Trying to implement recurrent network with tf.scan() - tensorflow

Question

I am trying to implement a recurrent state tensor using tf.scan. The code I have at the moment is this:
import tensorflow as tf
import math
import numpy as np
INPUTS = 10
HIDDEN_1 = 20
BATCH_SIZE = 3
def iterate_state(prev_state_tuple, input):
with tf.name_scope('h1'):
weights = tf.get_variable('W', shape=[INPUTS, HIDDEN_1], initializer=tf.truncated_normal_initializer(stddev=1.0 / math.sqrt(float(INPUTS))))
biases = tf.get_variable('bias', shape=[HIDDEN_1], initializer=tf.constant_initializer(0.0))
matmuladd = tf.matmul(inputs, weights) + biases
unpacked_state, unpacked_out = tf.split(0,2,prev_state_tuple)
prev_state = unpacked_state
state = 0.9* prev_state + 0.1*matmuladd
output = tf.nn.relu(state)
return tf.concat(0,[state, output])
def data_iter():
while True:
idxs = np.random.rand(BATCH_SIZE, INPUTS)
yield idxs
with tf.Graph().as_default():
inputs = tf.placeholder(tf.float32, shape=(BATCH_SIZE, INPUTS))
with tf.variable_scope('states'):
initial_state = tf.zeros([HIDDEN_1],
name='initial_state')
initial_out = tf.zeros([HIDDEN_1],
name='initial_out')
concat_tensor = tf.concat(0,[initial_state, initial_out])
states, output = tf.scan(iterate_state, inputs,
initializer=concat_tensor, name='states')
sess = tf.Session()
# Run the Op to initialize the variables.
sess.run(tf.initialize_all_variables())
iter_ = data_iter()
for i in xrange(0, 2):
print ("iteration: ",i)
input_data = iter_.next()
out,st = sess.run([output,states], feed_dict={ inputs: input_data})
However, I get this error when running this:
Traceback (most recent call last):
File "cycles_in_graphs_with_scan.py", line 37, in <module>
initializer=concat_tensor, name='states')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 442, in __iter__
raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable.
(tensorflow)charlesq#Leviathan ~/projects/stuff $ python cycles_in_graphs_with_scan.py
Traceback (most recent call last):
File "cycles_in_graphs_with_scan.py", line 37, in <module>
initializer=concat_tensor, name='states')
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 442, in __iter__
raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable.
I've already tried with pack/unpack and concat/split but I get this same error.
Any ideas how to solve this problem?

Answer 1

You're getting an error because tf.scan() returns a single tf.Tensor, so the line:
states, output = tf.scan(...)
...cannot destructure (unpack) the tensor returned from tf.scan() into two values (states and outputs). Effectively, the code is trying to treat the result of tf.scan() as a list of length 2, and assign the first element to states and the second element to output, but—unlike a Python list or tuple—tf.Tensor does not support this.
Instead you need to extract the values from the result of tf.scan() manually. For example, using tf.split():
scan_result = tf.scan(...)
# Assumes values are packed together along `split_dim`.
states, output = tf.split(split_dim, 2, scan_result)
Alternatively, you could use tf.slice() or tf.unpack() to extract the relevant states and output values.