Class ModelAverageOptimizer
Inherits From: Optimizer
Defined in tensorflow/contrib/opt/python/training/model_average_optimizer.py
.
Wrapper optimizer that implements the Model Average algorithm.
This is a sync optimizer. During the training, each worker will update the local variables and maintains its own local_step, which starts from 0 and is incremented by 1 after each update of local variables. Whenever the interval_steps divides the local step, the local variables from all the workers will be averaged and assigned to global center variables. Then the local variables will be assigned by global center variables.
__init__
__init__(
opt,
num_worker,
is_chief,
ma_custom_getter,
interval_steps=100,
use_locking=True,
name='ModelAverageOptimizer'
)
Construct a new model average optimizer.
Args:
opt
: The actual optimizer that will be used to update local variablesnum_worker
: The number of workersis_chief
: whether chief workerma_custom_getter
: ModelAverageCustomGetterinterval_steps
: An int point value to controls the frequency of the average of local variablesuse_locking
: If True use locks for update operationsname
: string. Optional name of the returned operation
Methods
tf.contrib.opt.ModelAverageOptimizer.apply_gradients
apply_gradients(
grads_and_vars,
global_step=None,
name=None
)
Apply gradients to variables.
This contains most of the synchronization implementation and also wraps the apply_gradients() from the real optimizer. The chief work updates global variables.
Args:
grads_and_vars
: List of (gradient, variable) pairs as returned by compute_gradients().global_step
: Optional Variable to increment by one after the variables have been updated.name
: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.
Returns:
A conditional 'Operation' that update both local and global variables or just local variables
Raises:
ValueError
: If the grads_and_vars is empty.ValueError
: If global step is not provided, the staleness cannot be checked.
tf.contrib.opt.ModelAverageOptimizer.compute_gradients
compute_gradients(
*args,
**kwargs
)
Compute gradients of "loss" for the variables in "var_list".
This simply wraps the compute_gradients() from the real optimizer.
Args:
*args
: Arguments for compute_gradients().**kwargs
: Keyword arguments for compute_gradients().
Returns:
A list of (gradient, variable) pairs.
tf.contrib.opt.ModelAverageOptimizer.get_init_op
get_init_op()
Returns the op.
This method lets all the local variables equal to the global variables before the training begins.
tf.contrib.opt.ModelAverageOptimizer.get_name
get_name()
tf.contrib.opt.ModelAverageOptimizer.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.ModelAverageOptimizer.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.ModelAverageOptimizer.make_session_run_hook
make_session_run_hook()
Creates a hook to handle ModelAverage ops such as initialization.
tf.contrib.opt.ModelAverageOptimizer.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.ModelAverageOptimizer.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.