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|
Wrapper optimizer that implements the Elastic Average SGD algorithm.
Inherits From: Optimizer
tf.contrib.opt.ElasticAverageOptimizer(
opt, num_worker, ea_custom_getter, communication_period=10, moving_rate=None,
rho=None, use_locking=True, synchronous=False, name='ElasticAverageOptimizer'
)
This is an async 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 communication period divides the local step, the worker requests the current global center variables and then computed the elastic difference between global center variables and local variables. The elastic difference then be used to update both local variables and global variables.
Args | |
|---|---|
opt
|
The actual optimizer that will be used to update local variables. Must be one of the Optimizer classes. |
num_worker
|
The number of workers |
ea_custom_getter
|
The ElasticAverageCustomGetter |
communication_period
|
An int point value to controls the frequency of the communication between every worker and the ps. |
moving_rate
|
A floating point value to control the elastic difference. |
rho
|
the amount of exploration we allow in the model. The default value is moving_rate/learning_rate rho=0.0 is suggested in async mode. |
use_locking
|
If True use locks for update operations. |
synchronous
|
Add_sync_queues_and_barrier or not. True: all workers will wait for each other before start training False: worker can start training when its initilization is done, no need to wait for everyone is ready. in case one worker is restarted, it can join and continue training without being blocked. |
name
|
Optional name prefix for the operations created when applying gradients. Defaults to "ElasticAverageOptimizer". |
Methods
apply_gradients
apply_gradients(
grads_and_vars, global_step=None, name=None
)
Apply gradients to global 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 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 | |
|---|---|
An Operation that applies the specified gradients. If global_step
was not None, that operation also increments global_step.
|
| Raises | |
|---|---|
TypeError
|
If grads_and_vars is malformed.
|
ValueError
|
If none of the variables have gradients. |
compute_gradients
compute_gradients(
loss, var_list=None, gate_gradients=optimizer.Optimizer.GATE_OP,
aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None
)
Compute gradients of loss for the variables in var_list.
Add rho*elastic_difference to loss to control the exploration
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. |
var_list
|
Optional list or tuple of tf.Variable to update to minimize
loss. Defaults to the list of variables collected in the graph under
the key GraphKey.TRAINABLE_VARIABLES.
|
gate_gradients
|
How to gate the computation of gradients. Can be
GATE_NONE, GATE_OP, or GATE_GRAPH.
|
aggregation_method
|
Specifies the method used to combine gradient terms.
Valid values are defined in the class AggregationMethod.
|
colocate_gradients_with_ops
|
If True, try colocating gradients with the corresponding op. |
grad_loss
|
Optional. A Tensor holding the gradient computed for loss.
|
| Returns | |
|---|---|
A list of (gradient, variable) pairs. Variable is always present, but
gradient can be None.
|
| Raises | |
|---|---|
TypeError
|
If var_list contains anything else than Variable objects.
|
ValueError
|
If some arguments are invalid. |
get_init_op
get_init_op(
task_index
)
Returns the op to let all the local variables and local center
variables equal to the global center variables before the training begins
get_name
get_name()
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 to minimize() or apply_gradients().
|
name
|
A string. |
| Returns | |
|---|---|
The Variable for the slot if it was created, None otherwise.
|
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. |
make_session_run_hook
make_session_run_hook(
is_chief, task_index
)
Creates a hook to handle ElasticAverageOptimizerHook ops such as initialization.
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
|
A Tensor containing the value to minimize.
|
global_step
|
Optional Variable to increment by one after the
variables have been updated.
|
var_list
|
Optional list or tuple of Variable objects to update to
minimize loss. Defaults to the list of variables collected in
the graph under the key GraphKeys.TRAINABLE_VARIABLES.
|
gate_gradients
|
How to gate the computation of gradients. Can be
GATE_NONE, GATE_OP, or GATE_GRAPH.
|
aggregation_method
|
Specifies the method used to combine gradient terms.
Valid values are defined in the class AggregationMethod.
|
colocate_gradients_with_ops
|
If True, try colocating gradients with the corresponding op. |
name
|
Optional name for the returned operation. |
grad_loss
|
Optional. A Tensor holding the gradient computed for loss.
|
| 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.
|
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.
swapping_saver
swapping_saver(
var_list=None, name='swapping_saver', **kwargs
)
Create a saver copy global_center_variable to trainable variables
Please call this function after all your variables created with
ElasticAverageCustomGetter. For evaluations or inference, use this saver
during training. It will save the global_center_variable of the trained
parameters under the original parameter names.
Args:
var_list: List of variables to save, as per Saver(). If set to None,
save all the trainable_variables that have been created before this
call.
name: The name of the saver.
**kwargs: Keyword arguments of Saver().
| Returns | |
|---|---|
A tf.compat.v1.train.Saver object.
|
| Raises | |
|---|---|
RuntimeError
|
global_center_variable is empty, please make sure this is called after model created and ElasticAverageCustomGetter is used when declaring you model |
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. |