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tfa.optimizers.MovingAverage

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Class MovingAverage

Optimizer that computes a moving average of the variables.

Aliases:

Empirically it has been found that using the moving average of the trained parameters of a deep network is better than using its trained parameters directly. This optimizer allows you to compute this moving average and swap the variables at save time so that any code outside of the training loop will use by default the average values instead of the original ones.

Example of usage:

opt = tf.keras.optimizers.SGD(learning_rate)
opt = tfa.optimizers.MovingAverage(opt)

__init__

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__init__(
    optimizer,
    average_decay=0.1,
    num_updates=None,
    sequential_update=True,
    name='MovingAverage',
    **kwargs
)

Construct a new MovingAverage optimizer.

Args:

  • optimizer: str or tf.keras.optimizers.Optimizer that will be used to compute and apply gradients.
  • average_decay: float. Decay to use to maintain the moving averages of trained variables. See tf.train.ExponentialMovingAverage for details.
  • num_updates: Optional count of the number of updates applied to variables. See tf.train.ExponentialMovingAverage for details.
  • sequential_update: Bool. If False, will compute the moving average at the same time as the model is updated, potentially doing benign data races. If True, will update the moving average after gradient updates.
  • name: Optional name for the operations created when applying gradients. Defaults to "MovingAverage".
  • **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.

Properties

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'
)

Add a new slot variable for var.

add_weight

add_weight(
    name,
    shape,
    dtype=None,
    initializer='zeros',
    trainable=None,
    synchronization=tf_variables.VariableSynchronization.AUTO,
    aggregation=tf_variables.VariableAggregation.NONE
)

apply_gradients

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apply_gradients(
    grads_and_vars,
    name=None
)

Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

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.

Returns:

An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

Raises:

  • TypeError: If grads_and_vars is malformed.
  • ValueError: If none of the variables have gradients.

assign_average_vars

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assign_average_vars(var_list)

Assign variables in var_list with their respective moving averages.

Example:

model = tf.Sequential([...])
opt = tfa.optimizers.MovingAverage(
    tf.keras.optimizers.SGD(lr=2.0), 0.5)

model.compile(opt, ...)
model.fit(x, y, ...)

# Update the weights to their mean before saving
opt.assign_average_vars(model.variables)

model.save('model.h5')

from_config

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@classmethod
from_config(
    cls,
    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.

Arguments:

  • 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

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get_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.

Returns:

Python dictionary.

get_gradients

get_gradients(
    loss,
    params
)

Returns gradients of loss with respect to params.

Arguments:

  • 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()

minimize

minimize(
    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.

Args:

  • 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.

Returns:

An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step.

Raises:

  • ValueError: If some of the variables are not Variable objects.

set_weights

set_weights(weights)

variables

variables()

Returns variables of this Optimizer based on the order created.