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Optimizer that implements the FTRL algorithm.
Inherits From: Optimizer
tf.keras.optimizers.legacy.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,
beta=0.0,
**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 = (sqrt(n) - sqrt(prev_n)) / 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 rateg
is the gradient for the variablelambda_1
is the L1 regularization strengthlambda_2
is the L2 regularization strength
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, or a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule . The learning rate.
|
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. |
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. 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. |
**kwargs
|
keyword arguments. Allowed arguments are clipvalue ,
clipnorm , global_clipnorm .
If clipvalue (float) is set, the gradient of each weight
is clipped to be no higher than this value.
If clipnorm (float) is set, the gradient of each weight
is individually clipped so that its norm is no higher than this value.
If global_clipnorm (float) is set the gradient of all weights is
clipped so that their global norm is no higher than this value.
|
Reference | |
---|---|
Raises | |
---|---|
ValueError
|
in case of any invalid argument. |
Attributes | |
---|---|
clipnorm
|
float or None . If set, clips gradients to a maximum norm.
|
clipvalue
|
float or None . If set, clips gradients to a maximum value.
|
global_clipnorm
|
float or None .
If set, clips gradients to a maximum norm. Check |
iterations
|
Variable. The number of training steps this Optimizer has run. |
weights
|
Returns variables of this Optimizer based on the order created. |
Methods
add_slot
add_slot(
var, slot_name, initializer='zeros', shape=None
)
Add a new slot variable for var
.
A slot variable is an additional variable associated with var
to
train. It is allocated and managed by optimizers, e.g. Adam
.
Args | |
---|---|
var
|
a Variable object.
|
slot_name
|
name of the slot variable. |
initializer
|
initializer of the slot variable |
shape
|
(Optional) shape of the slot variable. If not set, it will
default to the shape of var .
|
Returns | |
---|---|
A slot variable. |
add_weight
add_weight(
name,
shape,
dtype=None,
initializer='zeros',
trainable=None,
synchronization=tf.VariableSynchronization.AUTO
,
aggregation=tf.VariableAggregation.NONE
)
apply_gradients
apply_gradients(
grads_and_vars, name=None, experimental_aggregate_gradients=True
)
Apply gradients to variables.
This is the second part of minimize()
. It returns an Operation
that
applies gradients.
The method sums gradients from all replicas in the presence of
tf.distribute.Strategy
by default. You can aggregate gradients
yourself by passing experimental_aggregate_gradients=False
.
Example:
grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
experimental_aggregate_gradients=False)
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.
|
experimental_aggregate_gradients
|
Whether to sum gradients from
different replicas in the presence of tf.distribute.Strategy . If
False, it's user responsibility to aggregate the gradients. Default
to True.
|
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. |
RuntimeError
|
If called in a cross-replica context. |
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 Python objects used to create this optimizer, such as a function used for a hyperparameter. |
Returns | |
---|---|
An optimizer instance. |
get_config
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.
Returns | |
---|---|
Python dictionary. |
get_gradients
get_gradients(
loss, params
)
Returns gradients of loss
with respect to params
.
Should be used only in legacy v1 graph mode.
Args | |
---|---|
loss
|
Loss tensor. |
params
|
List of variables. |
Returns | |
---|---|
List of gradient tensors. |
Raises | |
---|---|
ValueError
|
In case any gradient cannot be computed (e.g. if gradient function not implemented). |
get_slot
get_slot(
var, slot_name
)
get_slot_names
get_slot_names()
A list of names for this optimizer's slots.
get_updates
get_updates(
loss, params
)
get_weights
get_weights()
Returns the current weights of the optimizer.
The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.
For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:
opt = tf.keras.optimizers.legacy.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
results = m.fit(data, labels) # Training.
len(opt.get_weights())
3
Returns | |
---|---|
Weights values as a list of numpy arrays. |
minimize
minimize(
loss, var_list, grad_loss=None, name=None, 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. If a Tensor , the
tape argument must be passed.
|
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) str. Name for the returned operation. |
tape
|
(Optional) tf.GradientTape . If loss is provided as a
Tensor , the tape that computed the loss must be provided.
|
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
set_weights(
weights
)
Set the weights of the optimizer.
The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.
For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:
opt = tf.keras.optimizers.legacy.RMSprop()
m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
m.compile(opt, loss='mse')
data = np.arange(100).reshape(5, 20)
labels = np.zeros(5)
results = m.fit(data, labels) # Training.
new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]
opt.set_weights(new_weights)
opt.iterations
<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>
Args | |
---|---|
weights
|
weight values as a list of numpy arrays. |
variables
variables()
Returns variables of this Optimizer based on the order created.