How to extract the cell state and hidden state from an RNN model in tensorflow?

Question

I am new to TensorFlow and have difficulties understanding the RNN module. I am trying to extract hidden/cell states from an LSTM. For my code, I am using the implementation fr

Answer 1

I have to disagree with the answer of user3480922. For the code:

outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

to be able to extract the hidden state for each time_step in a prediction, you have to use the outputs. Because outputs have the hidden state value for each time_step. However, I am not sure is there any way we can store the values of the cell state for each time_step as well. Because states tuple provides the cell state values but only for the last time_step.

For example, in the following sample with 5 time_steps, the outputs[4,:,:], time_step = 0,...,4 has the hidden state values for time_step=4, whereas the states tuple h only has the hidden state values for time_step=4. State tuple c has the cell value at the time_step=4 though.

  outputs = [[[ 0.0589103 -0.06925126 -0.01531546 0.06108122]
  [ 0.00861215 0.06067181 0.03790079 -0.04296958]
  [ 0.00597713 0.03916606 0.02355802 -0.0277683 ]]

  [[ 0.06252582 -0.07336216 -0.01607122 0.05024602]
  [ 0.05464711 0.03219429 0.06635305 0.00753127]
  [ 0.05385715 0.01259535 0.0524035 0.01696803]]

  [[ 0.0853352 -0.06414541 0.02524283 0.05798233]
  [ 0.10790729 -0.05008117 0.03003334 0.07391824]
  [ 0.10205664 -0.04479517 0.03844892 0.0693808 ]]

  [[ 0.10556188 0.0516542 0.09162509 -0.02726674]
  [ 0.11425048 -0.00211394 0.06025286 0.03575509]
  [ 0.11338984 0.02839304 0.08105748 0.01564003]]

  **[[ 0.10072514 0.14767936 0.12387902 -0.07391471]
  [ 0.10510238 0.06321315 0.08100517 -0.00940042]
  [ 0.10553667 0.0984127 0.10094948 -0.02546882]]**]
  states = LSTMStateTuple(c=array([[ 0.23870754, 0.24315512, 0.20842518, -0.12798975],
  [ 0.23749796, 0.10797793, 0.14181322, -0.01695861],
  [ 0.2413336 , 0.16692916, 0.17559692, -0.0453596 ]], dtype=float32), h=array(**[[ 0.10072514, 0.14767936, 0.12387902, -0.07391471],
  [ 0.10510238, 0.06321315, 0.08100517, -0.00940042],
  [ 0.10553667, 0.0984127 , 0.10094948, -0.02546882]]**, dtype=float32))