tf.keras.optimizers.experimental.Ftrl

Optimizer that implements the FTRL algorithm.

Inherits From: Optimizer

View aliases

Main aliases

tf.optimizers.experimental.Ftrl

tf.keras.optimizers.experimental.Ftrl(
    learning_rate=0.001,
    learning_rate_power=-0.5,
    initial_accumulator_value=0.1,
    l1_regularization_strength=0.0,
    l2_regularization_strength=0.0,
    l2_shrinkage_regularization_strength=0.0,
    beta=0.0,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    jit_compile=True,
    name='Ftrl',
    **kwargs
)

"Follow The Regularized Leader" (FTRL) is an optimization algorithm developed at Google for click-through rate prediction in the early 2010s. It is most suitable for shallow models with large and sparse feature spaces. The algorithm is described by McMahan et al., 2013. The Keras version has support for both online L2 regularization (the L2 regularization described in the paper above) and shrinkage-type L2 regularization (which is the addition of an L2 penalty to the loss function).

Initialization:

n = 0
sigma = 0
z = 0

Update rule for one variable w:

prev_n = n
n = n + g ** 2
sigma = (n ** -lr_power - prev_n ** -lr_power) / lr
z = z + g - sigma * w
if abs(z) < lambda_1:
  w = 0
else:
  w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)

Notation:

lr is the learning rate
g is the gradient for the variable
lambda_1 is the L1 regularization strength
lambda_2 is the L2 regularization strength
lr_power is the power to scale n.

Check the documentation for the l2_shrinkage_regularization_strength parameter for more details when shrinkage is enabled, in which case gradient is replaced with a gradient with shrinkage.

Args
`learning_rate`	A `Tensor`, floating point value, a schedule that is a `tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.001.
`learning_rate_power`	A float value, must be less or equal to zero. Controls how the learning rate decreases during training. Use zero for a fixed learning rate.
`initial_accumulator_value`	The starting value for accumulators. Only zero or positive values are allowed.
`l1_regularization_strength`	A float value, must be greater than or equal to zero. Defaults to 0.0.
`l2_regularization_strength`	A float value, must be greater than or equal to zero. Defaults to 0.0.
`l2_shrinkage_regularization_strength`	A float value, must be greater than or equal to zero. This differs from L2 above in that the L2 above is a stabilization penalty, whereas this L2 shrinkage is a magnitude penalty. When input is sparse shrinkage will only happen on the active weights.
`beta`	A float value, representing the beta value from the paper. Defaults to 0.0.
`name`	String. The name to use for momentum accumulator weights created by the optimizer.
`clipnorm`	Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
`clipvalue`	Float. If set, the gradient of each weight is clipped to be no higher than this value.
`global_clipnorm`	Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
`use_ema`	Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
`ema_momentum`	Float, defaults to 0.99. Only used if `use_ema=True`. This is # noqa: E501 the momentum to use when computing the EMA of the model's weights: `new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value`.
`ema_overwrite_frequency`	Int or None, defaults to None. Only used if `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer # noqa: E501 does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling `optimizer.finalize_variable_values()` (which updates the model # noqa: E501 variables in-place). When using the built-in `fit()` training loop, this happens automatically after the last epoch, and you don't need to do anything.
`jit_compile`	Boolean, defaults to True. If True, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.
`**kwargs`	keyword arguments only used for backward compatibility.

Attributes
`iterations`	The number of training steps this `optimizer` has run. By default, iterations would be incremented by one every time `apply_gradients()` is called.
`learning_rate`

Attributes

iterations

The number of training steps this optimizer has run.

By default, iterations would be incremented by one every time apply_gradients() is called.

learning_rate

Methods

`add_variable`

View source

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`

View source

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`

View source

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`

View source

apply_gradients(
    grads_and_vars, skip_gradients_aggregation=False
)

Apply gradients to variables.

Args
`grads_and_vars`	List of (gradient, variable) pairs.
`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.

Returns
None

Raises
`TypeError`	If `grads_and_vars` is malformed.
`RuntimeError`	If called in a cross-replica context.

`build`

View source

build(
    var_list
)

Initialize optimizer variables.

Args
`var_list`	list of model variables to build Ftrl variables on.

`compute_gradients`

View source

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`.
`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`.

`finalize_variable_values`

View source

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`

View source

@classmethod
from_config(
    config
)

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.

Returns
An optimizer instance.

`get_config`

View source

get_config()

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.

`minimize`

View source

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`.
`tape`	(Optional) `tf.GradientTape`.

Returns
None

`update_step`

View source

update_step(
    gradient, variable
)

Update step given gradient and the associated model variable.

tf.keras.optimizers.experimental.Ftrl

View aliases

Initialization:

Notation:

Args

Attributes

Methods

add_variable

add_variable_from_reference

aggregate_gradients

apply_gradients

build

compute_gradients

finalize_variable_values

from_config

get_config

minimize

update_step

`add_variable`

`add_variable_from_reference`

`aggregate_gradients`

`apply_gradients`

`build`

`compute_gradients`

`finalize_variable_values`

`from_config`

`get_config`

`minimize`

`update_step`