skip_rnn_cells.py 文件源码

def trainable_initial_state(self, batch_size):
        """
        Create a trainable initial state for the SkipLSTMCell
        :param batch_size: number of samples per batch
        :return: SkipLSTMStateTuple
        """
        with tf.variable_scope('initial_c'):
            initial_c = rnn_ops.create_initial_state(batch_size, self._num_units)
        with tf.variable_scope('initial_h'):
            initial_h = rnn_ops.create_initial_state(batch_size, self._num_units)
        with tf.variable_scope('initial_update_prob'):
            initial_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
                                                               initializer=tf.ones_initializer())
        with tf.variable_scope('initial_cum_update_prob'):
            initial_cum_update_prob = rnn_ops.create_initial_state(batch_size, 1, trainable=False,
                                                                   initializer=tf.zeros_initializer())
        return SkipLSTMStateTuple(initial_c, initial_h, initial_update_prob, initial_cum_update_prob)