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.

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)

# ...

optimizer = tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd)
``````

`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`, `exclude_from_weight_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. `exclude_from_weight_decay` accepts list of regex patterns of variables excluded from weight decay.

`clipnorm` `float` or `None`. If set, clips gradients to a maximum norm.
`clipvalue` `float` or `None`. If set, clips gradients to a maximum value.
`global_clipnorm` `float` or `None`.

If set, clips gradients to a maximum norm.

Check `tf.clip_by_global_norm` for more details.

`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 a new slot variable for `var`.

A slot variable is an additional variable associated with `var` to train. It is allocated and managed by optimizers, e.g. `Adam`.

Args
`var` a `Variable` object.
`slot_name` name of the slot variable.
`initializer` initializer of the slot variable
`shape` (Optional) shape of the slot variable. If not set, it will default to the shape of `var`.

Returns
A slot variable.

### `apply_gradients`

View source

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.
`decay_var_list` Optional list of variables to be decayed. Defaults to all variables in var_list. Note `decay_var_list` takes priority over `exclude_from_weight_decay` if specified.
`**kwargs` Additional arguments to pass to the base optimizer's apply_gradient method, e.g., TF2.2 added an argument `experimental_aggregate_gradients`.

Returns
An `Operation` that applies the specified gradients.

Raises
`TypeError` If `grads_and_vars` is malformed.
`ValueError` If none of the variables have gradients.

### `from_config`

View source

Creates an optimizer from its config.

This method is the reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary.

Args
`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.

View source

### `get_gradients`

Returns gradients of `loss` with respect to `params`.

Should be used only in legacy v1 graph mode.

Args
`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_names`

A list of names for this optimizer's slots.

### `get_weights`

Returns the current weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function returns the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they were created. The returned list can in turn be used to load state into similarly parameterized optimizers.

For example, the RMSprop optimizer for this simple model returns a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

````opt = tf.keras.optimizers.legacy.RMSprop()`
`m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])`
`m.compile(opt, loss=&#x27;mse')`
`data = np.arange(100).reshape(5, 20)`
`labels = np.zeros(5)`
`results = m.fit(data, labels)  # Training.`
`len(opt.get_weights())`
`3````

Returns
Weights values as a list of numpy arrays.

### `minimize`

View source

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` `Tensor` or callable. If a callable, `loss` should take no arguments and return the value to minimize. If a `Tensor`, the `tape` argument must be passed.
`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. Note `decay_var_list` takes priority over `exclude_from_weight_decay` if specified.
`name` Optional name for the returned operation.
`tape` (Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`, the tape that computed the `loss` must be provided.

Returns
An Operation that updates the variables in `var_list`.

Raises
`ValueError` If some of the variables are not `Variable` objects.

### `set_weights`

Set the weights of the optimizer.

The weights of an optimizer are its state (ie, variables). This function takes the weight values associated with this optimizer as a list of Numpy arrays. The first value is always the iterations count of the optimizer, followed by the optimizer's state variables in the order they are created. The passed values are used to set the new state of the optimizer.

For example, the RMSprop optimizer for this simple model takes a list of three values-- the iteration count, followed by the root-mean-square value of the kernel and bias of the single Dense layer:

````opt = tf.keras.optimizers.legacy.RMSprop()`
`m = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])`
`m.compile(opt, loss=&#x27;mse')`
`data = np.arange(100).reshape(5, 20)`
`labels = np.zeros(5)`
`results = m.fit(data, labels)  # Training.`
`new_weights = [np.array(10), np.ones([20, 10]), np.zeros([10])]`
`opt.set_weights(new_weights)`
`opt.iterations`
`<tf.Variable &#x27;RMSprop/iter:0' shape=() dtype=int64, numpy=10>````

Args
`weights` weight values as a list of numpy arrays.

### `variables`

Returns variables of this Optimizer based on the order created.

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{"lastModified": "Last updated 2023-07-12 UTC."}