# tf.contrib.estimator.TowerOptimizer

## Class TowerOptimizer

Inherits From: Optimizer

Gathers gradients from all towers and reduces them in the last one.

## Methods

### __init__

__init__(optimizer_or_optimizer_fn)


Wrap an existing optimizer for gathering gradients across towers.

Each invocation of model_fn has to call the same optimizers in the same order.

Multiple optimizers that use the same or different losses are supported.

If TowerOptimizer is used but replicate_model_fn isn't, then no aggregation will happen. All calls will simply be forwarded to the underlying optimizer. The behavior is similar if there is only one tower.

If TowerOptimizer is used together with SyncReplicasOptimizer that wraps the user's optimizer, then it's the SyncReplicasOptimizer that needs to be wrapped with TowerOptimizer.

#### 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.

### apply_gradients

apply_gradients(
global_step=None,
**kwargs
)


### 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
)


### get_slot_names

get_slot_names(
*args,
**kwargs
)


### has_been_used

@staticmethod
has_been_used()


### minimize

minimize(
loss,
global_step=None,
var_list=None,
aggregation_method=None,
name=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 elements of var_list as arguments and computes the value to be minimized. If var_list is None, loss should take no arguments. 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
)