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An Adagrad variant with adaptive clipping.
tfrs.experimental.optimizers.ClippyAdagrad(
learning_rate: Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001,
initial_accumulator_value: float = 0.1,
variable_relative_threshold: float = 0.1,
accumulator_relative_threshold: float = 0.0,
absolute_threshold: float = 1e-07,
epsilon: float = 1e-07,
export_clipping_factors: bool = False,
clip_accumulator_update: bool = False,
use_standard_accumulator_update: bool = False,
name: str = 'ClippyAdagrad',
**kwargs
) -> None
The adaptive clipping mechanism multiplies the learning rate for each model parameter w by a factor in (0, 1] that ensures that at each iteration w is never changed by more than: |w| * variable_relative_threshold
+ accumulator_relative_threshold / sqrt(accum) + absolute_threshold,
where accum
is the respective Adagrad accumulator.
Args | |
---|---|
learning_rate
|
Initial value for the learning rate: either a floating
point value, or a tf.keras.optimizers.schedules.LearningRateSchedule
instance. Defaults to 0.001. Note that Adagrad tends to benefit from
higher initial learning rate values compared to other optimizers.
To match the exact form in the original paper, use 1.0.
|
initial_accumulator_value
|
A non-negative floating point value. Starting value for the Adagrad accumulators. |
variable_relative_threshold
|
A non-negative floating point value. The relative threshold factor for the adaptive clipping, relatively to the updated variable. |
accumulator_relative_threshold
|
A non-negative floating point value. The clipping threshold factor relatively to the inverse square root of the Adagrad accumulators. Default to 0.0 but a non-negative value (e.g., 1e-3) allows loosening the clipping threshold in later training. |
absolute_threshold
|
A non-negative floating point value. The absolute clipping threshold constant. |
epsilon
|
Small floating point value used to maintain numerical stability. |
export_clipping_factors
|
When set to True, will add an attribute to the
optimizer, called clipping_factors , a list containing the scaling
factors used to clip each variable in the model. Otherwise,
the clipping_factors attribute is an empty list.
|
clip_accumulator_update
|
When set to True, will also apply clipping on the Adagrad accumulator update. This may help improve convergence speed in cases where the gradient contains outliers. This cannot be set to True when use_standard_accumulator_update is set to True. |
use_standard_accumulator_update
|
When set to True, will update the accumulator before calculating the Adagrad step, as in the classical Adagrad method. This cannot be set to True when clip_accumulator_update is set to True. |
name
|
String. The name to use for momentum accumulator weights created by the optimizer. |
**kwargs
|
Other arguments. See tf.keras.optimizers.Optimizer docs.
|
Raises | |
---|---|
ValueError
|
If both clip_accumulator_update and
use_standard_accumulator_update are set to True.
|
Methods
add_variable
add_variable(
shape, dtype=None, initializer='zeros', name=None
)
Create an optimizer variable.
Args | |
---|---|
shape
|
A list of integers, a tuple of integers, or a 1-D Tensor of type int32. Defaults to scalar if unspecified. |
dtype
|
The DType of the optimizer variable to be created. Defaults to
tf.keras.backend.floatx if unspecified.
|
initializer
|
string or callable. Initializer instance. |
name
|
The name of the optimizer variable to be created. |
Returns | |
---|---|
An optimizer variable, in the format of tf.Variable. |
add_variable_from_reference
add_variable_from_reference(
model_variable, variable_name, shape=None, initial_value=None
)
Create an optimizer variable from model variable.
Create an optimizer variable based on the information of model variable. For example, in SGD optimizer momemtum, for each model variable, a corresponding momemtum variable is created of the same shape and dtype.
Args | |
---|---|
model_variable
|
tf.Variable. The corresponding model variable to the optimizer variable to be created. |
variable_name
|
String. The name prefix of the optimizer variable to be
created. The create variables name will follow the pattern
{variable_name}/{model_variable.name} , e.g., momemtum/dense_1 .
|
shape
|
List or Tuple, defaults to None. The shape of the optimizer
variable to be created. If None, the created variable will have the
same shape as model_variable .
|
initial_value
|
A Tensor, or Python object convertible to a Tensor, defaults to None. The initial value of the optimizer variable, if None, the initial value will be default to 0. |
Returns | |
---|---|
An optimizer variable. |
aggregate_gradients
aggregate_gradients(
grads_and_vars
)
Aggregate gradients on all devices.
By default, we will perform reduce_sum of gradients across devices. Users can implement their own aggregation logic by overriding this method.
