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# tf.keras.optimizers.Ftrl

Optimizer that implements the FTRL algorithm.

Inherits From: Optimizer

tf.keras.optimizers.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, name='Ftrl',
l2_shrinkage_regularization_strength=0.0, **kwargs
)


See Algorithm 1 of this paper. This version has support for both online L2 (the L2 penalty given in the paper above) and shrinkage-type L2 (which is the addition of an L2 penalty to the loss function).

#### Initialization:

$$t = 0$$
$$n_{0} = 0$$
$$\sigma_{0} = 0$$
$$z_{0} = 0$$

Update (

$$i$$
is variable index):
$$t = t + 1$$
$$n_{t,i} = n_{t-1,i} + g_{t,i}^{2}$$
$$\sigma_{t,i} = (\sqrt{n_{t,i} } - \sqrt{n_{t-1,i} }) / \alpha$$
$$z_{t,i} = z_{t-1,i} + g_{t,i} - \sigma_{t,i} * w_{t,i}$$
$$w_{t,i} = - ((\beta+\sqrt{n+{t} }) / \alpha + \lambda_{2})^{-1} * (z_{i} - sgn(z_{i}) * \lambda_{1}) if \abs{z_{i} } > \lambda_{i} else 0$$

Check the documentation for the l2_shrinkage_regularization_strength parameter for more details when shrinkage is enabled, where gradient is replaced with gradient_with_shrinkage.

#### Args:

• learning_rate: A float value or a constant float Tensor.
• 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.
• l2_regularization_strength: A float value, must be greater than or equal to zero.
• name: Optional name prefix for the operations created when applying gradients. Defaults to "Ftrl".
• 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. The FTRL formulation can be written as: w_{t+1} = argminw(\hat{g}{1:t}w + L1||w||_1 + L2||w||_2^2), where \hat{g} = g + (2L2_shrinkagew), and g is the gradient of the loss function w.r.t. the weights w. Specifically, in the absence of L1 regularization, it is equivalent to the following update rule: w_{t+1} = w_t - lr_t / (1 + 2L2lr_t) * g_t - 2L2_shrinkagelr_t / (1 + 2L2lr_t) * w_t where lr_t is the learning rate at t. When input is sparse shrinkage will only happen on the active weights.\
• **kwargs: keyword arguments. Allowed to be {clipnorm, clipvalue, lr, decay}. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate. lr is included for backward compatibility, recommended to use learning_rate instead.

#### Attributes:

• iterations: Variable. The number of training steps this Optimizer has run.
• weights: Returns variables of this Optimizer based on the order created.

#### Raises:

• ValueError: If one of the arguments is invalid.

## Methods

### add_slot

View source

add_slot(
var, slot_name, initializer='zeros'
)


Add a new slot variable for var.

### add_weight

View source

add_weight(
name, shape, dtype=None, initializer='zeros', trainable=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.compat.v1.VariableAggregation.NONE
)


### apply_gradients

View source

apply_gradients(
)


This is the second part of minimize(). It returns an Operation that applies gradients.

#### Args:

• grads_and_vars: List of (gradient, variable) pairs.
• name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

#### Returns:

An Operation that applies the specified gradients. The iterations will be automatically increased by 1.

#### Raises:

• TypeError: If grads_and_vars is malformed.
• ValueError: If none of the variables have gradients.

### from_config

View source

@classmethod
from_config(
cls, 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.

#### Arguments:

• config: A Python dictionary, typically the output of get_config.
• custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter.

#### Returns:

An optimizer instance.

### get_config

View source

get_config()


Returns the config of the optimimizer.

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.

#### Returns:

Python dictionary.

### get_gradients

View source

get_gradients(
loss, params
)


Returns gradients of loss with respect to params.

#### Arguments:

• loss: Loss tensor.
• params: List of variables.

#### Raises:

• ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

### get_slot

View source

get_slot(
var, slot_name
)


### get_slot_names

View source

get_slot_names()


A list of names for this optimizer's slots.

### get_updates

View source

get_updates(
loss, params
)


### get_weights

View source

get_weights()


### minimize

View source

minimize(
)


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: A callable taking no arguments which returns 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.
• grad_loss: Optional. A Tensor holding the gradient computed for loss.
• name: Optional name for the returned operation.

#### Returns:

An Operation that updates the variables in var_list. The iterations will be automatically increased by 1.

#### Raises:

• ValueError: If some of the variables are not Variable objects.

### set_weights

View source

set_weights(
weights
)


### variables

View source

variables()


Returns variables of this Optimizer based on the order created.