Multi tool use

Why use constant i in `tf.while_loop` as `loop_vars`?

The while_loop is like this:

i = tf.constant(0)
c = lambda i: tf.less(i, 10)
b = lambda i: tf.add(i, 1)
r = tf.while_loop(c, b, [i])


i is used as a incremental variable. So, i is changeable.
Why we define i as a constant?
Why not i = tf.Variable(0, tf.int32)

i

i

i

i = tf.Variable(0, tf.int32)

2 Answers
2

i is used as a incremental variable. So, i is changeable.

i

i

Not quite true — if it was, i could not be a constant.

i

tf.add(i, 1) does not change i, it takes tensor i and creates a new tensor by adding 1 to it. (In practice, tensorflow is likely to reuse the same memory allocation for the resulting tensor, but that is an optimization irrelevant to the logic of tf.where).

tf.add(i, 1)

i

i

tf.where

You may be confused because the same name i is used in lambdas, but all those tensors are different tensors corresponding to the output of operations at each iteration.

i

So, the i that you declare is actually the first value of your iteration (same goes for all the other values in the loop, not only for your counter). It is indeed a constant, 0, so it makes sense to declare it as such. It would be awkward to declare your initial value 0 as a Variable.

i

Variable

tf.Variable can be used to create the same type of code.

The main properties of tf.Variable I understand is this. Reference is this

tf.Variable has to be initialized before it is used by using tf.assign or executing an initializer or by loading its saved state from a file.

Constant

i = tf.constant(0)
c = lambda i: tf.less(i, 11)
b = lambda i: tf.add(i, 1)
r = tf.while_loop(c, b, [i])
print (sess.run(r))


Variable

i = tf.Variable(0, tf.int32)
c = lambda i: tf.less(i, 11)
b = lambda i: tf.add(i, 1)
r = tf.while_loop(c, b, [i])
init = tf.variables_initializer([i])
sess.run(init)
print (sess.run(r))


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