RewriterConfigOrBuilder

antarmuka publik RewriterConfigOrBuilder

Subkelas Tidak Langsung yang Diketahui RewriterConfig , RewriterConfig.Builder Konfigurasi Penulis Ulang Graph rewriting is experimental and subject to change, not covered by any API stability guarantees.  Penulis UlangConfig.Builder Graph rewriting is experimental and subject to change, not covered by any API stability guarantees.

Metode Publik

abstrak RewriterConfig.Toggle	dapatkan Optimasi Aritmatika () Arithmetic optimizations (default is ON) e.g.
abstrak ke dalam	dapatkanNilai Optimasi Aritmatika () Arithmetic optimizations (default is ON) e.g.
abstrak RewriterConfig.Toggle	dapatkan Presisi Campuran Otomatis () Optimize data types for CUDA (default is OFF).
abstrak RewriterConfig.Toggle	dapatkanAutoMixedPrecisionMkl () Optimize data types for MKL (default is OFF).
abstrak ke dalam	dapatkanAutoMixedPrecisionMklValue () Optimize data types for MKL (default is OFF).
abstrak ke dalam	dapatkanAutoMixedPrecisionValue () Optimize data types for CUDA (default is OFF).
abstrak AutoParallelOptions	dapatkanAutoParallel () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
abstrak AutoParallelOptionsOrBuilder	dapatkanAutoParallelOrBuilder () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
abstrak RewriterConfig.Toggle	getCommonSubgraphElimination () Common subgraph elimination (default is ON) e.g.
abstrak ke dalam	dapatkanCommonSubgraphEliminationValue () Common subgraph elimination (default is ON) e.g.
abstrak RewriterConfig.Toggle	dapatkan Lipatan Konstan () Fold constants (default is ON) Statically infer the value of tensors when possible, and materialize the result using constants.
abstrak ke dalam	dapatkanConstantFoldingValue () Fold constants (default is ON) Statically infer the value of tensors when possible, and materialize the result using constants.
abstrak RewriterConfig.CpuLayout	dapatkanCpuLayoutConversion () CPU Conversion settings between NHCW and NCHW.
abstrak ke dalam	dapatkanCpuLayoutConversionValue () CPU Conversion settings between NHCW and NCHW.
abstrak RewriterConfig.CustomGraphOptimizer	getCustomOptimizers (indeks int) list of CustomGraphOptimizers to apply.
abstrak ke dalam	dapatkanCustomOptimizersCount () list of CustomGraphOptimizers to apply.
Daftar abstrak< RewriterConfig.CustomGraphOptimizer >	dapatkanDaftar Pengoptimal Khusus () list of CustomGraphOptimizers to apply.
abstrak RewriterConfig.CustomGraphOptimizerOrBuilder	getCustomOptimizersOrBuilder (int indeks) list of CustomGraphOptimizers to apply.
Daftar abstrak<? memperluas RewriterConfig.CustomGraphOptimizerOrBuilder >	dapatkanCustomOptimizersOrBuilderList () list of CustomGraphOptimizers to apply.
abstrak RewriterConfig.Toggle	dapatkanDebugStripper () Strips debug-related nodes from the graph (off by default).
abstrak ke dalam	dapatkanDebugStripperValue () Strips debug-related nodes from the graph (off by default).
abstrak RewriterConfig.Toggle	dapatkan DependencyOptimization () Control dependency optimizations (default is ON).
abstrak ke dalam	dapatkanDependencyOptimizationValue () Control dependency optimizations (default is ON).
boolean abstrak	dapatkanDisableMetaOptimizer () Disable the entire meta optimizer (off by default).
boolean abstrak	getDisableModelPruning () If true, don't remove unnecessary ops from the graph bool disable_model_pruning = 2;
boolean abstrak	dapatkanExperimentalDisableCompressedTensorOptimization () Disable optimizations that assume compressed tensors.
boolean abstrak	getFailOnOptimizerErrors () If true, any optimization pass failing will cause the MetaOptimizer to stop with an error.
abstrak RewriterConfig.Toggle	dapatkanFunctionOptimization () Function optimizations (default is ON).
abstrak ke dalam	dapatkanFunctionOptimizationValue () Function optimizations (default is ON).
abstrak RewriterConfig.Toggle	dapatkanImplementationSelector () Enable the swap of kernel implementations based on the device placement (default is ON).
abstrak ke dalam	dapatkanImplementationSelectorValue () Enable the swap of kernel implementations based on the device placement (default is ON).
