00d47eb3923d8722f75d56ba15139ad6821e2f50,basic/model.py,Model,_build_forward,#Model#,48

Before Change

        with tf.variable_scope("rnn1"):
            (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype="float", scope="start")  // [N, M, JX, 2d]
            tf.get_variable_scope().reuse_variables()
            (fw_us, bw_us), (_, (fw_u, bw_u)) = bidirectional_dynamic_rnn(cell, cell, qq, q_len, dtype="float", scope="start")  // [N, J, d], [N, d]
            h = tf.concat(3, [fw_h, bw_h])
            u = tf.concat(1, [fw_u, bw_u])

After Change

        with tf.variable_scope("rnn1"):
            (fw_us, bw_us), (_, (fw_u, bw_u)) = bidirectional_dynamic_rnn(cell, cell, qq, q_len, dtype="float", scope="u")  // [N, J, d], [N, d]
            u = tf.concat(1, [fw_u, bw_u])
            u = tf.tile(tf.expand_dims(tf.expand_dims(u, 1), 1), [1, M, JX, 1])

            xx = tf.concat(3, [xx, u])

            (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, xx, x_len, dtype="float", scope="h1")  // [N, M, JX, 2d]
            h = tf.concat(3, [fw_h, bw_h])
            (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell, cell, h, x_len, dtype="float", scope="h2")  // [N, M, JX, 2d]

Instances