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tf.data.experimental.OptimizationOptions

Represents options for dataset optimizations.

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)

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. inject_prefetch Whether to inject prefetch transformation as the last transformation when the last transformation is a synchronous transformation. If None, defaults to True. 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. seq_interleave_prefetch Whether to replace parallel interleave using a sequential interleave that prefetches elements from its input iterators. If None, defaults to False. 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.