Looping over a tensor

Question

I am trying to process a tensor of variable size, in a python way that would be something like:

# X is of shape [m, n]
for x in X:
    process(x)


        

Answer 1

Most of tensorflow built-in functions could be applied elementwise. So you could just pass a tensor into a function. Like:

outer_loop = inner_loop(x)

However, if you have some function that could not be applied this way (it's really tempting to see that function), you could use map_fn.

Say, your function simply adds 1 to every element of a tensor (or whatever):

inputs = tf.placeholder...

def my_elementwise_func(x):
    return x + 1

def recursive_map(inputs):
   if tf.shape(inputs).ndims > 0:
       return tf.map_fn(recursive_map, inputs)
   else:
       return my_elementwise_func(inputs)

result = recursive_map(inputs)  

Answer 2

To loop over a tensor you could try tf.unstack

Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.

So adding 1 to each tensor would look something like:

import tensorflow as tf
x = tf.placeholder(tf.float32, shape=(None, 10))
x_unpacked = tf.unstack(x) # defaults to axis 0, returns a list of tensors

processed = [] # this will be the list of processed tensors
for t in x_unpacked:
    # do whatever
    result_tensor = t + 1
    processed.append(result_tensor)

output = tf.concat(processed, 0)

with tf.Session() as sess:
    print(sess.run([output], feed_dict={x: np.zeros((5, 10))}))

Obviously you can further unpack each tensor from the list to process it, down to single elements. To avoid lots of nested unpacking though, you could maybe try flattening x with tf.reshape(x, [-1]) first, and then loop over it like

flattened_unpacked = tf.unstack(tf.reshape(x, [-1])
for elem in flattened_unpacked:
    process(elem)

In this case elem is a scalar.