tfa.optimizers.AdamW  |  TensorFlow Addons (original) (raw)

Optimizer that implements the Adam algorithm with weight decay.

Inherits From: DecoupledWeightDecayExtension

tfa.optimizers.AdamW(
    weight_decay: Union[FloatTensorLike, Callable],
    learning_rate: Union[FloatTensorLike, Callable] = 0.001,
    beta_1: Union[FloatTensorLike, Callable] = 0.9,
    beta_2: Union[FloatTensorLike, Callable] = 0.999,
    epsilon: tfa.types.FloatTensorLike = 1e-07,
    amsgrad: bool = False,
    name: str = 'AdamW',
    **kwargs
)

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)

Methods

add_slot

add_slot(
    var, slot_name, initializer='zeros', shape=None
)

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.

add_weight

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, decay_var_list=None, **kwargs
)

Apply gradients to variables.

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

get_config

View source

get_config()

get_gradients

get_gradients(
    loss, params
)

Returns gradients of loss with respect to params.

Should be used only in legacy v1 graph mode.

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()

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='mse') data = np.arange(100).reshape(5, 20) labels = np.zeros(5) results = m.fit(data, labels) # Training. len(opt.get_weights()) 3

minimize

View source

minimize(
    loss, var_list, grad_loss=None, name=None, decay_var_list=None, tape=None
)

Minimize loss by updating var_list.

This method simply computes gradient using tf.GradientTape and callsapply_gradients(). If you want to process the gradient before applying then call tf.GradientTape and apply_gradients() explicitly instead of using this function.

set_weights

set_weights(
    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='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 'RMSprop/iter:0' shape=() dtype=int64, numpy=10>

variables

variables()

Returns variables of this Optimizer based on the order created.