How to find an index of the first matching element in TensorFlow

Question

I am looking for a TensorFlow way of implementing something similar to Python's list.index() function.

Given a matrix and a value to find, I want to know the first occurrence of the value in each row of the matrix.

For example,

m is a <batch_size, 100> matrix of integers
val = 23

result = [0] * batch_size
for i, row_elems in enumerate(m):
  result[i] = row_elems.index(val)

I cannot assume that 'val' appears only once in each row, otherwise I would have implemented it using tf.argmax(m == val). In my case, it is important to get the index of the first occurrence of 'val' and not any.

Answer 1

It seems that tf.argmax works like np.argmax (according to the test), which will return the first index when there are multiple occurrences of the max value. You can use tf.argmax(tf.cast(tf.equal(m, val), tf.int32), axis=1) to get what you want. However, currently the behavior of tf.argmax is undefined in case of multiple occurrences of the max value.

If you are worried about undefined behavior, you can apply tf.argmin on the return value of tf.where as @Igor Tsvetkov suggested. For example,

# test with tensorflow r1.0
import tensorflow as tf

val = 3
m = tf.placeholder(tf.int32)
m_feed = [[0  ,   0, val,   0, val],
          [val,   0, val, val,   0],
          [0  , val,   0,   0,   0]]

tmp_indices = tf.where(tf.equal(m, val))
result = tf.segment_min(tmp_indices[:, 1], tmp_indices[:, 0])

with tf.Session() as sess:
    print(sess.run(result, feed_dict={m: m_feed})) # [2, 0, 1]

Note that tf.segment_min will raise InvalidArgumentError when there is some row containing no val. In your code row_elems.index(val) will raise exception too when row_elems don't contain val.

Answer 2

Looks a little ugly but works (assuming m and val are both tensors):

idx = list()
for t in tf.unpack(m, axis=0):
    idx.append(tf.reduce_min(tf.where(tf.equal(t, val))))
idx = tf.pack(idx, axis=0)

EDIT: As Yaroslav Bulatov mentioned, you could achieve the same result with tf.map_fn:

def index1d(t):
    return tf.reduce_min(tf.where(tf.equal(t, val)))

idx = tf.map_fn(index1d, m, dtype=tf.int64)