tf.switch_case

Create a switch/case operation, i.e. an integer-indexed conditional.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.switch_case, `tf.compat.v2.switch_case`

tf.switch_case(
    branch_index, branch_fns, default=None, name='switch_case'
)

See also tf.case.

This op can be substantially more efficient than tf.case when exactly one branch will be selected. tf.switch_case is more like a C++ switch/case statement than tf.case, which is more like an if/elif/elif/else chain.

The branch_fns parameter is either a list of (int, callable) pairs, or simply a list of callables (in which case the index is implicitly the key). The branch_index Tensor is used to select an element in branch_fns with matching int key, falling back to default if none match, or max(keys) if no default is provided. The keys must form a contiguous set from 0 to len(branch_fns) - 1.

tf.switch_case supports nested structures as implemented in tf.nest. All callables must return the same (possibly nested) value structure of lists, tuples, and/or named tuples.

Example:

Pseudocode:

switch (branch_index) {  // c-style switch
  case 0: return 17;
  case 1: return 31;
  default: return -1;
}

or

branches = {0: lambda: 17, 1: lambda: 31}
branches.get(branch_index, lambda: -1)()

Expressions:

def f1(): return tf.constant(17)
def f2(): return tf.constant(31)
def f3(): return tf.constant(-1)
r = tf.switch_case(branch_index, branch_fns={0: f1, 1: f2}, default=f3)
# Equivalent: tf.switch_case(branch_index, branch_fns={0: f1, 1: f2, 2: f3})

V2 Compatibility

branch_fns could be a dictionary in v1. However, tf.Tensor and tf.Variable are no longer hashable in v2, so cannot be used as a key for a dictionary. Please use a list or a tuple instead.