tf.contrib.opt.DropStaleGradientOptimizer

Class DropStaleGradientOptimizer

Inherits From: Optimizer

Defined in tensorflow/contrib/opt/python/training/drop_stale_gradient_optimizer.py.

Wrapper optimizer that checks and drops stale gradient.

This optimizer records the global step for each worker before computing gradients and compares it with the global step at the time of applying the gradients. If the difference is larger than a threshold, it will drop all the computed gradients.

Methods

__init__

__init__(
    opt,
    staleness,
    use_locking=False,
    name='DropStaleGradient'
)

Constructs a new DropStaleGradientOptimizer.

Args:

  • opt: The actual optimizer that will be used to compute and apply the gradients. Must be one of the Optimizer classes.
  • staleness: The maximum staleness allowed for the optimizer.
  • use_locking: If True use locks for clip update operations.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "DropStaleGradient".

apply_gradients

apply_gradients(
    grads_and_vars,
    global_step=None,
    name=None
)

compute_gradients

compute_gradients(
    loss,
    *args,
    **kwargs
)

get_name

get_name()

get_slot

get_slot(
    *args,
    **kwargs
)

get_slot_names

get_slot_names(
    *args,
    **kwargs
)

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 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()

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 Members

GATE_GRAPH

GATE_NONE

GATE_OP