LostTech.TensorFlow : API Documentation

Type tf.config.optimizer

Namespace tensorflow

Methods

Properties

Public static methods

IDictionary<string, object> get_experimental_options()

Get experimental optimizer options.

Refer to tf.config.optimizer.set_experimental_options for a list of current options.

Note that optimizations are only applied in graph mode, (within tf.function). In addition, as these are experimental options, the list is subject to change.
Returns
IDictionary<string, object>
Dictionary of configured experimental optimizer options

object get_experimental_options_dyn()

Get experimental optimizer options.

Refer to tf.config.optimizer.set_experimental_options for a list of current options.

Note that optimizations are only applied in graph mode, (within tf.function). In addition, as these are experimental options, the list is subject to change.
Returns
object
Dictionary of configured experimental optimizer options

bool get_jit()

Get if JIT compilation is enabled.

Note that optimizations are only applied in graph mode, (within tf.function).
Returns
bool
If JIT compilation is enabled.

object get_jit_dyn()

Get if JIT compilation is enabled.

Note that optimizations are only applied in graph mode, (within tf.function).
Returns
object
If JIT compilation is enabled.

void set_experimental_options(IDictionary<string, int> options)

Set experimental optimizer options.

Note that optimizations are only applied in graph mode, (within tf.function). In addition, as these are experimental options, the list is subject to change.
Parameters
IDictionary<string, int> options
Dictionary of experimental optimizer options to configure. Valid keys: - layout_optimizer: Optimize tensor layouts e.g. This will try to use NCHW layout on GPU which is faster. - constant_folding: Fold constants Statically infer the value of tensors when possible, and materialize the result using constants. - shape_optimization: Simplify computations made on shapes. - remapping: Remap subgraphs onto more efficient implementations. - arithmetic_optimization: Simplify arithmetic ops with common sub-expression elimination and arithmetic simplification. - dependency_optimization: Control dependency optimizations. Remove redundant control dependencies, which may enable other optimization. This optimizer is also essential for pruning Identity and NoOp nodes. - loop_optimization: Loop optimizations. - function_optimization: Function optimizations and inlining. - debug_stripper: Strips debug-related nodes from the graph. - disable_model_pruning: Disable removal of unnecessary ops from the graph - scoped_allocator_optimization: Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops. - pin_to_host_optimization: Force small ops onto the CPU. - implementation_selector: Enable the swap of kernel implementations based on the device placement. - auto_mixed_precision: Change certain float32 ops to float16 on Volta GPUs and above. Without the use of loss scaling, this can cause numerical underflow (see `keras.mixed_precision.experimental.LossScaleOptimizer`). - disable_meta_optimizer: Disable the entire meta optimizer. - min_graph_nodes: The minimum number of nodes in a graph to optimizer. For smaller graphs, optimization is skipped.

object set_experimental_options_dyn(object options)

Set experimental optimizer options.

Note that optimizations are only applied in graph mode, (within tf.function). In addition, as these are experimental options, the list is subject to change.
Parameters
object options
Dictionary of experimental optimizer options to configure. Valid keys: - layout_optimizer: Optimize tensor layouts e.g. This will try to use NCHW layout on GPU which is faster. - constant_folding: Fold constants Statically infer the value of tensors when possible, and materialize the result using constants. - shape_optimization: Simplify computations made on shapes. - remapping: Remap subgraphs onto more efficient implementations. - arithmetic_optimization: Simplify arithmetic ops with common sub-expression elimination and arithmetic simplification. - dependency_optimization: Control dependency optimizations. Remove redundant control dependencies, which may enable other optimization. This optimizer is also essential for pruning Identity and NoOp nodes. - loop_optimization: Loop optimizations. - function_optimization: Function optimizations and inlining. - debug_stripper: Strips debug-related nodes from the graph. - disable_model_pruning: Disable removal of unnecessary ops from the graph - scoped_allocator_optimization: Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops. - pin_to_host_optimization: Force small ops onto the CPU. - implementation_selector: Enable the swap of kernel implementations based on the device placement. - auto_mixed_precision: Change certain float32 ops to float16 on Volta GPUs and above. Without the use of loss scaling, this can cause numerical underflow (see `keras.mixed_precision.experimental.LossScaleOptimizer`). - disable_meta_optimizer: Disable the entire meta optimizer. - min_graph_nodes: The minimum number of nodes in a graph to optimizer. For smaller graphs, optimization is skipped.

void set_jit(bool enabled)

Set if JIT compilation is enabled.
Parameters
bool enabled
Whether to enable JIT compilation.

object set_jit_dyn(object enabled)

Set if JIT compilation is enabled.
Parameters
object enabled
Whether to enable JIT compilation.

Public properties

PythonFunctionContainer get_experimental_options_fn get;

PythonFunctionContainer get_jit_fn get;

PythonFunctionContainer set_experimental_options_fn get;

PythonFunctionContainer set_jit_fn get;