</th><th></th></tr></thead><tbody><tr><td><code>name</code></td><td><em>(str)</em> with the given name of the <strong>custom loss.</strong></td></tr></tbody></table>

#### 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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