add_custom_loss

code_loader.leap_binder.add_custom_loss

The purpose of the leap_binder.add_custom_loss function is to register the Custom Loss Function(s) to be used within the platform. This function adds the custom loss function to the selection list within the CustomLoss node.

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

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'
)