tf.keras.optimizers.RMSprop

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Optimizer that implements the RMSprop algorithm.

Inherits From: Optimizer

tf.keras.optimizers.RMSprop(
    learning_rate=0.001, rho=0.9, momentum=0.0, epsilon=1e-07, centered=False,
    name='RMSprop', **kwargs
)

A detailed description of rmsprop.

• maintain a moving (discounted) average of the square of gradients
• divide gradient by the root of this average

$$mean_square_t = rho * mean_square{t-1} + (1-rho) * gradient ** 2$$ $$mom_t = momentum * mom_{t-1} + learning_rate * gradient / \sqrt{ / mean_square_t + \epsilon}$$ $$variable_t := variable_{t-1} - mom_t$$

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:

$$mean_grad_t = rho * mean_grad_{t-1} + (1-rho) * gradient$$ $$mean_square_t = rho * mean_square_{t-1} + (1-rho) * gradient ** 2$$ $$mom_t = momentum * mom_{t-1} + learning_rate * gradient / sqrt(mean_square_t - mean_grad_t**2 + epsilon)$$ $$variable_t := variable_{t-1} - mom_t$$

References See ([pdf] http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).

Args:

• learning_rate: A Tensor or a floating point value. The learning rate.
• rho: Discounting factor for the history/coming gradient
• momentum: A scalar tensor.
• epsilon: Small value to avoid zero denominator.
• centered: 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: Optional name prefix for the operations created when applying gradients. Defaults to "RMSprop". @compatibility(eager) When eager execution is enabled, learning_rate, decay, momentum, and epsilon can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. @end_compatibility
• **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.

Attributes:

• 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

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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.compat.v1.VariableAggregation.NONE
)

apply_gradients

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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. The iterations will be automatically increased by 1.

Raises:

• TypeError: If grads_and_vars is malformed.
• ValueError: If none of the variables have gradients.

from_config

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@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

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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

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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

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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. The iterations will be automatically increased by 1.

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.