tree_lstm.py 文件源码

def create_recursive_unit(self):
        self.W_i = theano.shared(self.init_matrix([self.hidden_dim, self.emb_dim]))
        self.U_i = theano.shared(self.init_matrix(
            [self.degree, self.hidden_dim, self.hidden_dim]))
        self.b_i = theano.shared(self.init_vector([self.hidden_dim]))
        self.W_f = theano.shared(self.init_matrix([self.hidden_dim, self.emb_dim]))
        self.U_f = theano.shared(self.init_matrix(
            [self.degree, self.degree, self.hidden_dim, self.hidden_dim]))
        self.b_f = theano.shared(self.init_vector([self.hidden_dim]))
        self.W_o = theano.shared(self.init_matrix([self.hidden_dim, self.emb_dim]))
        self.U_o = theano.shared(self.init_matrix(
            [self.degree, self.hidden_dim, self.hidden_dim]))
        self.b_o = theano.shared(self.init_vector([self.hidden_dim]))
        self.W_u = theano.shared(self.init_matrix([self.hidden_dim, self.emb_dim]))
        self.U_u = theano.shared(self.init_matrix(
            [self.degree, self.hidden_dim, self.hidden_dim]))
        self.b_u = theano.shared(self.init_vector([self.hidden_dim]))
        self.params.extend([
            self.W_i, self.U_i, self.b_i,
            self.W_f, self.U_f, self.b_f,
            self.W_o, self.U_o, self.b_o,
            self.W_u, self.U_u, self.b_u])

        def unit(parent_x, child_h, child_c, child_exists):
            (h_i, h_o, h_u), _ = theano.map(
                fn=lambda Ui, Uo, Uu, h, exists:
                    (exists * T.dot(Ui, h), exists * T.dot(Uo, h), exists * T.dot(Uu, h)),
                sequences=[self.U_i, self.U_o, self.U_u, child_h, child_exists])

            i = T.nnet.sigmoid(T.dot(self.W_i, parent_x) + h_i.sum(axis=0) + self.b_i)
            o = T.nnet.sigmoid(T.dot(self.W_o, parent_x) + h_o.sum(axis=0) + self.b_o)
            u = T.tanh(T.dot(self.W_u, parent_x) + h_u.sum(axis=0) + self.b_u)

            def _sub_f(U):
                sub_h_f, _ = theano.map(
                    fn=lambda sub_U, h, exists: exists * T.dot(sub_U, h),
                    sequences=[U, child_h, child_exists])
                return sub_h_f.sum(axis=0)

            h_f, _ = theano.map(
                fn=lambda U: _sub_f(U),
                sequences=[self.U_f])
            f = (T.nnet.sigmoid(
                    T.dot(self.W_f, parent_x).dimshuffle('x', 0) + h_f +
                    self.b_f.dimshuffle('x', 0)) *
                 child_exists.dimshuffle(0, 'x'))

            c = i * u + T.sum(f * child_c, axis=0)
            h = o * T.tanh(c)
            return h, c

        return unit