Tensorflow, how to access all the middle states of an RNN, not just the last state

Question

My understanding is that tf.nn.dynamic_rnn returns the output of an RNN cell (e.g. LSTM) at each time step as well as the final state. How can I access cell sta

Answer 1

I would point you to this thread (highlights from me):

You can write a variant of the LSTMCell that returns both state tensors as part of the output, if you need both c and h state for each time step. If you just need the h state, that's the output of each time step.

As @jasekp wrote in its comment, the output is really the h part of the state. Then the dynamic_rnn method will just stack all the h part across time (see the string doc of _dynamic_rnn_loop in this file):

def _dynamic_rnn_loop(cell,
                      inputs,
                      initial_state,
                      parallel_iterations,
                      swap_memory,
                      sequence_length=None,
                      dtype=None):
  """Internal implementation of Dynamic RNN.
    [...]
    Returns:
    Tuple `(final_outputs, final_state)`.
    final_outputs:
      A `Tensor` of shape `[time, batch_size, cell.output_size]`.  If
      `cell.output_size` is a (possibly nested) tuple of ints or `TensorShape`
      objects, then this returns a (possibly nsted) tuple of Tensors matching
      the corresponding shapes.

Answer 2

Something like this should work.

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)

This uses concatenated states, as I don't know if you can store an arbitrary number of tuple states. The states variable is of shape (batch_size, max_time_size, state_size).