description: An deprecated optimizer that applies loss scaling.

tf.keras.mixed_precision.experimental.LossScaleOptimizer

An deprecated optimizer that applies loss scaling.

Inherits From: LossScaleOptimizer, Optimizer

tf.keras.mixed_precision.experimental.LossScaleOptimizer(
    optimizer, loss_scale
)

Warning: This class is deprecated and will be removed in TensorFlow 2.5. Please use the non-experimental class tf.keras.mixed_precision.LossScaleOptimizer instead.

This class is identical to the non-experimental keras.mixed_precision.LossScaleOptimizer except its constructor takes different arguments. For this class (the experimental version), the constructor takes a loss_scale argument. For the non-experimental class, the constructor encodes the loss scaling information in multiple arguments. Note that unlike this class, the non-experimental class does not accept a tf.compat.v1.mixed_precision.LossScale, which is deprecated.

If you currently use this class, you should switch to the non-experimental tf.keras.mixed_precision.LossScaleOptimizer instead. We show several examples of converting the use of the experimental class to the equivalent non-experimental class.

>>> # In all of the the examples below, `opt1` and `opt2` are identical
>>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), loss_scale='dynamic')
>>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
...     tf.keras.optimizers.SGD())
>>> assert opt1.get_config() == opt2.get_config()

>>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), loss_scale=123)
>>> # dynamic=False indicates to use fixed loss scaling. initial_scale=123
>>> # refers to the initial loss scale, which is the single fixed loss scale
>>> # when dynamic=False.
>>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), dynamic=False, initial_scale=123)
>>> assert opt1.get_config() == opt2.get_config()

>>> loss_scale = tf.compat.v1.mixed_precision.experimental.DynamicLossScale(
...     initial_loss_scale=2048, increment_period=500)
>>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), loss_scale=loss_scale)
>>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), initial_scale=2048,
...     dynamic_growth_steps=500)
>>> assert opt1.get_config() == opt2.get_config()

Make sure to also switch from this class to the non-experimental class in isinstance checks, if you have any. If you do not do this, your model may run into hard-to-debug issues, as the experimental LossScaleOptimizer subclasses the non-experimental LossScaleOptimizer, but not vice versa. It is safe to switch isinstance checks to the non-experimental LossScaleOptimizer even before using the non-experimental LossScaleOptimizer.

>>> opt1 = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
...     tf.keras.optimizers.SGD(), loss_scale='dynamic')
>>> # The experimental class subclasses the non-experimental class
>>> isinstance(opt1, tf.keras.mixed_precision.LossScaleOptimizer)
True
>>> opt2 = tf.keras.mixed_precision.LossScaleOptimizer(
...     tf.keras.optimizers.SGD())
>>> # The non-experimental class does NOT subclass the experimental class.
>>> isinstance(opt2, tf.keras.mixed_precision.experimental.LossScaleOptimizer)
False

Args

optimizer 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. If a DynamicLossScale is passed, DynamicLossScale.multiplier must be 2 (the default).

Raises

ValueError in case of any invalid argument.

Attributes

clipnorm float or None. If set, clips gradients to a maximum norm.
clipvalue float or None. If set, clips gradients to a maximum value.
dynamic Bool indicating whether dynamic loss scaling is used.
dynamic_counter The number of steps since the loss scale was last increased or decreased.

This is None if LossScaleOptimizer.dynamic is False.

The counter is incremented every step. Once it reaches LossScaleOptimizer.dynamic_growth_steps, the loss scale will be doubled and the counter will be reset back to zero. If nonfinite gradients are encountered, the loss scale will be halved and the counter will be reset back to zero.

dynamic_growth_steps The number of steps it takes to increase the loss scale.

This is None if LossScaleOptimizer.dynamic is False.

Every dynamic_growth_steps consecutive steps with finite gradients, the loss scale is increased.

global_clipnorm float or None. If set, clips gradients to a maximum norm.
initial_scale The initial loss scale.

If LossScaleOptimizer.dynamic is False, this is the same number as LossScaleOptimizer.loss_scale, as the loss scale never changes.

inner_optimizer The optimizer that this LossScaleOptimizer is wrapping.
iterations Variable. The number of training steps this Optimizer has run.
loss_scale The current loss scale as a float32 scalar tensor.
weights Returns variables of this Optimizer based on the order created.

