tf.data.experimental.OptimizationOptions     View source on GitHub    Represents options for dataset optimizations.  View aliases  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.map_vectorization.enabled = 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.  
  autotune   Whether to automatically tune performance knobs. If None, defaults to True.  
  autotune_buffers   When autotuning is enabled (through autotune), determines whether to also autotune buffer sizes for datasets with parallelism. If None, defaults to False.  
  autotune_cpu_budget   When autotuning is enabled (through autotune), determines the CPU budget to use. Values greater than the number of schedulable CPU cores are allowed but may result in CPU contention. If None, defaults to the number of schedulable CPU cores.  
  autotune_ram_budget   When autotuning is enabled (through autotune), determines the RAM budget to use. Values greater than the available RAM in bytes may result in OOM. If None, defaults to half of the available RAM in bytes.  
  filter_fusion   Whether to fuse filter transformations. If None, defaults to False.  
  filter_with_random_uniform_fusion   Whether to fuse filter dataset that predicts random_uniform < rate into a sampling dataset. 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   The map vectorization options associated with the dataset. See tf.data.experimental.MapVectorizationOptions for more details.  
  noop_elimination   Whether to eliminate no-op transformations. If None, defaults to True.  
  parallel_batch   Whether to parallelize copying of batch elements. This optimization is highly experimental and can cause performance degradation (e.g. when the parallelization overhead exceeds the benefits of performing the data copies in parallel). You should only enable this optimization if a) your input pipeline is bottlenecked on batching and b) you have validated that this optimization improves performance. If None, defaults to False.  
  reorder_data_discarding_ops   Whether to reorder ops that will discard data to the front of unary cardinality preserving transformations, e.g. dataset.map(...).take(3) will be optimized to dataset.take(3).map(...). For now this optimization will move skip, shard and take to the front of map and prefetch. This optimization is only for performance; it will not affect the output of the dataset. 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.