tf.cond( pred, true_fn=None, false_fn=None, strict=False, name=None, fn1=None, fn2=None )
See the guide: Control Flow > Control Flow Operations
true_fn() if the predicate
pred is true else
false_fn(). (deprecated arguments)
SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: fn1/fn2 are deprecated in favor of the true_fn/false_fn arguments.
false_fn both return lists of output tensors.
false_fn must have the same non-zero number and type of outputs.
Note that the conditional execution applies only to the operations defined in
false_fn. Consider the following simple program:
z = tf.multiply(a, b) result = tf.cond(x < y, lambda: tf.add(x, z), lambda: tf.square(y))
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
tf.multiply operation is always executed, unconditionally. Although this behavior is consistent with the dataflow model of TensorFlow, it has occasionally surprised some users who expected a lazier semantics.
false_fn exactly once (inside the call to
cond, and not at all during
cond stitches together the graph fragments created during the
false_fn calls with some additional graph nodes to ensure that the right branch gets executed depending on the value of
tf.cond supports nested structures as implemented in
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
false_fn, they are implicitly unpacked to single values. This behavior is disabled by passing
pred: A scalar determining whether to return the result of
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.
Tensors returned by the call to either
false_fn. If the callables return a singleton list, the element is extracted from the list.
false_fnis not callable.
false_fndo not return the same number of tensors, or return tensors of different types.
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.
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Code samples licensed under the Apache 2.0 License.