abstrak VerifierConfig	dapatkanInterOptimizerVerifierConfig () VerifierConfig specifying the verifiers to be run after every optimizer.
abstrak VerifierConfigOrBuilder	dapatkanInterOptimizerVerifierConfigOrBuilder () VerifierConfig specifying the verifiers to be run after every optimizer.
abstrak RewriterConfig.Toggle	dapatkanLayoutOptimizer () Optimize tensor layouts (default is ON) e.g.
abstrak ke dalam	dapatkanLayoutOptimizerValue () Optimize tensor layouts (default is ON) e.g.
abstrak RewriterConfig.Toggle	dapatkanLoopOptimasi () Loop optimizations (default is ON).
abstrak ke dalam	dapatkanLoopOptimizationValue () Loop optimizations (default is ON).
abstrak RewriterConfig.MemOptType	dapatkan Optimasi Memori () Configures memory optimization passes through the meta-optimizer.
abstrak ke dalam	dapatkanMemoryOptimizationValue () Configures memory optimization passes through the meta-optimizer.
Tali abstrak	dapatkanMemoryOptimizerTargetNodeNameScope () A node name scope for node names which are valid outputs of recomputations.
abstrak com.google.protobuf.ByteString	getMemoryOptimizerTargetNodeNameScopeBytes () A node name scope for node names which are valid outputs of recomputations.
abstrak RewriterConfig.NumIterationsType	dapatkanMetaOptimizerIterations () Controls how many times we run the optimizers in meta optimizer (default is once).
abstrak ke dalam	dapatkanMetaOptimizerIterationsValue () Controls how many times we run the optimizers in meta optimizer (default is once).
abstrak panjang	dapatkanMetaOptimizerTimeoutMs () Maximum number of milliseconds to spend optimizing a single graph before timing out.
abstrak ke dalam	dapatkanMinGraphNodes () The minimum number of nodes in a graph to optimizer.
Tali abstrak	getOptimizers (indeks int) If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
abstrak com.google.protobuf.ByteString	getOptimizersBytes (indeks int) If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
abstrak ke dalam	dapatkanOptimizersCount () If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
Daftar abstrak<String>	dapatkanDaftar Pengoptimal () If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
abstrak RewriterConfig.Toggle	dapatkanPinToHostOptimasi () Force small ops onto the CPU (default is OFF).
abstrak ke dalam	dapatkanPinToHostOptimizationValue () Force small ops onto the CPU (default is OFF).
abstrak VerifierConfig	dapatkanPostOptimizationVerifierConfig () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
abstrak VerifierConfigOrBuilder	dapatkanPostOptimizationVerifierConfigOrBuilder () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
abstrak RewriterConfig.Toggle	dapatkan pemetaan ulang () Remapping (default is ON) Remap subgraphs onto more efficient implementations.
abstrak ke dalam	dapatkan Pemetaan Ulang Nilai () Remapping (default is ON) Remap subgraphs onto more efficient implementations.
abstrak RewriterConfig.Toggle	getScopedAllocatorOptimization () Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops (off by default).
abstrak ke dalam	getScopedAllocatorOptimizationValue () Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops (off by default).
abstrak ScopedAllocatorOptions	dapatkanScopedAllocatorOpts () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
abstrak ScopedAllocatorOptionsOrBuilder	getScopedAllocatorOptsOrBuilder () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
abstrak RewriterConfig.Toggle	dapatkan Optimasi Bentuk () Shape optimizations (default is ON) Simplify computations made on shapes.
abstrak ke dalam	dapatkanBentukOptimasiNilai () Shape optimizations (default is ON) Simplify computations made on shapes.
boolean abstrak	memilikiAutoParallel () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
boolean abstrak	hasInterOptimizerVerifierConfig () VerifierConfig specifying the verifiers to be run after every optimizer.
boolean abstrak	hasPostOptimizationVerifierConfig () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
boolean abstrak	hasScopedAllocatorOpts () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

