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|
Gathers gradients from all towers and reduces them in the last one.
Inherits From: Optimizer
tf.contrib.estimator.TowerOptimizer(
optimizer_or_optimizer_fn
)
Args | |
|---|---|
optimizer_or_optimizer_fn
|
an instance of optimizer to wrap. That instance is going to be used for optimizer-specific logic. This can also be a no-argument function that returns such an optimizer instance. |
Methods
apply_gradients
apply_gradients(
grads_and_vars, global_step=None, **kwargs
)
Collect gradients updates to apply them with the last tower.
compute_gradients
compute_gradients(
loss, *args, **kwargs
)
Compute gradients, but first, if needed, scale the loss.
get_name
get_name(
*args, **kwargs
)
get_slot
get_slot(
*args, **kwargs
)
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(
*args, **kwargs
)
Return a list of the names of slots created by the Optimizer.
See get_slot().
| Returns | |
|---|---|
| A list of strings. |
has_been_used
@staticmethodhas_been_used()
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.
variables
variables(
*args, **kwargs
)
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. |