TensorFlow 2 version | View source on GitHub |
Updated base class for optimizers.
Inherits From: CheckpointableBase
tf.keras.optimizers.Optimizer(
name, **kwargs
)
This class defines the API to add Ops to train a model. You never use this
class directly, but instead instantiate one of its subclasses such as
tf.keras.optimizers.SGD
, tf.keras.optimizers.Adam
.
Usage
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.SGD(learning_rate=0.1)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
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])
Custom training loop with Keras models
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.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.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.
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 Constraint
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.
Hyper parameters
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.
Hyper parameters can be overwritten through user code:
Example:
# Create an optimizer with the desired parameters.
opt = tf.keras.optimizers.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])
Write a customized 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 dense)
- resource_apply_sparse (update variable given gradient tensor is sparse)
- create_slots (if your optimizer algorithm requires additional variables)
- get_config (serialization of the optimizer, include all hyper parameters)
Args | |
---|---|
name
|
A non-empty string. The name to use for accumulators created for the optimizer. |
**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.
|
Raises | |
---|---|
ValueError
|
If name is malformed. |
RuntimeError
|
If _create_slots has been overridden instead of _create_vars. |
Attributes | |
---|---|
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.VariableSynchronization.AUTO,
aggregation=tf.VariableAggregation.NONE
)
apply_gradients
apply_gradients(
grads_and_vars, name=None
)
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.
|
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. |
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.
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
@abc.abstractmethod
get_config()
Returns the config of the optimimizer.
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
.
Arguments | |
---|---|
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()
minimize
minimize(
loss, var_list, grad_loss=None, name=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 .
|
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.