9bcf05406490ca72b698db24636e5f2a51a5c82a,neuron/metrics.py,CategoricalCrossentropy,loss,#CategoricalCrossentropy#Any#Any#,75

Before Change

        // add prior to form posterior
        if self.log_prior is not None:
            prior = next(self.log_prior)
            ps = prior.shape
            prior = np.reshape(prior, (ps[0], np.prod(ps[1:4]), ps[4]))

            // prior to keras
            if self.use_float16:
                k_prior = K.variable(prior, dtype="float16")
            else:
                k_prior = K.variable(prior)
            log_post += K.log(K.clip(k_prior, K.epsilon(), 1))

        // loss
        loss = - y_true * log_post

After Change

        // take the mean loss
        mloss = K.mean(K.sum(K.cast(loss, "float32"), -1))
        tf.verify_tensor_all_finite(mloss, "Loss not finite")
        return mloss


class Dice(object):
    

Instances