tfp.experimental.auto_batching.instructions.interpret
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Interprets a program in this instruction language and returns the result.
tfp.experimental.auto_batching.instructions.interpret(
program, *inputs
)
This is a definitional interpreter; its purpose is to define the
semantics of the instruction language. As such, it does no
auto-batching, and generally strives to be as simple as possible.
It also does not stage graph computations, so will only work in
Eager mode TensorFlow.
Args |
|---|
program
|
The Program tuple to interpret.
|
*inputs
|
Values to pass to the program. The length of inputs must be
the same as the length of program.vars_in.
|
Returns |
|---|
results
|
A tuple of results, which are the values of the variables listed
in program.out_vars at termination.
|
Raises |
|---|
ValueError
|
If an internal invariant is violated, or an error is
detected in the program being interpreted.
|
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.experimental.auto_batching.instructions.interpret\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/auto_batching/instructions.py#L1119-L1194) |\n\nInterprets a program in this instruction language and returns the result.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tfp.experimental.auto_batching.frontend.instructions.interpret`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions/interpret), [`tfp.experimental.auto_batching.frontend.st.inst.interpret`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions/interpret), [`tfp.experimental.auto_batching.frontend.stack.inst.interpret`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions/interpret), [`tfp.experimental.auto_batching.stack_optimization.inst.interpret`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions/interpret), [`tfp.experimental.auto_batching.stackless.inst.interpret`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/auto_batching/instructions/interpret)\n\n\u003cbr /\u003e\n\n tfp.experimental.auto_batching.instructions.interpret(\n program, *inputs\n )\n\nThis is a definitional interpreter; its purpose is to define the\nsemantics of the instruction language. As such, it does no\nauto-batching, and generally strives to be as simple as possible.\nIt also does not stage graph computations, so will only work in\nEager mode TensorFlow.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|------------------------------------------------------------------------------------------------------------|\n| `program` | The Program tuple to interpret. |\n| `*inputs` | Values to pass to the program. The length of `inputs` must be the same as the length of `program.vars_in`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----------|--------------------------------------------------------------------------------------------------------|\n| `results` | A tuple of results, which are the values of the variables listed in `program.out_vars` at termination. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-------------------------------------------------------------------------------------------------|\n| `ValueError` | If an internal invariant is violated, or an error is detected in the program being interpreted. |\n\n\u003cbr /\u003e"]]
View source on GitHub