343392813338ae7b10b0a3bbb3b5a9a7da6e588d,lib/model/losses_plaid.py,LossWrapper,__call__,#LossWrapper#Any#Any#,608

Before Change

        
        loss = 0.0
        for func, weight in zip(self._loss_functions, self._loss_weights):
            loss += (K.mean(func(y_true, y_pred)) * weight)
        return loss

After Change

                n_pred = K.reshape(n_pred, K.int_shape(y_pred))
            this_loss = func(n_true, n_pred)
            loss_dims = K.ndim(this_loss)
            loss += (K.mean(this_loss, axis=list(range(1, loss_dims))) * weight)
        return loss

    @classmethod
    def _apply_mask(cls, y_true, y_pred, mask_channel, mask_prop=1.0):

Instances