Can I apply tf.map_fn(...) to multiple inputs/outputs?

Question

a = tf.constant([[1,2,3],[4,5,6]])
b = tf.constant([True, False], dtype=tf.bool)

a.eval()
array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)
b.eval()
array([ True, False], dtype=bool)

I want to apply a functions to the inputs above, a, and b using tf.map_fn. It will input both [1,2,3], and True and output similar values.

Let's say out function is simply the identity: lambda(x,y): x,y so, given an input of [1,2,3], True, it will output those identical tensors.

I know how to use tf.map_fn(...) with one variable, but not with two. And in this case I have mixed data types (int32 and bool) so I can't simply concatenate the tensors and split them after the call.

Can I use tf.map_fn(...) with multiple inputs/outputs of different data types?

Answer 1

Figured it out. You have to define the data types for each tensor in dtype for each of the different tensors, then you can pass the tensors as a tuple, your map function receives a tuple of inputs, and map_fn returns back back a tuple.

Example that works:

a = tf.constant([[1,2,3],[4,5,6]])
b = tf.constant([True, False], dtype=tf.bool)

c = tf.map_fn(lambda x: (x[0], x[1]), (a,b), dtype=(tf.int32, tf.bool))

c[0].eval()
array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)
c[1].eval()
array([ True, False], dtype=bool)