LostTech.TensorFlow : API Documentation

Type BatchConfig

Namespace tensorflow_estimator.python.estimator.tpu.tpu_estimator

Parent PythonObjectContainer

Interfaces IBatchConfig

Public static methods

BatchConfig NewDyn(object num_batch_threads, object max_batch_size, object batch_timeout_micros, object allowed_batch_sizes, ImplicitContainer<T> max_enqueued_batches)

Configure a `CheckpointManager` for use in `directory`.

If a `CheckpointManager` was previously used in `directory`, its state will be restored. This includes the list of managed checkpoints and the timestamp bookkeeping necessary to support `keep_checkpoint_every_n_hours`. The behavior of the new `CheckpointManager` will be the same as the previous `CheckpointManager`, including cleaning up existing checkpoints if appropriate.

Checkpoints are only considered for deletion just after a new checkpoint has been added. At that point, `max_to_keep` checkpoints will remain in an "active set". Once a checkpoint is preserved by `keep_checkpoint_every_n_hours` it will not be deleted by this `CheckpointManager` or any future `CheckpointManager` instantiated in `directory` (regardless of the new setting of `keep_checkpoint_every_n_hours`). The `max_to_keep` checkpoints in the active set may be deleted by this `CheckpointManager` or a future `CheckpointManager` instantiated in `directory` (subject to its `max_to_keep` and `keep_checkpoint_every_n_hours` settings).

Public properties

object allowed_batch_sizes get; set;

object batch_timeout_micros get; set;

object max_batch_size get; set;

object max_enqueued_batches get; set;

object num_batch_threads get; set;

object PythonObject get;