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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).
$$t = 0$$ $$n_{0} = 0$$ $$\sigma_{0} = 0$$ $$z_{0} = 0$$
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.
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} = argmin_w(\hat{g}_{1:t}w + L1*||w||_1 + L2*||w||_22), where
\hat{g} = g + (2*L2_shrinkage*w), 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 + 2*L2*lr_t) * g_t -
2*L2_shrinkage*lr_t / (1 + 2*L2*lr_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.iterations: Variable. The number of training steps this Optimizer has run.weights: Returns variables of this Optimizer based on the order created.ValueError: If one of the arguments is invalid.add_slotadd_slot(
var, slot_name, initializer='zeros'
)
Add a new slot variable for var.
add_weightadd_weight(
name, shape, dtype=None, initializer='zeros', trainable=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.compat.v1.VariableAggregation.NONE
)
apply_gradientsapply_gradients(
grads_and_vars, name=None
)
Apply gradients to variables.
This is the second part of minimize(). It returns an Operation that
applies gradients.
grads_and_vars: List of (gradient, variable) pairs.name: Optional name for the returned operation. Default to the name
passed to the Optimizer constructor.An Operation that applies the specified gradients. The iterations
will be automatically increased by 1.
TypeError: If grads_and_vars is malformed.ValueError: If none of the variables have gradients.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.
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.An optimizer instance.
get_configget_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.
Python dictionary.
get_gradientsget_gradients(
loss, params
)
Returns gradients of loss with respect to params.
loss: Loss tensor.params: List of variables.List of gradient tensors.
ValueError: In case any gradient cannot be computed (e.g. if gradient
function not implemented).get_slotget_slot(
var, slot_name
)
get_slot_namesget_slot_names()
A list of names for this optimizer's slots.
get_updatesget_updates(
loss, params
)
get_weightsget_weights()
minimizeminimize(
loss, var_list, grad_loss=None, name=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.
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.An Operation that updates the variables in var_list. The iterations
will be automatically increased by 1.
ValueError: If some of the variables are not Variable objects.set_weightsset_weights(
weights
)
variablesvariables()
Returns variables of this Optimizer based on the order created.