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TensorFlow 1 version View source on GitHub

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.

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.