I work with a lot of dtype="str"
data. I've been trying to build a simple graph as in https://www.tensorflow.org/versions/master/api_docs/python/train.html#SummaryWriter.
For a simple operation, I wanted to concatenate strings together using a placeholder
as in (How to feed a placeholder?)
Does anyone know how to merge string tensors together?
import tensorflow as tf
sess = tf.InteractiveSession()
with tf.name_scope("StringSequence") as scope:
left = tf.constant("aaa",name="LEFT")
middle = tf.placeholder(dtype=tf.string, name="MIDDLE")
right = tf.constant("ccc",name="RIGHT")
complete = tf.add_n([left,middle,right],name="COMPLETE") #fails here
sess.run(complete,feed_dict={middle:"BBB"})
#writer = tf.train.SummaryWriter("/users/mu/test_out/", sess.graph_def)
Tensors often contain floats and ints, but have many other types, including: complex numbers. strings.
String join is significantly faster then concatenation. Why? Strings are immutable and can't be changed in place. To alter one, a new representation needs to be created (a concatenation of the two).
Thanks to your question, we prioritized adding support for string concatenation in TensorFlow, and added it in this commit. String concatenation is implemented using the existing tf.add()
operator, to match the behavior of NumPy's add
operator (including broadcasting).
To implement your example, you can write:
complete = left + middle + right
…or, equivalently, but if you want to name the resulting tensor:
complete = tf.add(tf.add(left, middle), right, name="COMPLETE")
We have not yet added support for strings in tf.add_n()
(or related ops like tf.reduce_sum()
) but will consider this if there are use cases for it.
NOTE: To use this functionality immediately, you will need to build TensorFlow from source. The new op will be available in the next release of TensorFlow (0.7.0).
I believe the sparse_concat op is what you are looking for: https://www.tensorflow.org/versions/master/api_docs/python/sparse_ops.html#sparse_concat
add_n will add numeric values together.
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