Metode Publik

abstrak publik RewriterConfig.Toggle getArithmeticOptimization ()

 Arithmetic optimizations (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;

abstrak publik int getArithmeticOptimizationValue ()

 Arithmetic optimizations (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;

abstrak publik RewriterConfig.Toggle getAutoMixedPrecision ()

 Optimize data types for CUDA (default is OFF).
 This will try to use float16 on GPU which is faster.
 Note that this can change the numerical stability of the graph and may
 require the use of loss scaling to maintain model convergence.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision = 23;

abstrak publik RewriterConfig.Toggle getAutoMixedPrecisionMkl ()

 Optimize data types for MKL (default is OFF).
 This will try to use bfloat16 on CPUs, which is faster.
 Note that this can change the numerical stability of the graph.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision_mkl = 25;

abstrak publik int getAutoMixedPrecisionMklValue ()

 Optimize data types for MKL (default is OFF).
 This will try to use bfloat16 on CPUs, which is faster.
 Note that this can change the numerical stability of the graph.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision_mkl = 25;

abstrak publik int getAutoMixedPrecisionValue ()

 Optimize data types for CUDA (default is OFF).
 This will try to use float16 on GPU which is faster.
 Note that this can change the numerical stability of the graph and may
 require the use of loss scaling to maintain model convergence.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision = 23;

abstrak publik AutoParallelOptions getAutoParallel ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

abstrak publik AutoParallelOptionsOrBuilder getAutoParallelOrBuilder ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

abstrak publik RewriterConfig.Toggle getCommonSubgraphElimination ()

 Common subgraph elimination (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle common_subgraph_elimination = 24;

abstrak publik int getCommonSubgraphEliminationValue ()

 Common subgraph elimination (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle common_subgraph_elimination = 24;

abstrak publik RewriterConfig.Toggle getConstantFolding ()

 Fold constants (default is ON)
 Statically infer the value of tensors when possible, and materialize the
 result using constants.
 

.tensorflow.RewriterConfig.Toggle constant_folding = 3;

abstrak publik int getConstantFoldingValue ()

 Fold constants (default is ON)
 Statically infer the value of tensors when possible, and materialize the
 result using constants.
 

.tensorflow.RewriterConfig.Toggle constant_folding = 3;

abstrak publik RewriterConfig.CpuLayout getCpuLayoutConversion ()

 CPU Conversion settings between NHCW and NCHW.
 

.tensorflow.RewriterConfig.CpuLayout cpu_layout_conversion = 50;

abstrak publik int getCpuLayoutConversionValue ()

 CPU Conversion settings between NHCW and NCHW.
 

.tensorflow.RewriterConfig.CpuLayout cpu_layout_conversion = 50;

abstrak publik RewriterConfig.CustomGraphOptimizer getCustomOptimizers (indeks int)

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

abstrak publik int getCustomOptimizersCount ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

Daftar abstrak publik< RewriterConfig.CustomGraphOptimizer > getCustomOptimizersList ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

abstrak publik RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder (int indeks)

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

Daftar abstrak publik<? memperluas RewriterConfig.CustomGraphOptimizerOrBuilder > getCustomOptimizersOrBuilderList ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

abstrak publik RewriterConfig.Toggle getDebugStripper ()

 Strips debug-related nodes from the graph (off by default).
 

.tensorflow.RewriterConfig.Toggle debug_stripper = 11;

abstrak publik int getDebugStripperValue ()

 Strips debug-related nodes from the graph (off by default).
 

.tensorflow.RewriterConfig.Toggle debug_stripper = 11;

abstrak publik RewriterConfig.Toggle getDependencyOptimization ()

 Control dependency optimizations (default is ON).
 Remove redundant control dependencies, which may enable other optimization.
 

