tf.keras.optimizers.Adagrad

Optimizer that implements the Adagrad algorithm.

Inherits From: Optimizer

tf.keras.optimizers.Adagrad(
    learning_rate=0.001,
    initial_accumulator_value=0.1,
    epsilon=1e-07,
    weight_decay=None,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    loss_scale_factor=None,
    gradient_accumulation_steps=None,
    name='adagrad',
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives, the smaller the updates.

learning_rate A float, a keras.optimizers.schedules.LearningRateSchedule instance, or a callable that takes no arguments and returns the actual value to use. The learning rate. Defaults to 0.001. Note that Adagrad tends to benefit from higher initial learning rate values compared to other optimizers. To match the exact form in the original paper, use 1.0.
initial_accumulator_value Floating point value. Starting value for the accumulators (per-parameter momentum values). Must be non-negative.
epsilon Small floating point value for maintaining numerical stability.
name String. The name to use for momentum accumulator weights created by the optimizer.
weight_decay Float. If set, weight decay is applied.
clipnorm Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
clipvalue Float. If set, the gradient of each weight is clipped to be no higher than this value.
global_clipnorm Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
use_ema Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
ema_momentum Float, defaults to 0.99. Only used if use_ema=True. This is the momentum to use when computing the EMA of the model's weights: new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value.
ema_overwrite_frequency Int or None, defaults to None. Only used if use_ema=True. Every ema_overwrite_frequency steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling optimizer.finalize_variable_values() (which updates the model variables in-place). When using the built-in fit() training loop, this happens automatically after the last epoch, and you don't need to do anything.
loss_scale_factor Float or None. If a float, the scale factor will be multiplied the loss before computing gradients, and the inverse of the scale factor will be multiplied by the gradients before updating variables. Useful for preventing underflow during mixed precision training. Alternately, keras.optimizers.LossScaleOptimizer will automatically set a loss scale factor.
gradient_accumulation_steps Int or None. If an int, model & optimizer variables will not be updated at every step; instead they will be updated every gradient_accumulation_steps steps, using the average value of the gradients since the last update. This is known as "gradient accumulation". This can be useful when your batch size is very small, in order to reduce gradient noise at each update step.

Reference:

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(
    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

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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
)

Update step given gradient and the associated model variable.