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs. |
Returns | |
---|---|
List of (gradient, variable) pairs. |
apply_gradients
apply_gradients(
grads_and_vars, name=None, skip_gradients_aggregation=False, **kwargs
)
Apply gradients to variables.
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs.
|
name
|
string, defaults to None. The name of the namescope to
use when creating variables. If None, self.name will be used.
|
skip_gradients_aggregation
|
If true, gradients aggregation will not be performed inside optimizer. Usually this arg is set to True when you write custom code aggregating gradients outside the optimizer. |
**kwargs
|
keyword arguments only used for backward compatibility. |
Returns | |
---|---|
A tf.Variable , representing the current iteration.
|
Raises | |
---|---|
TypeError
|
If grads_and_vars is malformed.
|
RuntimeError
|
If called in a cross-replica context. |
build
build(
var_list: Sequence[tf.Variable]
) -> None
Initialize the optimizer's variables, such as momemtum variables.
This function has to be implemented by subclass optimizers, and subclass
optimizers need to call super().build(var_list)
.
Args | |
---|---|
var_list
|
List of model variables to build optimizers on. For example, SGD optimizer with momentum will store one momentum variable corresponding to each model variable. |
compute_gradients
compute_gradients(
loss, var_list, tape=None
)
Compute gradients of loss on trainable variables.
Args | |
---|---|
loss
|
Tensor or callable. If a callable, loss should take no
arguments and return the value to minimize.
|
var_list
|
list or tuple of Variable objects to update to minimize
loss , or a callable returning the list or tuple of Variable
objects. Use callable when the variable list would otherwise be
incomplete before minimize since the variables are created at the
first time loss is called.
|
tape
|
(Optional) tf.GradientTape . If loss is provided as a
Tensor , the tape that computed the loss must be provided.
|
Returns | |
---|---|
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be None .
|
exclude_from_weight_decay
exclude_from_weight_decay(
var_list=None, var_names=None
)
Exclude variables from weight decay.
This method must be called before the optimizer's build
method is
called. You can set specific variables to exclude out, or set a list of
strings as the anchor words, if any of which appear in a variable's
name, then the variable is excluded.
Args | |
---|---|
var_list
|
A list of tf.Variable s to exclude from weight decay.
|
var_names
|
A list of strings. If any string in var_names appear
in the model variable's name, then this model variable is
excluded from weight decay. For example, var_names=['bias']
excludes all bias variables from weight decay.
|
finalize_variable_values
finalize_variable_values(
var_list
)
Set the final value of model's trainable variables.
Sometimes there are some extra steps before ending the variable updates, such as overriding the model variables with its average value.
Args | |
---|---|
var_list
|
list of model variables. |
from_config
@classmethod
from_config( config, custom_objects=None )
Creates an optimizer from its config.
This method is the reverse of get_config
, capable of instantiating the
same optimizer from the config dictionary.
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
custom_objects
|
A Python dictionary mapping names to additional user-defined Python objects needed to recreate this optimizer. |
Returns | |
---|---|
An optimizer instance. |
get_config
get_config() -> Dict[str, Any]
Returns the config of the optimizer.
An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration.
Subclass optimizer should override this method to include other hyperparameters.
Returns | |
---|---|
Python dictionary. |
load_own_variables
load_own_variables(
store
)
Set the state of this optimizer object.
minimize
minimize(
loss, var_list, tape=None
)
Minimize loss
by updating var_list
.
This method simply computes gradient using tf.GradientTape
and calls
apply_gradients()
. If you want to process the gradient before applying
then call tf.GradientTape
and apply_gradients()
explicitly instead
of using this function.
Args | |
---|---|
loss
|
Tensor or callable. If a callable, loss should take no
arguments and return the value to minimize.
|
var_list
|
list or tuple of Variable objects to update to minimize
loss , or a callable returning the list or tuple of Variable
objects. Use callable when the variable list would otherwise be
incomplete before minimize since the variables are created at the
first time loss is called.
|
tape
|
(Optional) tf.GradientTape .
|
Returns | |
---|---|
None |
save_own_variables
save_own_variables(
store
)
Get the state of this optimizer object.
set_weights
set_weights(
weights
)
Set the weights of the optimizer.
Args | |
---|---|
weights
|
a list of tf.Variable s or numpy arrays, the target values
of optimizer variables. It should have the same order as
self._variables .
|
update_step
update_step(
grad: Union[tf.Tensor, tf.IndexedSlices], variable: tf.Variable
) -> None
Update step given gradient and the associated model variable.