tf.config.optimizer.set_experimental_options

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Set experimental optimizer options.

Aliases:

  • tf.compat.v1.config.optimizer.set_experimental_options
  • tf.compat.v2.config.optimizer.set_experimental_options
tf.config.optimizer.set_experimental_options(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.

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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