6c4e91a811ad9e996579f99eec9b9e6f68db72dd,basic/model.py,Model,_build_forward,#Model#,52

Before Change

                else:
                    word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype="float")
            else:
                word_emb_mat = config.emb_mat.astype("float32")
            Ax = tf.nn.embedding_lookup(word_emb_mat, self.x)  // [N, M, JX, d]
            Aq = tf.nn.embedding_lookup(word_emb_mat, self.q)  // [N, JQ, d]

        xx = tf.concat(3, [xxc, Ax])  // [N, M, JX, di]

After Change

                word_emb_mat = tf.get_variable("word_emb_mat", dtype="float", shape=[VW, dw], initializer=get_initializer(config.emb_mat))
            else:
                word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype="float")
            if config.use_glove_for_unk:
                new_word_emb_mat = tf.concat(0, [word_emb_mat, tf.constant(config.new_emb_mat, dtype="float32")])
                word_emb_mat = tf.cond(self.is_train, lambda: word_emb_mat, lambda: new_word_emb_mat)

            Ax = tf.nn.embedding_lookup(word_emb_mat, self.x)  // [N, M, JX, d]
            Aq = tf.nn.embedding_lookup(word_emb_mat, self.q)  // [N, JQ, d]

        xx = tf.concat(3, [xxc, Ax])  // [N, M, JX, di]

Instances