tf.keras.optimizers.Optimizer

Abstract optimizer base class.

View aliases

Main aliases

tf.keras.optimizers.experimental.Optimizer, tf.optimizers.Optimizer, tf.optimizers.experimental.Optimizer

tf.keras.optimizers.Optimizer(
    name,
    weight_decay=0,
    clipnorm=None,
    clipvalue=None,
    global_clipnorm=None,
    use_ema=False,
    ema_momentum=0.99,
    ema_overwrite_frequency=None,
    jit_compile=True,
    **kwargs
)

This class supports distributed training. If you want to implement your own optimizer, please subclass this class instead of _BaseOptimizer.

Args
`name`	String. The name to use for momentum accumulator weights created by the optimizer.
`weight_decay`	Float, defaults to None. If set, weight decay is applied.
`clipnorm`	Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
`clipvalue`	Float. If set, the gradient of each weight is clipped to be no higher than this value.
`global_clipnorm`	Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
`use_ema`	Boolean, defaults to False. If True, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
`ema_momentum`	Float, defaults to 0.99. Only used if `use_ema=True`. This is the momentum to use when computing the EMA of the model's weights: `new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value`.
`ema_overwrite_frequency`	Int or None, defaults to None. Only used if `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we overwrite the model variable by its moving average. If None, the optimizer does not overwrite model variables in the middle of training, and you need to explicitly overwrite the variables at the end of training by calling `optimizer.finalize_variable_values()` (which updates the model variables in-place). When using the built-in `fit()` training loop, this happens automatically after the last epoch, and you don't need to do anything.
`jit_compile`	Boolean, defaults to True. If True, the optimizer will use XLA compilation. If no GPU device is found, this flag will be ignored.
`mesh`	optional `tf.experimental.dtensor.Mesh` instance. When provided, the optimizer will be run in DTensor mode, e.g. state tracking variable will be a DVariable, and aggregation/reduction will happen in the global DTensor context.
`**kwargs`	keyword arguments only used for backward compatibility.

Usage

# Create an optimizer with the desired parameters.
opt = keras.optimizers.SGD(learning_rate=0.1)
var1, var2 = tf.Variable(1.0), tf.Variable(2.0)
# `loss` is a callable that takes no argument and returns the value
# to minimize.
loss = lambda: 3 * var1 * var1 + 2 * var2 * var2
# Call minimize to update the list of variables.
opt.minimize(loss, var_list=[var1, var2])

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.experimental.SGD(learning_rate=0.1)
var1, var2 = tf.Variable(1.0), tf.Variable(2.0)

# Compute the gradients for a list of variables.
with tf.GradientTape() as tape:
  loss = 3 * var1 * var1 + 2 * var2 * var2
grads = tape.gradient(loss, [var1, var2])

# Process the gradients.
grads[0] = grads[0] + 1

# Ask the optimizer to apply the gradients on variables.
opt.apply_gradients(zip(grads, [var1, var2]))

Dynamic learning rate

Dynamic learning rate can be achieved by setting learning rate as a built-in or customized tf.keras.optimizers.schedules.LearningRateSchedule.

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.experimental.SGD(learning_rate=learning_rate)
loss = lambda: 3 * var
opt.minimize(loss, var_list=[var])

Gradients clipping

Users can clip the gradients before applying to variables by setting clipnorm, clipvalue and global_clipnorm. Notice that clipnorm and global_clipnorm can only have one being set.

Example:

opt = tf.keras.optimizers.experimental.SGD(learning_rate=1, clipvalue=1)
var1, var2 = tf.Variable(2.0), tf.Variable(2.0)
with tf.GradientTape() as tape:
  loss = 2 * var1 + 2 * var2
grads = tape.gradient(loss, [var1, var2])
print([grads[0].numpy(), grads[1].numpy()])
[2.0, 2.0]
opt.apply_gradients(zip(grads, [var1, var2]))
# Without clipping, we should get [0, 0], but as gradients are clipped
# to have max value 1, we get [1.0, 1.0].
print([var1.numpy(), var2.numpy()])
[1.0, 1.0]

Using weight decay.

Weight decay in certain scenarios can boost the model's performance. Keras has built-in support for weight decay in all optimizers. Users can apply weight decay by setting weight_decay argument.

opt = tf.keras.optimizers.experimental.SGD(1, weight_decay=0.004)
grads, var1, var2 = tf.zeros(()), tf.Variable(2.0), tf.Variable(2.0)
# You can exclude variables from weight decay, in this case we
# exclude `var2`.
opt.exclude_from_weight_decay(var_list=[var2])
opt.apply_gradients(zip([grads, grads], [var1, var2]))
print([var1.numpy(), var2.numpy()])
[1.992, 2.0]

Using exponential moving average.

Empirically it has been found that using the exponential moving average (EMA) of the trained parameters of a deep network achieves a better performance than using its trained parameters directly. Keras optimizers allows users to compute this moving average and overwrite the model variables at desired time.

