tf.contrib.estimator.TowerOptimizer

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

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apply_gradients(
    grads_and_vars, global_step=None, **kwargs
)

Collect gradients updates to apply them with the last tower.

compute_gradients

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compute_gradients(
    loss, *args, **kwargs
)

Compute gradients, but first, if needed, scale the loss.

get_name

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get_name(
    *args, **kwargs
)

get_slot

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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

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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

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@staticmethod
has_been_used()

minimize

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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

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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.

Class Variables

• COLLECTION_FOR_GRAPH_STATES = 'replicate_model_fn_graph_states'
• GATE_GRAPH = 2
• GATE_NONE = 0
• GATE_OP = 1