tf.keras.optimizers.Optimizer

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Base class for Keras optimizers.

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Main aliases

tf.optimizers.Optimizer

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.optimizers.Optimizer

tf.keras.optimizers.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.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 a dense tf.Tensor)
_resource_apply_sparse (update variable given gradient tensor is a sparse tf.IndexedSlices. The most common way for this to happen is if you are taking the gradient through a tf.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.
`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`

View source

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`

View source

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, 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`

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.

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`

View source

@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`

View source

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`

View source

get_slot(
    var, slot_name
)

`get_slot_names`

View source

get_slot_names()

A list of names for this optimizer's slots.

`get_updates`

View source

get_updates(
    loss, params
)

`get_weights`

View source

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.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)
print('Training'); results = m.fit(data, labels)
Training ...
len(opt.get_weights())
3

Returns
Weights values as a list of numpy arrays.

`minimize`

View source

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`

View source

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.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)
print('Training'); 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`

View source

variables()

Returns variables of this Optimizer based on the order created.

tf.keras.optimizers.Optimizer

View aliases

Usage

Usage in custom training loops

Example:

Processing gradients before applying them

Example:

Use with tf.distribute.Strategy

Variable Constraints

Thread Compatibility

Slots

Hyperparameters

Example:

Callable learning rate

Example:

Example:

Creating a custom optimizer

Args

Raises

Attributes

Methods

add_slot

add_weight

apply_gradients

Example:

from_config

get_config

get_gradients

get_slot

get_slot_names

get_updates

get_weights

minimize

set_weights

variables

Use with `tf.distribute.Strategy`

`add_slot`

`add_weight`

`apply_gradients`

`from_config`

`get_config`

`get_gradients`

`get_slot`

`get_slot_names`

`get_updates`

`get_weights`

`minimize`

`set_weights`

`variables`