rcnn_class.py 文件源码

def get_cost(self, X, Y, X_sizes):
        """
        Calculates cost for each values in mini batch, also
        regularizes all the input parameters and then returns
        final cost function as theano variable

        """
        cost_fn, _ = theano.scan(
            fn=self.get_likelihood,
            sequences=[X, Y, X_sizes]
        )

        cost_fn = cost_fn.mean()
        cost_fn += self.reg_lambda * T.sqr(self.W_c_r).sum() / 2.
        cost_fn += self.reg_lambda * T.sqr(self.W_c_l).sum() / 2.
        cost_fn += self.reg_lambda * T.sqr(self.W_conv).sum() / 2.
        cost_fn += self.reg_lambda * T.sqr(self.W_output).sum() / 2.
        cost_fn += self.reg_lambda * T.sqr(self.b_output).sum() / 2.

        # Regularizing word embedding
        cost_fn += self.reg_lambda * T.sqr(self.vector_dict).sum() / 2

        return cost_fn