tf.contrib.opt.LARSOptimizer

Class LARSOptimizer

Inherits From: Optimizer

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

Layer-wise Adaptive Rate Scaling for large batch training.

Introduced by "Large Batch Training of Convolutional Networks" by Y. You, I. Gitman, and B. Ginsburg. (https://arxiv.org/abs/1708.03888)

Implements the LARS learning rate scheme presented in the paper above. This optimizer is useful when scaling the batch size to up to 32K without significant performance degradation. It is recommended to use the optimizer in conjunction with: - Gradual learning rate warm-up - Linear learning rate scaling - Poly rule learning rate decay

Note, LARS scaling is currently only enabled for dense tensors. Sparse tensors use the default momentum optimizer.

__init__

__init__(
    learning_rate,
    momentum=0.9,
    weight_decay=0.0001,
    eeta=0.001,
    epsilon=0.0,
    name='LARSOptimizer',
    skip_list=None,
    use_nesterov=False
)

Construct a new LARS Optimizer.

Args:

  • learning_rate: A Tensor or floating point value. The base learning rate.
  • momentum: A floating point value. Momentum hyperparameter.
  • weight_decay: A floating point value. Weight decay hyperparameter.
  • eeta: LARS coefficient as used in the paper. Dfault set to LARS coefficient from the paper. (eeta / weight_decay) determines the highest scaling factor in LARS.
  • epsilon: Optional epsilon parameter to be set in models that have very small gradients. Default set to 0.0.
  • name: Optional name prefix for variables and ops created by LARSOptimizer.
  • skip_list: List of strings to enable skipping variables from LARS scaling. If any of the strings in skip_list is a subset of var.name, variable 'var' is skipped from LARS scaling. For a typical classification model with batch normalization, the skip_list is ['batch_normalization', 'bias']
  • use_nesterov: when set to True, nesterov momentum will be enabled

Raises:

  • ValueError: If a hyperparameter is set to a non-sensical value.

Methods

apply_gradients

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

Apply gradients to variables.

This is the second part of minimize(). It returns an Operation that applies gradients.

Args:

  • grads_and_vars: List of (gradient, variable) pairs as returned by compute_gradients().
  • global_step: Optional Variable to increment by one after the variables have been updated.
  • name: Optional name for the returned operation. Default to the name passed to the Optimizer constructor.

Returns:

An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.

Raises:

  • TypeError: If grads_and_vars is malformed.
  • ValueError: If none of the variables have gradients.
  • RuntimeError: If you should use _distributed_apply() instead.

compute_gradients

compute_gradients(
    loss,
    var_list=None,
    gate_gradients=GATE_OP,
    aggregation_method=None,
    colocate_gradients_with_ops=False,
    grad_loss=None
)

Compute gradients of loss for the variables in var_list.

This is the first part of minimize(). It returns a list of (gradient, variable) pairs where "gradient" is the gradient for "variable". Note that "gradient" can be a Tensor, an IndexedSlices, or None if there is no gradient for the given variable.

Args:

  • loss: A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
  • var_list: Optional list or tuple of tf.Variable 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.
  • grad_loss: Optional. A Tensor holding the gradient computed for loss.

Returns:

A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.

Raises:

  • TypeError: If var_list contains anything else than Variable objects.
  • ValueError: If some arguments are invalid.
  • RuntimeError: If called with eager execution enabled and loss is not callable.

Eager Compatibility

When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored.

compute_lr

compute_lr(
    grad,
    var
)

get_name

get_name()

get_slot

get_slot(
    var,
    name
)

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()

Return a list of the names of slots created by the Optimizer.

See get_slot().

Returns:

A list of strings.

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()

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