tf.keras.optimizers.Nadam

View source on GitHub

Optimizer that implements the NAdam algorithm.

Inherits From: Optimizer

tf.keras.optimizers.Nadam(
    learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name='Nadam',
    **kwargs
)

Much like Adam is essentially RMSprop with momentum, Nadam is Adam with Nesterov momentum.

Initialization:

$$m_0 := 0 \text{(Initialize 1st moment vector)}$$ $$v_0 := 0 \text{(Initialize 2nd moment vector)}$$ $$mu_0 := 1$$ $$t := 0 \text{(Initialize timestep)}$$

Computes:

$$t := t + 1$$ $$\mu_t := \beta_1 * (1 - 0.5 * 0.96^{0.004 * t})$$ $$g' := g / (1 - \prod_{i=1}^{{t}{\mu_i})$$} $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ $$m' := m_t / (1 - \prod_{i=1}^{{t+1}{\mu_i})$$} $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ $$v' := v_t / (1 - \beta_2^t)$$ $$\bar{m} := (1 - \mu_t) * g' + \mu_{t+1} * m'$$ $$\theta_t := \theta_{t-1} - lr * \bar{m} / (\sqrt{v'} + \epsilon)$$

gradient is evaluated at theta(t) + momentum * v(t), and the variables always store theta + beta_1 * m / sqrt(v) instead of theta.

References See Dozat, T., 2015.

Args:

• learning_rate: A Tensor or a floating point value. The learning rate.
• beta_1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
• beta_2: A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm.
• epsilon: A small constant for numerical stability.
• name: Optional name for the operations created when applying gradients. Defaults to "Adamax".
• **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

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.compat.v1.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. 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

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.

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()

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

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. The iterations will be automatically increased by 1.

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.