lstm_model_sim.py 文件源码

def getRegTerm(self, params, We, initial_We, l_out, l_softmax, pickled_params):
        if params.traintype == "normal":
            l2 = 0.5*params.LC*sum(lasagne.regularization.l2(x) for x in self.network_params)
            if params.updatewords:
                return l2 + 0.5*params.LW*lasagne.regularization.l2(We-initial_We)
            else:
                return l2
        elif params.traintype == "reg":
            tmp = lasagne.layers.get_all_params(l_out, trainable=True)
            idx = 1
            l2 = 0.
            while idx < len(tmp):
                l2 += 0.5*params.LRC*(lasagne.regularization.l2(tmp[idx]-np.asarray(pickled_params[idx].get_value(), dtype = config.floatX)))
                idx += 1
            tmp = lasagne.layers.get_all_params(l_softmax, trainable=True)
            l2 += 0.5*params.LC*sum(lasagne.regularization.l2(x) for x in tmp)
            return l2 + 0.5*params.LRW*lasagne.regularization.l2(We-initial_We)
        elif params.traintype == "rep":
            tmp = lasagne.layers.get_all_params(l_softmax, trainable=True)
            l2 = 0.5*params.LC*sum(lasagne.regularization.l2(x) for x in tmp)
            return l2
        else:
            raise ValueError('Params.traintype not set correctly.')