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Create a switch/case operation, i.e.
tf.switch_case(
branch_index, branch_fns, default=None, name='switch_case'
)
an integerindexed conditional.
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 dict from int
to callables, or 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) { // cstyle 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})
Returns  

The tensors returned by the callable identified by branch_index , or those
returned by default if no key matches and default was provided, or those
returned by the maxkeyed branch_fn if no default is provided.
