tf.contrib.layers.recompute_grad

Decorator that recomputes the function on the backwards pass.

tf.contrib.layers.recompute_grad(
    fn, use_data_dep=_USE_DEFAULT, tupleize_grads=False
)

To use this function, you must use ResourceVariables (i.e. `variable_scope(name, use_resource=True), which are the default in Eager mode and when running on TPU.

Args
`fn`	a function that takes Tensors (all as positional arguments) and returns a tuple of Tensors. Note that `fn` should not close over any other Tensors or Variables.
`use_data_dep`	`bool`, if `True` will use a dummy data dependency to force the recompute to happen. If `False` will use a control dependency. By default will be `True` if in an XLA context and `False` otherwise. XLA ignores control dependencies and so this data dependency is necessary.
`tupleize_grads`	`bool`, if `True` will use control dependencies to ensure that all gradients are produced before any are consumed by downstream ops. If `use_data_dep` is also `True`, will use a data dependency instead of a control dependency.

Returns
A wrapped fn that is identical to fn when called, but its activations will be discarded and recomputed on the backwards pass (i.e. on a call to tf.gradients).

Raises
`ValueError`	if `fn` closes over any Tensors or Variables.

tf.contrib.layers.recompute_grad

Args

Returns

Raises