model.py 文件源码

def loss_func(self, y_true, y_predict):
        active_notes = T.shape_padright(y_true[:,:,:,0])
        mask = T.concatenate([T.ones_like(active_notes), active_notes, T.repeat(T.ones_like(active_notes), self.output_size-2, -1)], axis=-1)
        loglikelihoods = mask * T.log( 2*y_predict*y_true - y_predict - y_true + 1 + self.epsilon )
        return T.neg(T.sum(loglikelihoods))