Fill a tensor with a value that is not a scalar - tensorflow

Question

I am trying to move a list of points to the origin using tensorflow the best way to do it mathematically is to find the centroid of the list of points then subtract the list of points by that centroid.
The problems: The number of rows contained in the point list is unknown until runtime.
Code so far:
import tensorflow as tf
example_point_list = tf.constant([[3., 3.], [.2, .2], [.1, .1]]) // but with any number of points
centroid = tf.reduce_mean(example_point_list, 0)
// subtract???
origin_point_list = tf.sub(example_point_list, centroid)
The problem is that subtract works on an element by element basis so I have to create a centroid tensor with the same number of rows as the point list but there are no methods that do that.
(to put it in math terms)
A = [[1, 1],
[2, 2]
[3, 3]]
B = avg(A) // [2, 2]
// step I need to do but do not know how to do it
B -> B1 // [[2, 2], [2, 2], [2, 2]]
Result = A - B1
Any help is appreciated!

Answer 1

Because of broadcasting, you don't need to tile the rows. In fact, it's more efficient to not tile them and subtract vector from matrix directly. In your case it would look like this
tf.reset_default_graph()
example_points = np.array([[1, 1], [2, 2], [3, 3]], dtype=np.float32)
example_point_list = tf.placeholder(tf.float32)
centroid = tf.reduce_mean(example_point_list, 0)
result = example_point_list - centroid
sess = tf.InteractiveSession()
sess.run(result, feed_dict={example_point_list: example_points})
result
array([[-1., -1.],
[ 0., 0.],
[ 1., 1.]], dtype=float32)
If you really want to tile the centroid vector explicitly, you could do it using shape operator which can get shape during runtime
tf.reset_default_graph()
example_point_list0 = np.array([[1, 1], [2, 2], [3, 3]], dtype=np.float32)
example_point_list = tf.placeholder(tf.float32)
# get number of examples from the array: [3]
num_examples = tf.slice(tf.shape(example_points), [0], [1])
# reshape [3] into 3
num_examples_flat = tf.reshape(num_examples, ())
centroid = tf.reduce_mean(example_point_list, 0)
# reshape centroid vector [2, 2] into matrix [[2, 2]]
centroid_matrix = tf.reshape(centroid, [1, -1])
# assemble 3 into vector of dimensions to tile: [3, 1]
tile_shape = tf.pack([num_examples_flat, 1])
# tile [[2, 2]] into [[2, 2], [2, 2], [2, 2]]
centroid_tiled = tf.tile(centroid_matrix, tile_shape)
sess = tf.InteractiveSession()
sess.run(centroid_tiled, feed_dict={example_point_list: example_point_list0})
result
array([[ 2., 2.],
[ 2., 2.],
[ 2., 2.]], dtype=float32)