Class LazyAdamOptimizer
Inherits From: AdamOptimizer
Defined in tensorflow/contrib/opt/python/training/lazy_adam_optimizer.py
.
Variant of the Adam optimizer that handles sparse updates more efficiently.
The original Adam algorithm maintains two moving-average accumulators for each trainable variable; the accumulators are updated at every step. This class provides lazier handling of gradient updates for sparse variables. It only updates moving-average accumulators for sparse variable indices that appear in the current batch, rather than updating the accumulators for all indices. Compared with the original Adam optimizer, it can provide large improvements in model training throughput for some applications. However, it provides slightly different semantics than the original Adam algorithm, and may lead to different empirical results.
__init__
__init__(
learning_rate=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-08,
use_locking=False,
name='Adam'
)
Construct a new Adam optimizer.
Initialization:
The update rule for variable
with gradient g
uses an optimization
described at the end of section2 of the paper:
The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the formulation just before Section 2.1 of the Kingma and Ba paper rather than the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon hat" in the paper.
The sparse implementation of this algorithm (used when the gradient is an
IndexedSlices object, typically because of tf.gather
or an embedding
lookup in the forward pass) does apply momentum to variable slices even if
they were not used in the forward pass (meaning they have a gradient equal
to zero). Momentum decay (beta1) is also applied to the entire momentum
accumulator. This means that the sparse behavior is equivalent to the dense
behavior (in contrast to some momentum implementations which ignore momentum
unless a variable slice was actually used).
Args:
learning_rate
: A Tensor or a floating point value. The learning rate.beta1
: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.beta2
: A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.epsilon
: A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.use_locking
: If True use locks for update operations.name
: Optional name for the operations created when applying gradients. Defaults to "Adam".
Eager Compatibility
When eager execution is enabled, learning_rate
, beta1
, beta2
, and
epsilon
can each be a callable that takes no arguments and returns the
actual value to use. This can be useful for changing these values across
different invocations of optimizer functions.
Methods
tf.contrib.opt.LazyAdamOptimizer.apply_gradients
apply_gradients(
grads_and_vars,
global_step=None,
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 as returned bycompute_gradients()
.global_step
: OptionalVariable
to increment by one after the variables have been updated.name
: Optional name for the returned operation. Default to the name passed to theOptimizer
constructor.
Returns:
An Operation
that applies the specified gradients. If global_step
was not None, that operation also increments global_step
.
Raises:
TypeError
: Ifgrads_and_vars
is malformed.ValueError
: If none of the variables have gradients.RuntimeError
: If you should use_distributed_apply()
instead.
tf.contrib.opt.LazyAdamOptimizer.compute_gradients
compute_gradients(
loss,
var_list=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
grad_loss=None
)
Compute gradients of loss
for the variables in var_list
.
This is the first part of minimize()
. It returns a list
of (gradient, variable) pairs where "gradient" is the gradient
for "variable". Note that "gradient" can be a Tensor
, an
IndexedSlices
, or None
if there is no gradient for the
given variable.
Args:
loss
: A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.var_list
: Optional list or tuple oftf.Variable
to update to minimizeloss
. Defaults to the list of variables collected in the graph under the keyGraphKeys.TRAINABLE_VARIABLES
.gate_gradients
: How to gate the computation of gradients. Can beGATE_NONE
,GATE_OP
, orGATE_GRAPH
.aggregation_method
: Specifies the method used to combine gradient terms. Valid values are defined in the classAggregationMethod
.colocate_gradients_with_ops
: If True, try colocating gradients with the corresponding op.grad_loss
: Optional. ATensor
holding the gradient computed forloss
.
Returns:
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be None
.
Raises:
TypeError
: Ifvar_list
contains anything else thanVariable
objects.ValueError
: If some arguments are invalid.RuntimeError
: If called with eager execution enabled andloss
is not callable.
Eager Compatibility
When eager execution is enabled, gate_gradients
, aggregation_method
,
and colocate_gradients_with_ops
are ignored.
tf.contrib.opt.LazyAdamOptimizer.get_name
get_name()
tf.contrib.opt.LazyAdamOptimizer.get_slot
get_slot(
var,
name
)
Return a slot named name
created for var
by the Optimizer.
Some Optimizer
subclasses use additional variables. For example
Momentum
and Adagrad
use variables to accumulate updates. This method
gives access to these Variable
objects if for some reason you need them.
Use get_slot_names()
to get the list of slot names created by the
Optimizer
.
Args:
var
: A variable passed tominimize()
orapply_gradients()
.name
: A string.
Returns:
The Variable
for the slot if it was created, None
otherwise.
tf.contrib.opt.LazyAdamOptimizer.get_slot_names
get_slot_names()
Return a list of the names of slots created by the Optimizer
.
See get_slot()
.
Returns:
A list of strings.
tf.contrib.opt.LazyAdamOptimizer.minimize
minimize(
loss,
global_step=None,
var_list=None,
gate_gradients=GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None
)
Add operations to minimize loss
by updating var_list
.
This method simply combines calls compute_gradients()
and
apply_gradients()
. If you want to process the gradient before applying
them call compute_gradients()
and apply_gradients()
explicitly instead
of using this function.
Args:
loss
: ATensor
containing the value to minimize.global_step
: OptionalVariable
to increment by one after the variables have been updated.var_list
: Optional list or tuple ofVariable
objects to update to minimizeloss
. Defaults to the list of variables collected in the graph under the keyGraphKeys.TRAINABLE_VARIABLES
.gate_gradients
: How to gate the computation of gradients. Can beGATE_NONE
,GATE_OP
, orGATE_GRAPH
.aggregation_method
: Specifies the method used to combine gradient terms. Valid values are defined in the classAggregationMethod
.colocate_gradients_with_ops
: If True, try colocating gradients with the corresponding op.name
: Optional name for the returned operation.grad_loss
: Optional. ATensor
holding the gradient computed forloss
.
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 notVariable
objects.
Eager Compatibility
When eager execution is enabled, loss
should be a Python function that
takes no arguments and computes the value to be minimized. Minimization (and
gradient computation) is done with respect to the elements of var_list
if
not None, else with respect to any trainable variables created during the
execution of the loss
function. gate_gradients
, aggregation_method
,
colocate_gradients_with_ops
and grad_loss
are ignored when eager
execution is enabled.
tf.contrib.opt.LazyAdamOptimizer.variables
variables()
A list of variables which encode the current state of Optimizer
.
Includes slot variables and additional global variables created by the optimizer in the current default graph.
Returns:
A list of variables.