tf.contrib.opt.VariableClippingOptimizer

class tf.contrib.opt.VariableClippingOptimizer

Wrapper optimizer that clips the norm of specified variables after update.

This optimizer delegates all aspects of gradient calculation and application to an underlying optimizer. After applying gradients, this optimizer then clips the variable to have a maximum L2 norm along specified dimensions. NB: this is quite different from clipping the norm of the gradients.

Multiple instances of VariableClippingOptimizer may be chained to specify different max norms for different subsets of variables.

This is more efficient at serving-time than using normalization during embedding lookup, at the expense of more expensive training and fewer guarantees about the norms.

Methods

__init__(opt, vars_to_clip_dims, max_norm, use_locking=False, colocate_clip_ops_with_vars=False, name='VariableClipping')

Construct a new clip-norm optimizer.

Args:

  • opt: The actual optimizer that will be used to compute and apply the gradients. Must be one of the Optimizer classes.
  • vars_to_clip_dims: A dict with keys as Variables and values as lists of dimensions along which to compute the L2-norm. See tf.clip_by_norm for more details.
  • max_norm: The L2-norm to clip to, for all variables specified.
  • use_locking: If True use locks for clip update operations.
  • colocate_clip_ops_with_vars: If True, try colocating the clip norm ops with the corresponding variable.
  • name: Optional name prefix for the operations created when applying gradients. Defaults to "VariableClipping".

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

compute_gradients(*args, **kwargs)

get_name()

get_slot(*args, **kwargs)

get_slot_names(*args, **kwargs)

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 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.

Class Members

GATE_GRAPH

GATE_NONE

GATE_OP

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