如何在tf.nn.dynamic_rnn中为LSTM初始化intitial_state？

Question

I am not sure how to pass a value for initial_state when the cell is a LSTMCell. I am using LSTMStateTuple as it is shown in the following piece of code:

c_placeholder = tf.placeholder(tf.float32, [ None, config.state_dim], name='c_lstm')

h_placeholder = tf.placeholder(tf.float32, [ None, config.state_dim], name='h_lstm')

state_tuple = tf.nn.rnn_cell.LSTMStateTuple(c_placeholder, h_placeholder)

cell = tf.contrib.rnn.LSTMCell(num_units=config.state_dim, state_is_tuple=True, reuse=not is_training)  

rnn_outs, states = tf.nn.dynamic_rnn(cell=cell, inputs=x,sequence_length=seqlen, initial_state=state_tuple, dtype= tf.float32)

However, the execution returns this error:

TypeError: 'Tensor' object is not iterable.

Here is the link of the documentation for dynamic_rnn

Answer 1

I've seen this same error before. I was using multiple layers of RNN-cells made with tf.contrib.rnn.MultiRNNCell , and I needed to specify a tuple of LSTMStateTuples -- one for each layer. Something like

state = tuple(
        [tf.nn.rnn_cell.LSTMStateTuple(c_ph[i], h_ph[i])
         for i in range(nRecurrentLayers)]
    )