Set experimental optimizer options.
tf.config.optimizer.set_experimental_options(
options
)
Used in the notebooks
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.
Args |
|---|
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.
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TensorFlow 1 version
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