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)
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.
|
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.
|
Methods
__eq__
View source
__eq__(
other
)
Return self==value.
__ne__
View source
__ne__(
other
)
Return self!=value.