Methods

add_slot

View source

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

Add a new slot variable for var.

add_weight

View source

add_weight(
    name, shape, dtype=None, initializer='zeros', trainable=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.compat.v1.VariableAggregation.NONE
)

apply_gradients

View source

apply_gradients(
    grads_and_vars, name=None, experimental_aggregate_gradients=True
)

Apply gradients to variables.

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

The method sums gradients from all replicas in the presence of tf.distribute.Strategy by default. You can aggregate gradients yourself by passing experimental_aggregate_gradients=False.

Example:

grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
    experimental_aggregate_gradients=False)

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.
experimental_aggregate_gradients Whether to sum gradients from different replicas in the presense of tf.distribute.Strategy. If False, it's user responsibility to aggregate the gradients. Default to True.

Returns
An Operation that applies the specified gradients. The iterations will be automatically increased by 1.

Raises
TypeError If grads_and_vars is malformed.
ValueError If none of the variables have gradients.
RuntimeError If called in a cross-replica context.

from_config

View source

@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.

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

View source

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.

Returns
Python dictionary.

get_gradients

View source

get_gradients(
    loss, params
)

Returns gradients of loss with respect to params.

Should be used only in legacy v1 graph mode.

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_scaled_loss

View source

get_scaled_loss(
    loss
)

Scales the loss by the loss scale.

This method is only needed if you compute gradients manually, e.g. with tf.GradientTape. In that case, call this method to scale the loss before passing the loss to tf.GradientTape. If you use LossScaleOptimizer.minimize or LossScaleOptimizer.get_gradients, loss scaling is automatically applied and this method is unneeded.

If this method is called, get_unscaled_gradients should also be called. See the tf.keras.mixed_precision.LossScaleOptimizer doc for an example.

Args
loss The loss, which will be multiplied by the loss scale. Can either be a tensor or a callable returning a tensor.

Returns
loss multiplied by LossScaleOptimizer.loss_scale.

get_slot

View source

get_slot(
    var, slot_name
)

get_slot_names

View source

get_slot_names()

A list of names for this optimizer's slots.

get_unscaled_gradients

View source

get_unscaled_gradients(
    grads
)

Unscales the gradients by the loss scale.

This method is only needed if you compute gradients manually, e.g. with tf.GradientTape. In that case, call this method to unscale the gradients after computing them with tf.GradientTape. If you use LossScaleOptimizer.minimize or LossScaleOptimizer.get_gradients, loss scaling is automatically applied and this method is unneeded.

If this method is called, get_scaled_loss should also be called. See the tf.keras.mixed_precision.LossScaleOptimizer doc for an example.

Args
grads A list of tensors, each which will be divided by the loss scale. Can have None values, which are ignored.

Returns
A new list the same size as grads, where every non-None value in grads is divided by LossScaleOptimizer.loss_scale.

get_updates

View source

get_updates(
    loss, params
)

get_weights

View source

get_weights()

Returns the current weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

>>> opt = tf.keras.optimizers.RMSprop()
>>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
>>> m.compile(opt, loss='mse')
>>> data = np.arange(100).reshape(5, 20)
>>> labels = np.zeros(5)
>>> print('Training'); results = m.fit(data, labels)
Training ...
>>> len(opt.get_weights())
3

Returns
Weights values as a list of numpy arrays.

minimize

View source

minimize(
    loss, var_list, grad_loss=None, name=None, tape=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 Tensor or callable. If a callable, loss should take no arguments and return the value to minimize. If a Tensor, the tape argument must be passed.
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) str. Name for the returned operation.
tape (Optional) tf.GradientTape. If loss is provided as a Tensor, the tape that computed the loss must be provided.

Returns
An Operation that updates the variables in var_list. The iterations will be automatically increased by 1.

Raises
ValueError If some of the variables are not Variable objects.

set_weights

View source

set_weights(
    weights
)

Set the weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

>>> opt = tf.keras.optimizers.RMSprop()
>>> m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
>>> m.compile(opt, loss='mse')
>>> data = np.arange(100).reshape(5, 20)
>>> labels = np.zeros(5)
>>> print('Training'); results = m.fit(data, labels)
Training ...
>>> new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]
>>> opt.set_weights(new_weights)
>>> opt.iterations
<tf.Variable 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>

Arguments
weights weight values as a list of numpy arrays.

variables

View source

variables()

Returns variables of this Optimizer based on the order created.