Custom loss function and metrics in Keras

Introduction#

You can create a custom loss function and metrics in Keras by defining a TensorFlow/Theano symbolic function that returns a scalar for each data-point and takes the following two arguments: tensor of true values, tensor of the corresponding predicted values.

Note that the loss/metric (for display and optimization) is calculated as the mean of the losses/metric across all datapoints in the batch.

Remarks#

Keras loss functions are defined in losses.py

Additional loss functions for Keras can be found in keras-contrib repository.

Euclidean distance loss

Define a custom loss function:

import keras.backend as K


def euclidean_distance_loss(y_true, y_pred):
    """
    Euclidean distance loss
    https://en.wikipedia.org/wiki/Euclidean_distance
    :param y_true: TensorFlow/Theano tensor
    :param y_pred: TensorFlow/Theano tensor of the same shape as y_true
    :return: float
    """
    return K.sqrt(K.sum(K.square(y_pred - y_true), axis=-1))

Use it:

model.compile(loss=euclidean_distance_loss, optimizer='rmsprop')