tf.keras.Optimizer

A class for Tensorflow specific optimizer logic.

tf.keras.Optimizer(
    *args, **kwargs
)

The major behavior change for this class is for tf.distribute.

It will override methods from base Keras core Optimizer, which provide distribute specific functionality, e.g. variable creation, loss reduction, etc.

learning_rate

variables

Methods

add_variable

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

add_variable_from_reference

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

Add an all-zeros variable with the shape and dtype of a reference 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

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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

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build(
    variables
)

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

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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

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@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

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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
)

update_step

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update_step(
    gradient, variable, learning_rate
)