rnn_cell.py 文件源码

def __call__(self, inputs, state, scope=None):
        """

        :param inputs: [N, d + JQ + JQ * d]
        :param state: [N, d]
        :param scope:
        :return:
        """
        with tf.variable_scope(scope or self.__class__.__name__):
            c_prev, h_prev = state
            x = tf.slice(inputs, [0, 0], [-1, self._input_size])
            q_mask = tf.slice(inputs, [0, self._input_size], [-1, self._q_len])  # [N, JQ]
            qs = tf.slice(inputs, [0, self._input_size + self._q_len], [-1, -1])
            qs = tf.reshape(qs, [-1, self._q_len, self._input_size])  # [N, JQ, d]
            x_tiled = tf.tile(tf.expand_dims(x, 1), [1, self._q_len, 1])  # [N, JQ, d]
            h_prev_tiled = tf.tile(tf.expand_dims(h_prev, 1), [1, self._q_len, 1])  # [N, JQ, d]
            f = tf.tanh(linear([qs, x_tiled, h_prev_tiled], self._input_size, True, scope='f'))  # [N, JQ, d]
            a = tf.nn.softmax(exp_mask(linear(f, 1, True, squeeze=True, scope='a'), q_mask))  # [N, JQ]
            q = tf.reduce_sum(qs * tf.expand_dims(a, -1), 1)
            z = tf.concat(1, [x, q])  # [N, 2d]
            return self._cell(z, state)