Function cond

tensorflow/python/ops/control_flow_ops.py:1099–1293

Return `true_fn()` if the predicate `pred` is true else `false_fn()`. `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. **WARNING**: Any Tensors or Operations created outside of `true_fn` and `fals

(pred,
         true_fn=None,
         false_fn=None,
         strict=False,
         name=None,
         fn1=None,
         fn2=None)

    None, "fn1/fn2 are deprecated in favor of the true_fn/false_fn arguments.",
    "fn1", "fn2")
def cond(pred,
         true_fn=None,
         false_fn=None,
         strict=False,
         name=None,
         fn1=None,
         fn2=None):
  """Return `true_fn()` if the predicate `pred` is true else `false_fn()`.

  `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.

  **WARNING**: Any Tensors or Operations created outside of `true_fn` and
  `false_fn` will be executed regardless of which branch is selected at runtime.

  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:

  ```python
  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.
  This behavior is disabled by passing `strict=True`.

  Args:
    pred: A scalar determining whether to return the result of `true_fn` or
      `false_fn`.
    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.

  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.

  Raises:
    TypeError: if `true_fn` or `false_fn` is not callable.
    ValueError: if `true_fn` and `false_fn` do not return the same number of
      tensors, or return tensors of different types.