What's the difference between tf.cond and if-else?

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What difference between tf.cond and if-else?

Scenario 1

import tensorflow as tf

x = 'x'
y = tf.cond(tf.equal(x, 'x'), lambda: 1, lambda: 0)
with tf.Session() as sess:
    print(sess.run(y))
x = 'y'
with tf.Session() as sess:
    print(sess.run(y))

Scenario 2

import tensorflow as tf

x = tf.Variable('x')
y = tf.cond(tf.equal(x, 'x'), lambda: 1, lambda: 0)
init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    print(sess.run(y))

tf.assign(x, 'y')
with tf.Session() as sess:
    init.run()
    print(sess.run(y))

The outputs are both 1.

Does it mean only tf.placeholder can work, and not all the tensor, such as tf.variable? When should I choose if-else condition and when to use tf.cond? What are the diffences between them?

tensorflow
4 Answers

Since the graph in TensorFlow is static, you cannot modify it once built. Thus you can use if-else outside of the graph at anytime for example while preparing batches and etc., but you can also employ it while constructing the graph. That is, if the condition doesn't depend on the value of any tensor, for example the dimention(having been set) of the tensor or the shape of any tensor. In such scenarios the graph will not be changed due to the condition while excuting the graph. The graph has been fixed after you finished drawing the graph and the if-else condition would not affect the graph while excuting the graph.

But if the condition depends on the value of the tensor in it that condition should be included in the graph and hence tf.cond should be applied.

user_35529750
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