tf.keras.dtensor.experimental.optimizers.RMSprop

DTensor specific optimizers.

Inherits From: RMSprop, Optimizer

tf.keras.dtensor.experimental.optimizers.RMSprop(
    learning_rate=0.001,
    rho=0.9,
    momentum=0.0,
    epsilon=1e-07,
    centered=False,
    gradients_clip_option=None,
    ema_option=None,
    jit_compile=False,
    name=&#x27;RMSprop',
    mesh=None
)

The major changes for this class is that all the variable init logic will be mesh/layout aware.

Optimizer that implements the RMSprop algorithm.

The gist of RMSprop is to:

Maintain a moving (discounted) average of the square of gradients
Divide the gradient by the root of this average

This implementation of RMSprop uses plain momentum, not Nesterov momentum.

The centered version additionally maintains a moving average of the gradients, and uses that average to estimate the variance.

Args
`learning_rate`	Initial value for the learning rate: either a floating point value, or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance. Defaults to 0.001.
`rho`	float, defaults to 0.9. Discounting factor for the old gradients.
`momentum`	float, defaults to 0.0. If not 0.0., the optimizer tracks the momentum value, with a decay rate equals to `1 - momentum`.
`epsilon`	A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to 1e-7.
`centered`	Boolean. If `True`, gradients are normalized by the estimated variance of the gradient; if False, by the uncentered second moment. Setting this to `True` may help with training, but is slightly more expensive in terms of computation and memory. Defaults to `False`.
`name`	String. The name to use for momentum accumulator weights created by the optimizer.
`weight_decay`	Float, defaults to None. 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 # noqa: E501 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 # noqa: E501 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 # noqa: E501 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.
`jit_compile`	Boolean, defaults to True. If True, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.
`**kwargs`	keyword arguments only used for backward compatibility.

Usage:

opt = tf.keras.optimizers.experimental.RMSprop(learning_rate=0.1)
var1 = tf.Variable(10.0)
loss = lambda: (var1 ** 2) / 2.0  # d(loss) / d(var1) = var1
opt.minimize(loss, [var1])
var1.numpy()
9.683772

Reference
Hinton, 2012

Reference

Hinton, 2012

Attributes
`iterations`	The number of training steps this `optimizer` has run. By default, iterations would be incremented by one every time `apply_gradients()` is called.
`learning_rate`

Attributes

iterations

The number of training steps this optimizer has run.

By default, iterations would be incremented by one every time apply_gradients() is called.

learning_rate

Methods

`add_variable`

View source

add_variable(
    shape, dtype=None, initializer=&#x27;zeros', name=None
)

Create an optimizer variable.

Args
`shape`	A list of integers, a tuple of integers, or a 1-D Tensor of type int32. Defaults to scalar if unspecified.
`dtype`	The DType of the optimizer variable to be created. Defaults to `tf.keras.backend.floatx` if unspecified.
`initializer`	string or callable. Initializer instance.
`name`	The name of the optimizer variable to be created.

Returns
An optimizer variable, in the format of tf.Variable.

`build`

View source

build(
    var_list
)

Initialize the optimizer's variables, such as momemtum variables.

This function has to be implemented by subclass optimizers, and subclass optimizers need to call super().build(var_list).

Args
`var_list`	List of model variables to build optimizers on. For example, SGD optimizer with momentum will store one momentum variable corresponding to each model variable.

`compute_gradients`

View source

compute_gradients(
    loss, var_list, tape=None
)

Compute gradients of loss on trainable variables.

Args
`loss`	`Tensor` or callable. If a callable, `loss` should take no arguments and return 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.
`tape`	(Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`, the tape that computed the `loss` must be provided.

Returns
A list of (gradient, variable) pairs. Variable is always present, but gradient can be `None`.

`exclude_from_weight_decay`

View source

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 `tf.Variable`s 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`

View source

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`

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.

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`

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.

Subclass optimizer should override this method to include other hyperparameters.

Returns
Python dictionary.

`minimize`

View source

minimize(
    loss, var_list, 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.
`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.
`tape`	(Optional) `tf.GradientTape`.

Returns
None

`set_weights`

View source

set_weights(
    weights
)

Set the weights of the optimizer.

Args
`weights`	a list of `tf.Variable`s or numpy arrays, the target values of optimizer variables. It should have the same order as `self._variables`.

`update_step`

View source

update_step(
    gradient, variable
)

Update step given gradient and the associated model variable.

`variables`

View source

variables()

Returns variables of this optimizer.

tf.keras.dtensor.experimental.optimizers.RMSprop

Args

Usage:

Reference

Attributes

Methods

add_variable

build

compute_gradients

exclude_from_weight_decay

finalize_variable_values

from_config

get_config

minimize

set_weights

update_step

variables

`add_variable`

`build`

`compute_gradients`

`exclude_from_weight_decay`

`finalize_variable_values`

`from_config`

`get_config`

`minimize`

`set_weights`

`update_step`

`variables`