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Represents an optimizer for use in TensorFlow Federated.
Its pair of initialize and next methods define the optimization
algorithm, with next corresponding to a step of the optimizer.
This class captures iterative optimization algorithms where the same operation
is applied in every optimization step. The next method should be a
computation that can be implemented as a tf.function. Each method will
generally only be traced once to create the corresponding TensorFlow graph
functions. Thus, the methods should not create or use tf.Variable objects.
Instead, any dependency between steps of the algorithm should be included
as tensors in a state. For instance, a momentum term for momentum SGD is
created in the initialize method as all-zeros tensor, which is then both
an input and an output of the next method. These aspects should be accessed
and changed via get_hparams and set_hparams, respectively.
As a best practice, any implementation using learning rate, should store it in
its state under the key tff.learning.optimizers.LEARNING_RATE_KEY.
Methods
get_hparams
get_hparams(
state: State
) -> Hparams
Returns a dictionary containing the optimizer state hyperparameters.
| Args | |
|---|---|
state
|
The state of the optimizer. Must match the structure returned by
the initialize method.
|
| Returns | |
|---|---|
| An ordered dictionary representing the hyperparameters in the given state. |
initialize
@abc.abstractmethodinitialize( specs: Any ) -> State
Returns the initial state of the optimizer.
| Args | |
|---|---|
specs
|
A (possibly nested) structure of tf.TensorSpecs describing the
weights to be optimized. The weights and grads argument of next
must match the structure and (shape, dtype) of specs.
|
| Returns | |
|---|---|
| Initial state of the optimizer. A (possibly nested) structure of tensors. |
next
@abc.abstractmethodnext( state: State, weights: Weights, gradients: Any ) -> tuple[State, Weights]
Takes a step of the optimizer.
| Args | |
|---|---|
state
|
State of the optimizer. A structure of tensors matching the
structure returned by initialize method.
|
weights
|
The weights to be updated by the optimizer. A collection of
tensors matching the structure of specs provided in the initialize
method.
|
gradients
|
The gradients to use for the update by the optimizer. A
collection of tensors matching the structure of specs provided in the
initialize method.
|
| Returns | |
|---|---|
A (state, weights) tuple representing the updated state and weights.
|
set_hparams
set_hparams(
state: State, hparams: Hparams
) -> State
Returns an optimizer state with updated hyperparameters.
| Args | |
|---|---|
state
|
The state of the optimizer. Must match the structure returned by
the initialize method.
|
hparams
|
A dictionary matching the output of get_hparams containing the
updated hyperparameters to use.
|
| Returns | |
|---|---|
| An ordered dictionary representing the hyperparameters in the given state. |