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tf.keras.mixed_precision.experimental.LossScaleOptimizer

Class LossScaleOptimizer

An optimizer that applies loss scaling.

Inherits From: Optimizer

Aliases:

  • Class tf.compat.v1.keras.mixed_precision.experimental.LossScaleOptimizer
  • Class tf.compat.v2.keras.mixed_precision.experimental.LossScaleOptimizer
  • Class tf.keras.mixed_precision.experimental.LossScaleOptimizer
View source on GitHub

Loss scaling is a process that multiplies the loss by a multiplier called the loss scale, and divides each gradient by the same multiplier. The pseudocode for this process is:

loss = ...
loss *= loss_scale
grads = gradients(loss, vars)
grads /= loss_scale

Mathematically, loss scaling has no effect, but can help avoid numerical underflow in intermediate gradients when float16 tensors are used. By multiplying the loss, each intermediate gradient will have the same multiplier applied.

The loss scale can either be a fixed constant, chosen by the user, or be dynamically determined. Dynamically determining the loss scale is convenient as a loss scale does not have to be explicitly chosen. However it reduces performance.

This optimizer wraps another optimizer and applies loss scaling to it via a LossScale. Loss scaling is applied whenever gradients are computed, either through minimize() or get_gradients().

__init__

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__init__(
    opt,
    loss_scale
)

Initializes this loss scale optimizer.

Args:

  • opt: The Optimizer instance to wrap.
  • loss_scale: The loss scale to scale the loss and gradients. This can either be an int/float to use a fixed loss scale, the string "dynamic" to use dynamic loss scaling, or an instance of a LossScale. The string "dynamic" equivalent to passing DynamicLossScale(), and passing an int/float is equivalent to passing a FixedLossScale with the given loss scale.

Properties

iterations

Variable. The number of training steps this Optimizer has run.

learning_rate

weights

Returns variables of this Optimizer based on the order created.

Methods

add_slot

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add_slot(
    var,
    slot_name,
    initializer='zeros'
)

Add a new slot variable for var.

add_weight

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

apply_gradients

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apply_gradients(
    grads_and_vars,
    name=None
)

from_config

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@classmethod
from_config(
    cls,
    config,
    custom_objects=None
)

get_config

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get_config()

get_gradients

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get_gradients(
    loss,
    params
)

get_slot

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get_slot(
    var,
    slot_name
)

get_slot_names

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get_slot_names()

A list of names for this optimizer's slots.

get_updates

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get_updates(
    loss,
    params
)

get_weights

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get_weights()

minimize

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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

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variables()

Returns variables of this Optimizer based on the order created.