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tf.keras.optimizers.Adagrad

TensorFlow 2.0 version View source on GitHub

Class Adagrad

Optimizer that implements the Adagrad algorithm.

Inherits From: Optimizer

Aliases:

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

Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives, the smaller the updates.

Initialization:

$$accum_{g_0} := \text{initial_accumulator_value}$$

Update step:

$$t := t + 1$$
$$accum_{g_t} := accum_{g_{t-1}} + g^2$$
$$\theta_t := \theta_{t-1} - lr * g / (\sqrt{accum_{g_t}} + \epsilon)$$

References:

__init__

View source

__init__(
    learning_rate=0.001,
    initial_accumulator_value=0.1,
    epsilon=1e-07,
    name='Adagrad',
    **kwargs
)

Construct a new Adagrad optimizer.

Args:

  • learning_rate: A Tensor or a floating point value. The learning rate.
  • initial_accumulator_value: A floating point value. Starting value for the accumulators, must be positive.
  • epsilon: A floating point value. Starting value for the accumulators, must be positive.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "Adagrad".
  • **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.

Raises:

  • ValueError: If the initial_accumulator_value or epsilon is invalid.

Eager Compatibility

When eager execution is enabled, learning_rate can 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.

Properties

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

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.VariableAggregation.NONE
)

apply_gradients

View source

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. 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

View source

@classmethod
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

View source

get_config()

get_gradients

View source

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

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_updates

View source

get_updates(
    loss,
    params
)

get_weights

View source

get_weights()

minimize

View source

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. 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.