# add\_custom\_loss
The purpose of the `leap_binder.add_custom_loss` function 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
code_loader.leap_binder.add_custom_loss(
function=CustomCallableInterface,
name=str
)
```
Args
function
(CustomCallableInterface) This parameter points to the custom Loss Function.
name
(str) with the given name of the custom loss.
### Examples
#### Basic Usage
```python
import numpy as np
from code_loader import leap_binder
...
def weighted_categorical_crossentropy(y_true, y_pred):
# scale predictions so that the class probas of each sample sum to 1
y_pred /= K.sum(y_pred, axis=-1, keepdims=True)
# clip to prevent NaN's and Inf's
y_pred = K.clip(y_pred, K.epsilon(), 1 - K.epsilon())
# calc
weights = np.array([0.5, 2.1, 3, 4, 4, 4, 4, 4])
loss = y_true * K.log(y_pred) * weights
loss = -K.sum(loss, -1)
return loss
...
leap_binder.add_custom_loss(
function=weighted_categorical_crossentropy,
name='weighted_categorical_crossentropy'
)
```
---
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