.tensorflow.RewriterConfig.Toggle dependency_optimization = 8;

abstrak publik int getDependencyOptimizationValue ()

 Control dependency optimizations (default is ON).
 Remove redundant control dependencies, which may enable other optimization.
 

.tensorflow.RewriterConfig.Toggle dependency_optimization = 8;

boolean abstrak publik getDisableMetaOptimizer ()

 Disable the entire meta optimizer (off by default).
 

bool disable_meta_optimizer = 19;

boolean abstrak publik getDisableModelPruning ()

 If true, don't remove unnecessary ops from the graph
 

bool disable_model_pruning = 2;

boolean abstrak publik getExperimentalDisableCompressedTensorOptimization ()

 Disable optimizations that assume compressed tensors. Note that this flag
 is experimental and may be removed in the future.
 

bool experimental_disable_compressed_tensor_optimization = 26;

boolean abstrak publik getFailOnOptimizerErrors ()

 If true, any optimization pass failing will cause the MetaOptimizer to
 stop with an error. By default - or when set to false, failing passes are
 skipped silently.
 

bool fail_on_optimizer_errors = 21;

abstrak publik RewriterConfig.Toggle getFunctionOptimization ()

 Function optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle function_optimization = 10;

abstrak publik int getFunctionOptimizationValue ()

 Function optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle function_optimization = 10;

abstrak publik RewriterConfig.Toggle getImplementationSelector ()

 Enable the swap of kernel implementations based on the device placement
 (default is ON).
 

.tensorflow.RewriterConfig.Toggle implementation_selector = 22;

abstrak publik int getImplementationSelectorValue ()

 Enable the swap of kernel implementations based on the device placement
 (default is ON).
 

.tensorflow.RewriterConfig.Toggle implementation_selector = 22;

abstrak publik VerifierConfig getInterOptimizerVerifierConfig ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

abstrak publik VerifierConfigOrBuilder getInterOptimizerVerifierConfigOrBuilder ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

abstrak publik RewriterConfig.Toggle getLayoutOptimizer ()

 Optimize tensor layouts (default is ON)
 e.g. This will try to use NCHW layout on GPU which is faster.
 

.tensorflow.RewriterConfig.Toggle layout_optimizer = 1;

abstrak publik int getLayoutOptimizerValue ()

 Optimize tensor layouts (default is ON)
 e.g. This will try to use NCHW layout on GPU which is faster.
 

.tensorflow.RewriterConfig.Toggle layout_optimizer = 1;

abstrak publik RewriterConfig.Toggle getLoopOptimization ()

 Loop optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle loop_optimization = 9;

abstrak publik int getLoopOptimizationValue ()

 Loop optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle loop_optimization = 9;

abstrak publik RewriterConfig.MemOptType getMemoryOptimization ()

 Configures memory optimization passes through the meta-optimizer. Has no
 effect on manually requested memory optimization passes in the optimizers
 field.
 

.tensorflow.RewriterConfig.MemOptType memory_optimization = 4;

abstrak publik int getMemoryOptimizationValue ()

 Configures memory optimization passes through the meta-optimizer. Has no
 effect on manually requested memory optimization passes in the optimizers
 field.
 

.tensorflow.RewriterConfig.MemOptType memory_optimization = 4;

String abstrak publik getMemoryOptimizerTargetNodeNameScope ()

 A node name scope for node names which are valid outputs of recomputations.
 Inputs to nodes that match this scope may be recomputed (subject either to
 manual annotation of those input nodes or to manual annotation and
 heuristics depending on memory_optimization), but the nodes themselves will
 not be recomputed. This matches any sub-scopes as well, meaning the scope
 can appear not just as a top-level scope. For example, if the value is
 "gradients/", the default, it will match node name "gradients/foo",
 "foo/gradients/bar", but not "foo_gradients/"
 

string memory_optimizer_target_node_name_scope = 6;

abstrak publik com.google.protobuf.ByteString getMemoryOptimizerTargetNodeNameScopeBytes ()

