models.py 文件源码

def get_costs(self, probs, y, y_mask,
                  decay_cs=None, opt_rets=None):
        """
        probs : dict, mapping cg_name to probabilities
        y : theano tensor variable
        y_mask : theano tensor variable
        decay_cs : list of l2 regularization weights
        opt_rets : dict, mapping cg_name to optional returned variables
        """
        costs = self.decoder.costs(probs, y, y_mask)

        if decay_cs is not None:
            for name, cost in costs.iteritems():
                if decay_cs[name] > 0.:
                    decay_c = theano.shared(numpy.float32(decay_cs[name]),
                                            name='decay_c')
                    weight_decay = 0.
                    for pp in ComputationGraph(cost).parameters:
                        weight_decay += (pp ** 2).sum()
                    weight_decay *= decay_c
                    costs[name] += weight_decay
                    costs[name].name = name

        return costs