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tf.data.experimental.OptimizationOptions

Class OptimizationOptions

Defined in tensorflow/python/data/experimental/ops/optimization_options.py.

Represents options for dataset optimizations.

You can set the optimization options of a dataset through the experimental_optimization property of tf.data.Options; the property is an instance of tf.data.experimental.OptimizationOptions.

options = tf.data.Options()
options.experimental_optimization.map_vectorization = True
options.apply_default_optimizations = False
dataset = dataset.with_options(options)

__init__

__init__()

Initialize self. See help(type(self)) for accurate signature.

Properties

apply_default_optimizations

Whether to apply default static optimizations. If False, only static optimizations that have been explicitly enabled will be applied.

filter_fusion

Whether to fuse filter transformations. If None, defaults to False.

hoist_random_uniform

Whether to hoist tf.random_uniform() ops out of map transformations. If None, defaults to False.

map_and_batch_fusion

Whether to fuse map and batch transformations. If None, defaults to True.

map_and_filter_fusion

Whether to fuse map and filter transformations. If None, defaults to False.

map_fusion

Whether to fuse map transformations. If None, defaults to False.

map_parallelization

Whether to parallelize stateless map transformations. If None, defaults to False.

map_vectorization

Whether to vectorize map transformations. If None, defaults to False.

noop_elimination

Whether to eliminate no-op transformations. If None, defaults to True.

shuffle_and_repeat_fusion

Whether to fuse shuffle and repeat transformations. If None, defaults to True.

Methods

__eq__

__eq__(other)

Return self==value.

__ne__

__ne__(other)

Return self!=value.

__setattr__

__setattr__(
    name,
    value
)

Implement setattr(self, name, value).