tf.contrib.opt.ExternalOptimizerInterface

class tf.contrib.opt.ExternalOptimizerInterface

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

Base class for interfaces with external optimization algorithms.

Subclass this and implement _minimize in order to wrap a new optimization algorithm.

ExternalOptimizerInterface should not be instantiated directly; instead use e.g. ScipyOptimizerInterface.

Methods

__init__

__init__(
    loss,
    var_list=None,
    equalities=None,
    inequalities=None,
    **optimizer_kwargs
)

Initialize a new interface instance.

Args:

  • loss: A scalar Tensor to be minimized.
  • 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.
  • equalities: Optional list of equality constraint scalar Tensors to be held equal to zero.
  • inequalities: Optional list of inequality constraint scalar Tensors to be kept nonnegative. **optimizer_kwargs: Other subclass-specific keyword arguments.

minimize

minimize(
    session=None,
    feed_dict=None,
    fetches=None,
    step_callback=None,
    loss_callback=None,
    **run_kwargs
)

Minimize a scalar Tensor.

Variables subject to optimization are updated in-place at the end of optimization.

Note that this method does not just return a minimization Op, unlike Optimizer.minimize(); instead it actually performs minimization by executing commands to control a Session.

Args:

  • session: A Session instance.
  • feed_dict: A feed dict to be passed to calls to session.run.
  • fetches: A list of Tensors to fetch and supply to loss_callback as positional arguments.
  • step_callback: A function to be called at each optimization step; arguments are the current values of all optimization variables flattened into a single vector.
  • loss_callback: A function to be called every time the loss and gradients are computed, with evaluated fetches supplied as positional arguments. **run_kwargs: kwargs to pass to session.run.