LostTech.TensorFlow : API Documentation

Type CheckpointInputPipelineHook

Namespace tensorflow.data.experimental

Parent SessionRunHook

Interfaces ICheckpointInputPipelineHook

Checkpoints input pipeline state every N steps or seconds.

This hook saves the state of the iterators in the `Graph` so that when training is resumed the input pipeline continues from where it left off. This could potentially avoid overfitting in certain pipelines where the number of training steps per eval are small compared to the dataset size or if the training pipeline is pre-empted.

Differences from `CheckpointSaverHook`: 1. Saves only the input pipelines in the "iterators" collection and not the global variables or other saveable objects. 2. Does not write the `GraphDef` and `MetaGraphDef` to the summary.

Example of checkpointing the training pipeline: This hook should be used if the input pipeline state needs to be saved separate from the model checkpoint. Doing so may be useful for a few reasons: 1. The input pipeline checkpoint may be large, if there are large shuffle or prefetch buffers for instance, and may bloat the checkpoint size. 2. If the input pipeline is shared between training and validation, restoring the checkpoint during validation may override the validation input pipeline.

For saving the input pipeline checkpoint alongside the model weights use tf.data.experimental.make_saveable_from_iterator directly to create a `SaveableObject` and add to the `SAVEABLE_OBJECTS` collection. Note, however, that you will need to be careful not to restore the training iterator during eval. You can do that by not adding the iterator to the SAVEABLE_OBJECTS collector when building the eval graph.
Show Example
est = tf.estimator.Estimator(model_fn)
            while True:
              # Note: We do not pass the hook here.
              metrics = est.evaluate(eval_input_fn)
              if should_stop_the_training(metrics):


Public properties

object PythonObject get;