tf.keras.optimizers.MultiOptimizer

An optimizer wrapper that delegates variables to different optimizers.

Inherits From: Optimizer

tf.keras.optimizers.MultiOptimizer(
    optimizer_map, loss_scale_factor=None, name=None
)

Initialize the object with an OptimizerMap instance or a callable function that returns an optimizer for a given variable.

model.compile( optimizer=MultiOptimizer( OptimizerMap(default_optimizer=optimizers.SGD(), {"encoder/.*": optimizers.Adam()}) ), loss="binary_crossentropy", )

Or using a callable

def optimizer_selector(variable): if "encoder" in variable.path: return optimizers.Adam() else: return optimizers.SGD()

model.compile( optimizer=MultiOptimizer(optimizer_selector), loss="binary_crossentropy", )

To access the attributes of the sub-optimizers, iterate over the optimizers using .optimizers:

For example:

optimizer = MultiOptimizer(OptimizerMap( default_optimizer=optimizers.Adam() )) optimizer['.encoder'] = optimizers.SGD()

for optim in optimizer.optimizers: print(optim.learning_rate) print(optim.iterations) print(optim.loss_scale_factor) ...

The MultiOptimizer class instances will not expose learning_rate attribute and will raise an error if accessed. This is because the learning rate might be different for different sub-optimizers.

optimizer_map An OptimizerMap instance or a callable function that returns an optimizer for a given variable.
loss_scale_factor It overrides the loss_scale_factor passed to the sub-optimizers.
name The name of the optimizer.

iterations

learning_rate

loss_scale_factor

optimizers

variables

Methods

add_optimizer_variables

View source

add_optimizer_variables(
    trainable_variables, name, initializer='zeros'
)

Add optimizer variables from the list of trainable model variables.

Create an optimizer variable based on the information of the supplied model variables. For example, in SGD optimizer momemtum, for each model variable, a corresponding momemtum variable is created of the same shape and dtype.

Note that trainable variables with v.overwrite_with_gradient == True will insert None, into the output list, since the optimizer variable will not be used anyways, and could be wasteful.

Args
trainable_variables keras.Variable, the corresponding model variable to the optimizer variable to be created.
name The name prefix(es) of the optimizer variable(s) to be created. Can be a single string or list of strings. If a list of strings, will create an optimizer variable for each prefix. The variable name will follow the pattern {variable_name}_{trainable_variable.name}, e.g., momemtum/dense_1.
initializer Initializer object(s) to use to populate the initial variable value(s), or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "zeros".

Returns
A list of optimizer variables, in the format of keras.Variables. If multiple names are provide, returns a tuple of lists.

add_variable

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add_variable(
    shape,
    initializer='zeros',
    dtype=None,
    aggregation='none',
    layout=None,
    name=None
)

Add a variable to the optimizer.

Args
shape Shape tuple for the variable. Must be fully-defined (no None entries).
initializer Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). Defaults to "zeros".
dtype Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the keras.backend.floatx().
aggregation Optional string, one of None, "none", "mean", "sum" or "only_first_replica". Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops. Defaults to "none".
layout Optional tensor layout. Defaults to None.
name String name of the variable. Useful for debugging purposes.

Returns
An optimizer variable, in the format of keras.Variable.

add_variable_from_reference

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add_variable_from_reference(
    reference_variable, name=None, initializer='zeros'
)

Add an optimizer variable from the 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
reference_variable keras.Variable. The corresponding model variable to the optimizer variable to be created.
name Optional string. The name prefix of the optimizer variable to be created. If not provided, it will be set to "var". The variable name will follow the pattern {variable_name}_{reference_variable.name}, e.g., momemtum/dense_1. Defaults to None.
initializer Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "zeros".

Returns
An optimizer variable, in the format of keras.Variable.

apply

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apply(
    grads, trainable_variables=None
)

Update traininable variables according to provided gradient values.

grads should be a list of gradient tensors with 1:1 mapping to the list of variables the optimizer was built with.

trainable_variables can be provided on the first call to build the optimizer.

apply_gradients

View source

apply_gradients(
    grads_and_vars
)

assign

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assign(
    variable, value
)

Assign a value to a variable.

This should be used in optimizers instead of variable.assign(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

assign_add

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assign_add(
    variable, value
)

Add a value to a variable.

This should be used in optimizers instead of variable.assign_add(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

assign_sub

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assign_sub(
    variable, value
)

Subtract a value from a variable.

This should be used in optimizers instead of variable.assign_sub(value) to support backend specific optimizations. Note that the variable can be a model variable or an optimizer variable; it can be a backend native variable or a Keras variable.

Args
variable The variable to update.
value The value to add to the variable.

build

View source

build(
    var_list
)

exclude_from_weight_decay

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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 Variables 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

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, 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

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.

load_own_variables

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load_own_variables(
    store
)

Set the state of this optimizer object.

save_own_variables

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save_own_variables(
    store
)

Get the state of this optimizer object.

scale_loss

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scale_loss(
    loss
)

Scale the loss before computing gradients.

Scales the loss before gradients are computed in a train_step. This is primarily useful during mixed precision training to prevent numeric underflow.

set_weights

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set_weights(
    weights
)

Set the weights of the optimizer.

stateless_apply

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stateless_apply(
    optimizer_variables, grads, trainable_variables
)

Stateless version of apply that returns modified variables.

Args
optimizer_variables list of tensors containing the current values for the optimizer variables. These are native tensors and not keras.Variables.
grads list of gradients to apply.
trainable_variables list of tensors containing the current values for the model variables. These are native tensors and not keras.Variables.

Returns: A tuple containing two list of tensors, the updated trainable_variables and the updated optimizer_variables.

update_step

View source

update_step(
    gradient, variable, learning_rate
)