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|
Optimizer that implements the Adamax algorithm.
Inherits From: Optimizer
tf.keras.optimizers.legacy.Adamax(
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-07,
name='Adamax',
**kwargs
)
It is a variant of Adam based on the infinity norm. Default parameters follow those provided in the paper. Adamax is sometimes superior to adam, specially in models with embeddings.
Initialization:
m = 0 # Initialize initial 1st moment vector
v = 0 # Initialize the exponentially weighted infinity norm
t = 0 # Initialize timestep
The update rule for parameter w with gradient g is
described at the end of section 7.1 of the paper:
t += 1
m = beta1 * m + (1 - beta) * g
v = max(beta2 * v, abs(g))
current_lr = learning_rate / (1 - beta1 ** t)
w = w - current_lr * m / (v + epsilon)
Similarly to Adam, the epsilon is added for numerical stability
(especially to get rid of division by zero when v_t == 0).
In contrast to Adam, the sparse implementation of this algorithm
(used when the gradient is an IndexedSlices object, typically because of
tf.gather or an embedding lookup in the forward pass) only updates
variable slices and corresponding m_t, v_t terms when that part of
the variable was used in the forward pass. This means that the sparse
behavior is contrast to the dense behavior (similar to some momentum
implementations which ignore momentum unless a variable slice was actually
used).
Args | |
|---|---|
learning_rate
|
A Tensor, floating point value, or a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule. 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 arguments are clipvalue,
clipnorm, global_clipnorm.
If clipvalue (float) is set, the gradient of each weight
is clipped to be no higher than this value.
If clipnorm (float) is set, the gradient of each weight
is individually clipped so that its norm is no higher than this value.
If global_clipnorm (float) is set the gradient of all weights is
clipped so that their global norm is no higher than this value.
|
Reference | |
|---|---|
Raises | |
|---|---|
ValueError
|
in case of any invalid argument. |
Attributes | |
|---|---|
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 |
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', 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.
| 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. |
add_weight
add_weight(
name,
shape,
dtype=None,
initializer='zeros',
trainable=None,
synchronization=tf.VariableSynchronization.AUTO,
aggregation=tf.VariableAggregation.NONE
)
apply_gradients
apply_gradients(
grads_and_vars, name=None, experimental_aggregate_gradients=True
)
Apply gradients to variables.
This is the second part of minimize(). It returns an Operation that
applies gradients.
The method sums gradients from all replicas in the presence of
tf.distribute.Strategy by default. You can aggregate gradients
yourself by passing experimental_aggregate_gradients=False.
Example:
grads = tape.gradient(loss, vars)
grads = tf.distribute.get_replica_context().all_reduce('sum', grads)
# Processing aggregated gradients.
optimizer.apply_gradients(zip(grads, vars),
experimental_aggregate_gradients=False)
| Args | |
|---|---|
grads_and_vars
|
List of (gradient, variable) pairs. |
name
|
Optional name for the returned operation. When None, uses the
name passed to the Optimizer constructor. Defaults to None.
|
experimental_aggregate_gradients
|
Whether to sum gradients from
different replicas in the presence of tf.distribute.Strategy. If
False, it's user responsibility to aggregate the gradients. Default
to True.
|
| 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. |
RuntimeError
|
If called in a cross-replica context. |
from_config
@classmethodfrom_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.
| 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. |
get_config
get_config()
Returns the config of the optimizer.
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
get_gradients(
loss, params
)
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
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
| Returns | |
|---|---|
| Weights values as a list of numpy arrays. |
minimize
minimize(
loss, var_list, grad_loss=None, name=None, tape=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
|
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.
|
name
|
(Optional) str. 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. The
iterations will be automatically increased by 1.
|
| Raises | |
|---|---|
ValueError
|
If some of the variables are not Variable objects.
|
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>
| Args | |
|---|---|
weights
|
weight values as a list of numpy arrays. |
variables
variables()
Returns variables of this Optimizer based on the order created.