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# tf.keras.optimizers.RMSprop

## Class RMSprop

Optimizer that implements the RMSprop algorithm.

Inherits From: Optimizer

### Aliases:

• Class tf.compat.v1.keras.optimizers.RMSprop
• Class tf.compat.v2.keras.optimizers.RMSprop
• Class tf.compat.v2.optimizers.RMSprop

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).

## __init__

View source

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

Construct a new RMSprop optimizer.

Note that in the dense implementation of this algorithm, variables and their corresponding accumulators (momentum, gradient moving average, square gradient moving average) will be updated even if the gradient is zero (i.e. accumulators will decay, momentum will be applied). The sparse implementation (used when the gradient is an IndexedSlices object, typically because of tf.gather or an embedding lookup in the forward pass) will not update variable slices or their accumulators unless those slices were used in the forward pass (nor is there an "eventual" correction to account for these omitted updates). This leads to more efficient updates for large embedding lookup tables (where most of the slices are not accessed in a particular graph execution), but differs from the published algorithm.

#### 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.

## Properties

### iterations

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

### weights

Returns variables of this Optimizer based on the order created.

## Methods

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var,
slot_name,
initializer='zeros'
)

Add a new slot variable for var.

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

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name=None
)

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

#### Args:

• name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

#### Returns:

An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

#### Raises:

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

### from_config

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from_config(
cls,
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()

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loss,
params
)

Returns gradients of loss with respect to params.

#### Arguments:

• loss: Loss tensor.
• params: List of variables.

#### Raises:

• ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented).

View source

get_slot(
var,
slot_name
)

### get_slot_names

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get_slot_names()

A list of names for this optimizer's slots.

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loss,
params
)

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get_weights()

### minimize

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minimize(
loss,
var_list,
name=None
)

Minimize loss by updating var_list.

#### 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.
• 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

View source

set_weights(weights)

### variables

View source

variables()

Returns variables of this Optimizer based on the order created.