TensorFlow 1 version
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View source on GitHub
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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
)
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.
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.
Args | |
|---|---|
pred
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A scalar determining whether to return the result of true_fn or
false_fn.
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true_fn
|
The callable to be performed if pred is true. |
false_fn
|
The callable to be performed if pred is false. |
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.
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Raises | |
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TypeError
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if true_fn or false_fn is not callable.
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ValueError
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if true_fn and false_fn do not return the same number of
tensors, or return tensors of different types.
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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.