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Base class for Keras optimizers.
tf.keras.optimizers.Optimizer(
name, **kwargs
)
You should not use this class directly, but instead instantiate one of its
subclasses such as tf.keras.optimizers.SGD
, tf.keras.optimizers.Adam
, etc.
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])
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.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.
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.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.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.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 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. |
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.compat.v1.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 presense 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. |
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. |