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.
shuffle_and_repeat_fusion Whether to fuse shuffle and repeat transformations. If None, defaults to True.
warm_start Whether to start background threads of asynchronous transformations upon iterator creation (as opposed to upon first call to GetNext). If None, defaults to False. It should be noted that this possibly improves the latency of the initial 'GetNext' call at the expense of requiring more memory to hold prefetched elements between the time of iterator construction and usage.

Methods

__eq__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.