Tensorflow，如何访问 RNN 的所有中间状态，而不仅仅是最后一个状态

我的理解是tf.nn.dynamic_rnn返回 RNN 单元（例如 LSTM）在每个时间步的输出以及最终状态。如何访问所有时间步骤中的细胞状态，而不仅仅是最后一个时间步骤？例如，我希望能够对所有隐藏状态进行平均，然后在后续层中使用它。

以下是我如何定义 LSTM 单元，然后使用tf.nn.dynamic_rnn。但这仅给出了 LSTM 的最后一个单元状态。

import tensorflow as tf
import numpy as np

# [batch-size, sequence-length, dimensions] 
X = np.random.randn(2, 10, 8)
X[1,6:] = 0
X_lengths = [10, 6]

cell = tf.contrib.rnn.LSTMCell(num_units=64, state_is_tuple=True)

outputs, last_state = tf.nn.dynamic_rnn(
    cell=cell,
    dtype=tf.float64,
    sequence_length=X_lengths,
    inputs=X)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())                                 
out, last = sess.run([outputs, last_state], feed_dict=None)

像这样的东西应该有效。

import tensorflow as tf
import numpy as np


class CustomRNN(tf.contrib.rnn.LSTMCell):
    def __init__(self, *args, **kwargs):
        kwargs['state_is_tuple'] = False # force the use of a concatenated state.
        returns = super(CustomRNN, self).__init__(*args, **kwargs) # create an lstm cell
        self._output_size = self._state_size # change the output size to the state size
        return returns
    def __call__(self, inputs, state):
        output, next_state = super(CustomRNN, self).__call__(inputs, state)
        return next_state, next_state # return two copies of the state, instead of the output and the state

X = np.random.randn(2, 10, 8)
X[1,6:] = 0
X_lengths = [10, 10]

cell = CustomRNN(num_units=64)

outputs, last_states = tf.nn.dynamic_rnn(
    cell=cell,
    dtype=tf.float64,
    sequence_length=X_lengths,
    inputs=X)

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())                                 
states, last_state = sess.run([outputs, last_states], feed_dict=None)

这使用连接状态，因为我不知道是否可以存储任意数量的元组状态。 states 变量的形状为 (batch_size, max_time_size, state_size)。