Variable-Length Bi-Directional RNN about tensorflow-examples HOT 3 CLOSED

import tensorflow as tf
from tensorflow.models.rnn import rnn
from tensorflow.models.rnn.rnn_cell import BasicLSTMCell, LSTMCell
import numpy as np

if __name__ == '__main__':
  np.random.seed(1)
  size = 100
  batch_size= 100
  n_steps = 45
  seq_width = 50

  initializer = tf.random_uniform_initializer(-1,1)

  seq_input = tf.placeholder(tf.float32, [n_steps, batch_size, seq_width])
    #sequence we will provide at runtime  
  early_stop = tf.placeholder(tf.int32)
    #what timestep we want to stop at

  inputs = [tf.reshape(i, (batch_size, seq_width)) for i in tf.split(0, n_steps, seq_input)]
    #inputs for rnn needs to be a list, each item being a timestep. 
    #we need to split our input into each timestep, and reshape it because split keeps dims by default  

  cell = LSTMCell(size, seq_width, initializer=initializer)
  initial_state = cell.zero_state(batch_size, tf.float32)
  outputs, states = rnn.rnn(cell, inputs, initial_state=initial_state, sequence_length=early_stop)
    #set up lstm

  iop = tf.initialize_all_variables()
    #create initialize op, this needs to be run by the session!
  session = tf.Session()
  session.run(iop)

  for e_s in [10, 100, 200, 250]:
    feed = {early_stop:e_s, seq_input:np.random.rand(n_steps, batch_size, seq_width).astype('float32')}
    outs = session.run(outputs, feed_dict=feed)
    print len(outs)