LossFunctions.py

#implement Loss function

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from __future__ import print_function, division
import keras.backend as K
import numpy as np

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def gradient_penalty_loss(y_true, y_pred, averaged_samples):
    """
    Computes gradient penalty based on prediction and weighted real / fake samples
    """
    gradients = K.gradients(y_pred, averaged_samples)[0]
    # compute the euclidean norm by squaring ...
    gradients_sqr = K.square(gradients)
    #   ... summing over the rows ...
    gradients_sqr_sum = K.sum(gradients_sqr, axis=np.arange(1, len(gradients_sqr.shape)))
    #   ... and sqrt
    gradient_l2_norm = K.sqrt(gradients_sqr_sum)
    # compute lambda * (1 - ||grad||)^2 still for each single sample
    gradient_penalty = K.square(1 - gradient_l2_norm)
    # return the mean as loss over all the batch samples
    return K.mean(gradient_penalty)

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def wasserstein_loss(y_true, y_pred):
    return K.mean(y_true * y_pred)