tfa.optimizers.RectifiedAdam

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Class RectifiedAdam

Variant of the Adam optimizer whose adaptive learning rate is rectified

Aliases:

so as to have a consistent variance.

It implements the Rectified Adam (a.k.a. RAdam) proposed by Liyuan Liu et al. in On The Variance Of The Adaptive Learning Rate And Beyond.

Example of usage:

opt = tfa.optimizers.RectifiedAdam(lr=1e-3)

RAdam is not a placement of the heuristic warmup, the settings should be kept if warmup has already been employed and tuned in the baseline method. You can enable warmup by setting total_steps and warmup_proportion:

opt = tfa.optimizers.RectifiedAdam(
    lr=1e-3,
    total_steps=10000,
    warmup_proportion=0.1,
    min_lr=1e-5,
)

In the above example, the learning rate will increase linearly from 0 to lr in 1000 steps, then decrease linearly from lr to min_lr in 9000 steps.

Lookahead, proposed by Michael R. Zhang et.al in the paper Lookahead Optimizer: k steps forward, 1 step back, can be integrated with RAdam, which is announced by Less Wright and the new combined optimizer can also be called "Ranger". The mechanism can be enabled by using the lookahead wrapper. For example:

radam = tfa.optimizers.RectifiedAdam()
ranger = tfa.optimizers.Lookahead(radam, sync_period=6, slow_step_size=0.5)

__init__

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__init__(
    learning_rate=0.001,
    beta_1=0.9,
    beta_2=0.999,
    epsilon=1e-07,
    weight_decay=0.0,
    amsgrad=False,
    sma_threshold=5.0,
    total_steps=0,
    warmup_proportion=0.1,
    min_lr=0.0,
    name='RectifiedAdam',
    **kwargs
)

Construct a new RAdam optimizer.

Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • beta_1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
  • beta_2: A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
  • epsilon: A small constant for numerical stability.
  • weight_decay: A floating point value. Weight decay for each param.
  • amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". sma_threshold. A float value. The threshold for simple mean average.
  • total_steps: An integer. Total number of training steps. Enable warmup by setting a positive value.
  • warmup_proportion: A floating point value. The proportion of increasing steps.
  • min_lr: A floating point value. Minimum learning rate after warmup.
  • name: Optional name for the operations created when applying gradients. Defaults to "RectifiedAdam".
  • **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

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.

from_config

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

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set_weights(weights)

variables

variables()

Returns variables of this Optimizer based on the order created.