Computing Edit Distance (feed_dict error) - tensorflow

Question

I've written some code in Tensorflow to compute the edit-distance between one string and a set of strings. I can't figure out the error.
import tensorflow as tf
sess = tf.Session()
# Create input data
test_string = ['foo']
ref_strings = ['food', 'bar']
def create_sparse_vec(word_list):
num_words = len(word_list)
indices = [[xi, 0, yi] for xi,x in enumerate(word_list) for yi,y in enumerate(x)]
chars = list(''.join(word_list))
return(tf.SparseTensor(indices, chars, [num_words,1,1]))
test_string_sparse = create_sparse_vec(test_string*len(ref_strings))
ref_string_sparse = create_sparse_vec(ref_strings)
sess.run(tf.edit_distance(test_string_sparse, ref_string_sparse, normalize=True))
This code works and when run, it produces the output:
array([[ 0.25],
[ 1. ]], dtype=float32)
But when I attempt to do this by feeding the sparse tensors in through sparse placeholders, I get an error.
test_input = tf.sparse_placeholder(dtype=tf.string)
ref_input = tf.sparse_placeholder(dtype=tf.string)
edit_distances = tf.edit_distance(test_input, ref_input, normalize=True)
feed_dict = {test_input: test_string_sparse,
ref_input: ref_string_sparse}
sess.run(edit_distances, feed_dict=feed_dict)
Here is the error traceback:
Traceback (most recent call last):
File "<ipython-input-29-4e06de0b7af3>", line 1, in <module>
sess.run(edit_distances, feed_dict=feed_dict)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 372, in run
run_metadata_ptr)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 597, in _run
for subfeed, subfeed_val in _feed_fn(feed, feed_val):
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 558, in _feed_fn
return feed_fn(feed, feed_val)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 268, in <lambda>
[feed.indices, feed.values, feed.shape], feed_val)),
TypeError: zip argument #2 must support iteration
Any idea what is going on here?

Answer 1

TL;DR: For the return type of create_sparse_vec(), use tf.SparseTensorValue instead of tf.SparseTensor.
The problem here comes from the return type of create_sparse_vec(), which is tf.SparseTensor, and is not understood as a feed value in the call to sess.run().
When you feed a (dense) tf.Tensor, the expected value type is a NumPy array (or certain objects that can be converted to an array). When you feed a tf.SparseTensor, the expected value type is a tf.SparseTensorValue, which is similar to a tf.SparseTensor but its indices, values, and shape properties are NumPy arrays (or certain objects that can be converted to arrays, like the lists in your example.
The following code should work:
def create_sparse_vec(word_list):
num_words = len(word_list)
indices = [[xi, 0, yi] for xi,x in enumerate(word_list) for yi,y in enumerate(x)]
chars = list(''.join(word_list))
return tf.SparseTensorValue(indices, chars, [num_words,1,1])