rntn.py 文件源码

def predict(self, tree):
        if tr.isleaf(tree):
            # output = word vector
            try:
                tree.vector = self.L[:, self.word_map[tree[0]]]
            except:
                tree.vector = self.L[:, self.word_map[tr.UNK]]
        else:
            # calculate output of child nodes
            self.predict(tree[0])
            self.predict(tree[1])

            # compute output
            lr = np.hstack([tree[0].vector, tree[1].vector])
            tree.vector = np.tanh(
                np.tensordot(self.V, np.outer(lr, lr), axes=([1, 2], [0, 1])) +
                np.dot(self.W, lr) + self.b)

        # softmax
        import util
        tree.output = util.softmax(np.dot(self.Ws, tree.vector) + self.bs)
        label = np.argmax(tree.output)
        tree.set_label(str(label))
        return tree