RewriterConfigOrBuilder

antarmuka publik RewriterConfigOrBuilder
Subkelas Tidak Langsung yang Diketahui

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;