 A node name scope for node names which are valid outputs of recomputations.
 Inputs to nodes that match this scope may be recomputed (subject either to
 manual annotation of those input nodes or to manual annotation and
 heuristics depending on memory_optimization), but the nodes themselves will
 not be recomputed. This matches any sub-scopes as well, meaning the scope
 can appear not just as a top-level scope. For example, if the value is
 "gradients/", the default, it will match node name "gradients/foo",
 "foo/gradients/bar", but not "foo_gradients/"
 

string memory_optimizer_target_node_name_scope = 6;

abstrak publik RewriterConfig.NumIterationsType getMetaOptimizerIterations ()

 Controls how many times we run the optimizers in meta optimizer (default
 is once).
 

.tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;

abstrak publik int getMetaOptimizerIterationsValue ()

 Controls how many times we run the optimizers in meta optimizer (default
 is once).
 

.tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;

abstrak publik getMetaOptimizerTimeoutMs panjang ()

 Maximum number of milliseconds to spend optimizing a single graph before
 timing out. If equal to 0 the system picks a default (currently 5 minutes).
 If less than 0 the optimizer will never time out.
 

int64 meta_optimizer_timeout_ms = 20;

abstrak publik int getMinGraphNodes ()

 The minimum number of nodes in a graph to optimizer. For smaller graphs,
 optimization is skipped.
 0 means the system picks an appropriate number.
 < 0 means do not skip optimization.
 

int32 min_graph_nodes = 17;

abstrak publik String getOptimizers (int indeks)

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

abstrak publik com.google.protobuf.ByteString getOptimizersBytes (indeks int)

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

abstrak publik int getOptimizersCount ()

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

Daftar abstrak publik<String> getOptimizersList ()

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

abstrak publik RewriterConfig.Toggle getPinToHostOptimization ()

 Force small ops onto the CPU (default is OFF).
 

.tensorflow.RewriterConfig.Toggle pin_to_host_optimization = 18;

abstrak publik int getPinToHostOptimizationValue ()

 Force small ops onto the CPU (default is OFF).
 

.tensorflow.RewriterConfig.Toggle pin_to_host_optimization = 18;

abstrak publik VerifierConfig getPostOptimizationVerifierConfig ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

abstrak publik VerifierConfigOrBuilder getPostOptimizationVerifierConfigOrBuilder ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

abstrak publik RewriterConfig.Toggle getRemapping ()

 Remapping (default is ON)
 Remap subgraphs onto more efficient implementations.
 

.tensorflow.RewriterConfig.Toggle remapping = 14;

abstrak publik int getRemappingValue ()

 Remapping (default is ON)
 Remap subgraphs onto more efficient implementations.
 

.tensorflow.RewriterConfig.Toggle remapping = 14;

abstrak publik RewriterConfig.Toggle getScopedAllocatorOptimization ()

 Try to allocate some independent Op outputs contiguously in order to
 merge or eliminate downstream Ops (off by default).
 

.tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;

abstrak publik int getScopedAllocatorOptimizationValue ()

 Try to allocate some independent Op outputs contiguously in order to
 merge or eliminate downstream Ops (off by default).
 

.tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;

abstrak publik ScopedAllocatorOptions getScopedAllocatorOpts ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

abstrak publik ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

abstrak publik RewriterConfig.Toggle getShapeOptimization ()

 Shape optimizations (default is ON)
 Simplify computations made on shapes.
 

.tensorflow.RewriterConfig.Toggle shape_optimization = 13;

abstrak publik int getShapeOptimizationValue ()

 Shape optimizations (default is ON)
 Simplify computations made on shapes.
 

.tensorflow.RewriterConfig.Toggle shape_optimization = 13;

boolean abstrak publik hasAutoParallel ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

boolean abstrak publik hasInterOptimizerVerifierConfig ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

boolean abstrak publik hasPostOptimizationVerifierConfig ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

boolean abstrak publik hasScopedAllocatorOpts ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;