tf.cond

Return true_fn() if the predicate pred is true else false_fn().

Used in the notebooks

Used in the tutorials

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 name