tf.data.experimental.OptimizationOptions

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Represents options for dataset optimizations.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.data.experimental.OptimizationOptions

tf.data.experimental.OptimizationOptions()

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.noop_elimination = True
options.experimental_optimization.apply_default_optimizations = False
dataset = dataset.with_options(options)

Attributes
apply_default_optimizations	Whether to apply default graph optimizations. If False, only graph optimizations that have been explicitly enabled will be applied.
filter_fusion	Whether to fuse filter transformations. If None, defaults to False.
filter_parallelization	Whether to parallelize stateless filter 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 True.
noop_elimination	Whether to eliminate no-op transformations. If None, defaults to True.
parallel_batch	Whether to parallelize copying of batch elements. If None, defaults to True.
shuffle_and_repeat_fusion	Whether to fuse shuffle and repeat transformations. If None, defaults to True.

Methods

__eq__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.