tf.cond(
pred,
true_fn=None,
false_fn=None,
strict=False,
name=None,
fn1=None,
fn2=None
)
Defined in tensorflow/python/ops/control_flow_ops.py
.
Return true_fn()
if the predicate pred
is true else false_fn()
. (deprecated arguments)
true_fn
and false_fn
both return lists of output tensors. true_fn
and
false_fn
must have the same non-zero number and type of outputs.
WARNING: Any Tensors or Operations created outside of true_fn
and
false_fn
will be executed regardless of which branch is selected at runtime.
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:
z = tf.multiply(a, b)
result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))
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.
This behavior is disabled by passing strict=True
.
Args:
pred
: A scalar determining whether to return the result oftrue_fn
orfalse_fn
.true_fn
: The callable to be performed if pred is true.false_fn
: The callable to be performed if pred is false.strict
: A boolean that enables/disables 'strict' mode; see above.name
: Optional name prefix for the returned tensors.
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
: iftrue_fn
orfalse_fn
is not callable.ValueError
: iftrue_fn
andfalse_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, 17)
def f2(): return tf.add(y, 23)
r = tf.cond(tf.less(x, y), f1, f2)
# r is set to f1().
# Operations in f2 (e.g., tf.add) are not executed.