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Base class for legacy Keras optimizers.
tf.keras.optimizers.legacy.Optimizer(
name, gradient_aggregator=None, gradient_transformers=None, **kwargs
)
You should not use this class directly, but instead instantiate one of its
subclasses such as tf.keras.optimizers.legacy.SGD
,
tf.keras.optimizers.legacy.Adam
, etc.
This is the default Keras optimizer base class until v2.10 (included).
In v2.11 and later, tf.keras.optimizers.Optimizer
points to a new base class implementation. The legacy class won't be
deleted in the future and will continue to be available at
tf.keras.optimizers.legacy.Optimizer
.
Usage
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
var1 = tf.Variable(2.0)
var2 = tf.Variable(5.0)
loss = lambda: 3 * var1 * var1 + 2 * var2 * var2
# In graph mode, returns op that minimizes the loss by updating the listed
# variables.
opt_op = opt.minimize(loss, var_list=[var1, var2])
opt_op.run()
# In eager mode, simply call minimize to update the list of variables.
opt.minimize(loss, var_list=[var1, var2])
Usage in custom training loops
In Keras models, sometimes variables are created when the model is first called, instead of construction time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases.
Example:
opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(num_hidden, activation='relu'))
model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid'))
loss_fn = lambda: tf.keras.losses.mse(model(input), output)
var_list_fn = lambda: model.trainable_weights
for input, output in data:
opt.minimize(loss_fn, var_list_fn)
Processing gradients before applying them
Calling minimize()
takes care of both computing the gradients and
applying them to the variables. If you want to process the gradients
before applying them you can instead use the optimizer in three steps:
- Compute the gradients with
tf.GradientTape
. - Process the gradients as you wish.
- Apply the processed gradients with
apply_gradients()
.
Example:
# Create an optimizer.
opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1)
# Compute the gradients for a list of variables.
with tf.GradientTape() as tape:
loss = <call_loss_function>
vars = <list_of_variables>
grads = tape.gradient(loss, vars)
# Process the gradients, for example cap them, etc.
# capped_grads = [MyCapper(g) for g in grads]
processed_grads = [process_gradient(g) for g in grads]
# Ask the optimizer to apply the processed gradients.
opt.apply_gradients(zip(processed_grads, var_list))
Use with tf.distribute.Strategy
This optimizer class is tf.distribute.Strategy
aware, which means it
automatically sums gradients across all replicas. To average gradients,
you divide your loss by the global batch size, which is done
automatically if you use tf.keras
built-in training or evaluation loops.
See the reduction
argument of your loss which should be set to
tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE
for averaging or
tf.keras.losses.Reduction.SUM
for not.
To aggregate gradients yourself, call apply_gradients
with
experimental_aggregate_gradients
set to False. This is useful if you need
to process aggregated gradients.
If you are not using these and you want to average gradients, you should use
tf.math.reduce_sum
to add up your per-example losses and then divide by
the global batch size. Note that when using tf.distribute.Strategy
, the
first component of a tensor's shape is the replica-local batch size, which
is off by a factor equal to the number of replicas being used to compute a
single step. As a result, using tf.math.reduce_mean
will give the wrong
answer, resulting in gradients that can be many times too big.
Variable Constraints
All Keras optimizers respect variable constraints. If constraint function is passed to any variable, the constraint will be applied to the variable after the gradient has been applied to the variable. Important: If gradient is sparse tensor, variable constraint is not supported.
Thread Compatibility
The entire optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary.
Slots
Many optimizer subclasses, such as Adam
and Adagrad
allocate and manage
additional variables associated with the variables to train. These are
called Slots. Slots have names and you can ask the optimizer for the
names of the slots that it uses. Once you have a slot name you can ask the
optimizer for the variable it created to hold the slot value.
This can be useful if you want to log debug a training algorithm, report stats about the slots, etc.
Hyperparameters
These are arguments passed to the optimizer subclass constructor
(the __init__
method), and then passed to self._set_hyper()
.
They can be either regular Python values (like 1.0), tensors, or
callables. If they are callable, the callable will be called during
apply_gradients()
to get the value for the hyper parameter.
Hyperparameters can be overwritten through user code:
Example:
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
loss = lambda: 3 * var1 + 2 * var2
# In eager mode, simply call minimize to update the list of variables.
opt.minimize(loss, var_list=[var1, var2])
# update learning rate
opt.learning_rate = 0.05
opt.minimize(loss, var_list=[var1, var2])
Callable learning rate
Optimizer accepts a callable learning rate in two ways. The first way is
through built-in or customized
tf.keras.optimizers.schedules.LearningRateSchedule
. The schedule will be
called on each iteration with schedule(iteration)
, a tf.Variable
owned by the optimizer.
Example:
var = tf.Variable(np.random.random(size=(1,)))
learning_rate = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=.01, decay_steps=20, decay_rate=.1)
opt = tf.keras.optimizers.legacy.SGD(learning_rate=learning_rate)
loss = lambda: 3 * var
opt.minimize(loss, var_list=[var])
<tf.Variable...
The second way is through a callable function that does not accept any arguments.
Example:
var = tf.Variable(np.random.random(size=(1,)))
def lr_callable():
return .1
opt = tf.keras.optimizers.legacy.SGD(learning_rate=lr_callable)
loss = lambda: 3 * var
opt.minimize(loss, var_list=[var])
<tf.Variable...
Creating a custom optimizer
If you intend to create your own optimization algorithm, simply inherit from this class and override the following methods:
_resource_apply_dense
(update variable given gradient tensor is a densetf.Tensor
)_resource_apply_sparse
(update variable given gradient tensor is a sparsetf.IndexedSlices
. The most common way for this to happen is if you are taking the gradient through atf.gather
.)_create_slots
(if your optimizer algorithm requires additional variables)get_config
(serialization of the optimizer, include all hyper parameters)
Args | |
---|---|
name
|
String. The name to use for momentum accumulator weights created by the optimizer. |
gradient_aggregator
|
The function to use to aggregate gradients across
devices (when using tf.distribute.Strategy ). If None , defaults
to summing the gradients across devices. The function should accept
and return a list of (gradient, variable) tuples.
|
gradient_transformers
|
Optional. List of functions to use to transform
gradients before applying updates to Variables. The functions are
applied after gradient_aggregator . The functions should accept and
return a list of (gradient, variable) tuples.
|
**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.
|
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. Default to the name
passed to the Optimizer constructor.
|
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
@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.
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
@abc.abstractmethod
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.