tf.keras.optimizers.Optimizer

Updated base class for optimizers.

Inherits From: CheckpointableBase

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.optimizers.Optimizer, `tf.compat.v2.keras.optimizers.Optimizer`, `tf.compat.v2.optimizers.Optimizer`

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:

1. Compute the gradients with tf.GradientTape.
2. Process the gradients as you wish.
3. 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

View source

add_slot(
    var, slot_name, initializer='zeros'
)

Add a new slot variable for var.

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
)

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

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.

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

View source

@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

View source

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

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()

minimize

View source

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

View source

set_weights(
    weights
)

variables

View source

variables()

Returns variables of this Optimizer based on the order created.