Example:

# Create an SGD optimizer with EMA on. `ema_momentum` controls the decay
# rate of the moving average. `ema_momentum=1` means no decay and the stored
# moving average is always model variable's initial value before training.
# Reversely, `ema_momentum=0` is equivalent to not using EMA.
# `ema_overwrite_frequency=3` means every 3 iterations, we overwrite the
# trainable variables with their moving average values.
opt = tf.keras.optimizers.experimental.SGD(
    learning_rate=1,
    use_ema=True,
    ema_momentum=0.5,
    ema_overwrite_frequency=3)
var1, var2 = tf.Variable(2.0), tf.Variable(2.0)
with tf.GradientTape() as tape:
  loss = var1 + var2
grads = tape.gradient(loss, [var1, var2])
# First iteration: [var1, var2] = [1.0, 1.0]
opt.apply_gradients(zip(grads, [var1, var2]))
print([var1, var2])

# Second iteration: [var1, var2] = [0.0, 0.0]
opt.apply_gradients(zip(grads, [var1, var2]))
print([var1, var2])

# Third iteration, without EMA, we should see [var1, var2] = [-1.0, -1.0],
# but overwriting results in [var1, var2] = [-0.125, -0.125]. The full
# calculation for the moving average of var1 is:
# var1=2*0.5**3+1*(1-0.5)*0.5**2+0*(1-0.5)*0.5**1+(-1)*(1-0.5)=-0.125.
opt.apply_gradients(zip(grads, [var1, var2]))
print([var1, var2])

When optimizer is constructed with use_ema=True, in custom training loop, users can explicitly call finalize_variable_values() to overwrite trainable variables with their EMA values. finalize_variable_values() is by default called at the end of model.fit().

Use with `tf.distribute.Strategy`

This optimizer class is tf.distribute.Strategy aware, which means it automatically sums gradients across all replicas. To aggregate gradients yourself, call apply_gradients with skip_aggregate_gradients set to True. This is useful if you need to process aggregated gradients.

# This example is not runnable, it consists of dummy code for simple
# tutorial.
strategy = tf.distribute.experimental.TPUStrategy()

with strategy.scope():
  opt = tf.keras.optimizers.experimental.SGD()
  model = magic_function_that_returns_model()
  gradients = magic_function_that_returns_gradients()
  # Custom logic to aggregate gradients.
  gradients = strategy.reduce("SUM", gradients, axis=None)
  opt.apply_gradients(zip(gradients, model.trainable_variables),
      skip_aggregate_gradients=True)

Creating a custom optimizer

If you intend to create your own optimization algorithm, please inherit from this class and override the following methods:

build: Create your optimizer-related variables, such as momentums in SGD optimizer.
update_step: Implement your optimizer's updating logic.
get_config: serialization of the optimizer, include all hyper parameters.

Your optimizer would automatically be compatible with tensorflow distributed training if you subclass optimizer_experimental.Optimizer.

Attributes
`iterations`	The number of training steps this `optimizer` has run. By default, iterations would be incremented by one every time `apply_gradients()` is called.
`learning_rate`
`variables`	Returns variables of this optimizer.

Attributes

iterations

The number of training steps this optimizer has run.

By default, iterations would be incremented by one every time apply_gradients() is called.

learning_rate

variables Returns variables of this optimizer.

Args
`shape`	A list of integers, a tuple of integers, or a 1-D Tensor of type int32. Defaults to scalar if unspecified.
`dtype`	The DType of the optimizer variable to be created. Defaults to `tf.keras.backend.floatx` if unspecified.
`initializer`	string or callable. Initializer instance.
`name`	The name of the optimizer variable to be created.

Args
`model_variable`	tf.Variable. The corresponding model variable to the optimizer variable to be created.
`variable_name`	String. The name prefix of the optimizer variable to be created. The create variables name will follow the pattern `{variable_name}/{model_variable.name}`, e.g., `momemtum/dense_1`.
`shape`	List or Tuple, defaults to None. The shape of the optimizer variable to be created. If None, the created variable will have the same shape as `model_variable`.
`initial_value`	A Tensor, or Python object convertible to a Tensor, defaults to None. The initial value of the optimizer variable, if None, the initial value will be default to 0.

Args
`grads_and_vars`	List of `(gradient, variable)` pairs.
`name`	string, defaults to None. The name of the namescope to use when creating variables. If None, `self.name` will be used.
`skip_gradients_aggregation`	If true, gradients aggregation will not be performed inside optimizer. Usually this arg is set to True when you write custom code aggregating gradients outside the optimizer.
`**kwargs`	keyword arguments only used for backward compatibility.

Raises
`TypeError`	If `grads_and_vars` is malformed.
`RuntimeError`	If called in a cross-replica context.

Args
`loss`	`Tensor` or callable. If a callable, `loss` should take no arguments and return 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.
`tape`	(Optional) `tf.GradientTape`. If `loss` is provided as a `Tensor`, the tape that computed the `loss` must be provided.

Args
`var_list`	A list of `tf.Variable`s to exclude from weight decay.
`var_names`	A list of strings. If any string in `var_names` appear in the model variable's name, then this model variable is excluded from weight decay. For example, `var_names=['bias']` excludes all bias variables from weight decay.

Args
`config`	A Python dictionary, typically the output of get_config.
`custom_objects`	A Python dictionary mapping names to additional user-defined Python objects needed to recreate this optimizer.

Args
`gradient`	backpropagated gradient of the given variable.
`variable`	variable whose value needs to be updated.

tf.keras.optimizers.Optimizer

View aliases

Args

Usage

Processing gradients before applying them

Example:

Dynamic learning rate

Example:

Gradients clipping

Example:

Using weight decay.

Using exponential moving average.

Example:

Use with tf.distribute.Strategy

Creating a custom optimizer

Attributes

Methods

add_variable

add_variable_from_reference

aggregate_gradients

apply_gradients

build

compute_gradients

exclude_from_weight_decay

finalize_variable_values

from_config

get_config

load_own_variables

minimize

save_own_variables

set_weights

update_step

Use with `tf.distribute.Strategy`

`add_variable`

`add_variable_from_reference`

`aggregate_gradients`

`apply_gradients`

`build`

`compute_gradients`

`exclude_from_weight_decay`

`finalize_variable_values`

`from_config`

`get_config`

`load_own_variables`

`minimize`

`save_own_variables`

`set_weights`

`update_step`