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Return true_fn()
if the predicate pred
is true else false_fn()
.
tf.cond(
pred, true_fn=None, false_fn=None, name=None
)
@tf.function
def fun1(x,y):
if x > 0: # AutoGraph converts ifstatement to tf.cond().
z = y+1
else:
z = y1
return z
fun1(tf.constant(7), tf.constant(3)).numpy()
4
@tf.function
def fun2(x,y):
pred = x > 0
true_fn = lambda: y+1
false_fn = lambda: y1
return tf.cond(pred, true_fn, false_fn) # Use tf.cond() explicitly.
fun1(tf.constant(7), tf.constant(3)).numpy()
4
For more information, see tf.function and AutoGraph guide.
true_fn
and false_fn
both return lists of output tensors. true_fn
and
false_fn
must have the same nonzero number and type of outputs.
Although this behavior is consistent with the dataflow model of TensorFlow, it has frequently surprised users who expected a lazier semantics. Consider the following simple program:
x, y = tf.constant(2, dtype=tf.int32), tf.constant(4, dtype=tf.int32)
z = tf.multiply(x, y)
r = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))
r.numpy()
10
If x < y
, the tf.add
operation will be executed and tf.square
operation will not be executed. Since z
is needed for at least one
branch of the cond
, the tf.multiply
operation is always executed,
unconditionally.
Note that cond
calls true_fn
and false_fn
exactly once (inside the
call to cond
, and not at all during Session.run()
). cond
stitches together the graph fragments created during the true_fn
and
false_fn
calls with some additional graph nodes to ensure that the right
branch gets executed depending on the value of pred
.
tf.cond
supports nested structures as implemented in
tensorflow.python.util.nest
. Both true_fn
and false_fn
must return the
same (possibly nested) value structure of lists, tuples, and/or named tuples.
Singleton lists and tuples form the only exceptions to this: when returned by
true_fn
and/or false_fn
, they are implicitly unpacked to single values.
Returns  

Tensors returned by the call to either true_fn or false_fn . If the
callables return a singleton list, the element is extracted from the list.

Raises  

TypeError

if true_fn or false_fn is not callable.

ValueError

if true_fn and false_fn do not return the same number of
tensors, or return tensors of different types.

Example:
x = tf.constant(2)
y = tf.constant(5)
def f1(): return tf.multiply(x, 7)
def f2(): return tf.add(y, 3)
r = tf.cond(tf.less(x, y), f1, f2)
# r is set to f1().
# Operations in f2 (e.g., tf.add) are not executed.
r.numpy()
14