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## Class `AdamW`

Optimizer that implements the Adam algorithm with weight decay.

Inherits From: `DecoupledWeightDecayExtension`

This is an implementation of the AdamW optimizer described in "Decoupled Weight Decay Regularization" by Loshchilov & Hutter (https://arxiv.org/abs/1711.05101) ([pdf])(https://arxiv.org/pdf/1711.05101.pdf).

It computes the update step of `tf.keras.optimizers.Adam` and additionally decays the variable. Note that this is different from adding L2 regularization on the variables to the loss: it regularizes variables with large gradients more than L2 regularization would, which was shown to yield better training loss and generalization error in the paper above.

For further information see the documentation of the Adam Optimizer.

This optimizer can also be instantiated as

``````extend_with_decoupled_weight_decay(tf.keras.optimizers.Adam,
weight_decay=weight_decay)
``````
``````step = tf.Variable(0, trainable=False)
schedule = tf.optimizers.schedules.PiecewiseConstantDecay(
[10000, 15000], [1e-0, 1e-1, 1e-2])
# lr and wd can be a function or a tensor
lr = 1e-1 * schedule(step)
wd = lambda: 1e-4 * schedule(step)

# ...

``````

## `__init__`

View source

``````__init__(
weight_decay,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
**kwargs
)
``````

For further information see the documentation of the Adam Optimizer.

#### Args:

• `weight_decay`: A Tensor or a floating point value. The weight decay.
• `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 2nd moment estimates.
• `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.
• `amsgrad`: boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond".
• `name`: Optional name for the operations created when applying gradients. Defaults to "AdamW".
• `**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

### `add_slot`

``````add_slot(
var,
slot_name,
initializer='zeros'
)
``````

Add a new slot variable for `var`.

### `add_weight`

``````add_weight(
name,
shape,
dtype=None,
initializer='zeros',
trainable=None,
synchronization=tf_variables.VariableSynchronization.AUTO,
aggregation=tf_variables.VariableAggregation.NONE
)
``````

### `apply_gradients`

View source

``````apply_gradients(
name=None,
decay_var_list=None
)
``````

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.
• `decay_var_list`: Optional list of variables to be decayed. Defaults to all variables in var_list.

#### 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`

``````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`

``````get_gradients(
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).

### `get_slot`

``````get_slot(
var,
slot_name
)
``````

### `get_slot_names`

``````get_slot_names()
``````

A list of names for this optimizer's slots.

### `get_updates`

``````get_updates(
loss,
params
)
``````

### `get_weights`

``````get_weights()
``````

### `minimize`

View source

``````minimize(
loss,
var_list,
name=None,
decay_var_list=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`.
• `decay_var_list`: Optional list of variables to be decayed. Defaults to all variables in var_list.
• `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`

``````set_weights(weights)
``````

### `variables`

``````variables()
``````

Returns variables of this Optimizer based on the order created.