|TensorFlow 1 version||View source on GitHub|
Set experimental optimizer options.
Used in the guide:
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.
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
- 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.