tf_tree_lstm.py 文件源码

def add_embedding(self, embeddings):

        #embed=np.load('glove{0}_uniform.npy'.format(self.emb_dim))
        if embeddings is not None:
            initializer = embeddings
        else:
            initializer = tf.random_uniform_initializer(-0.05,0.05)

        with tf.variable_scope("Embed",regularizer=None):
            embedding=tf.Variable(initial_value = initializer, trainable=True, name = 'embedding', dtype='float32')
            ix=tf.to_int32(tf.not_equal(self.input,-1))*self.input
            emb_tree=tf.nn.embedding_lookup(embedding,ix)
            emb_tree=emb_tree*(tf.expand_dims(
                        tf.to_float(tf.not_equal(self.input,-1)),2))

            return emb_tree