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Optimizer that implements the RMSprop algorithm.
Inherits From: Optimizer
tf.keras.optimizers.experimental.RMSprop(
learning_rate=0.001,
rho=0.9,
momentum=0.0,
epsilon=1e-07,
centered=False,
clipnorm=None,
clipvalue=None,
global_clipnorm=None,
use_ema=False,
ema_momentum=0.99,
ema_overwrite_frequency=100,
jit_compile=True,
name='RMSprop',
**kwargs
)
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. |
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.RMSprop(learning_rate=0.1)
var1 = tf.Variable(10.0)
loss = lambda: (var1 ** 2) / 2.0 # d(loss) / d(var1) = var1
step_count = opt.minimize(loss, [var1]).numpy()
var1.numpy()
9.683772
Reference | |
---|---|
Attributes | |
---|---|
iterations
|
The number of training steps this optimizer has run.
By default, iterations would be incremented by one every time
|
learning_rate
|
Methods
add_variable
add_variable(
shape, dtype=None, initializer='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. |
add_variable_from_reference
add_variable_from_reference(
model_variable, variable_name, shape=None, initial_value=None
)
Create an optimizer variable from model variable.
Create an optimizer variable based on the information of model variable. For example, in SGD optimizer momemtum, for each model variable, a corresponding momemtum variable is created of the same shape and dtype.
Args | |
---|---|
model_variable
|
tf.Variable. The corresponding model variable to the optimizer variable to be created. |
variable_name
|
String. The name prefix of the optimizer variable to be
created. The create variables name will follow the pattern
{variable_name}/{model_variable.name} , e.g., momemtum/dense_1 .
|
shape
|
List or Tuple, defaults to None. The shape of the optimizer
variable to be created. If None, the created variable will have the
same shape as model_variable .
|
initial_value
|
A Tensor, or Python object convertible to a Tensor, defaults to None. The initial value of the optimizer variable, if None, the initial value will be default to 0. |
Returns | |
---|---|
An optimizer variable. |
aggregate_gradients
aggregate_gradients(
grads_and_vars
)
Aggregate gradients on all devices.
By default we will perform reduce_sum of gradients across devices. Users can implement their own aggregation logic by overriding this method.
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs. |
Returns | |
---|---|
List of (gradient, variable) pairs. |
apply_gradients
apply_gradients(
grads_and_vars, skip_gradients_aggregation=False
)
Apply gradients to variables.
Args | |
---|---|
grads_and_vars
|
List of (gradient, variable) pairs. |
skip_gradients_aggregation
|
If true, gradients aggregation will not be performed inside optimizer. Usually this arg is set to True when you write custom code aggregating gradients outside the optimizer. |
Returns | |
---|---|
None |
Raises | |
---|---|
TypeError
|
If grads_and_vars is malformed.
|
RuntimeError
|
If called in a cross-replica context. |
build
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
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 .
|
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 .
|
finalize_variable_values
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
@classmethod
from_config( config )
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. |
Returns | |
---|---|
An optimizer instance. |
get_config
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
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 .
|
tape
|
(Optional) tf.GradientTape .
|
Returns | |
---|---|
None |
update_step
update_step(
gradient, variable
)
Update step given gradient and the associated model variable.