tf.types.experimental.ConcreteFunction
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Base class for differentiable graph functions.
Inherits From: Callable
A ConcreteFunction encapsulates the original graph function definition with
support for differentiability under tf.GradientTape contexts. In the
process, it may generate new graph functions (using the original) to
efficiently perform forwards and backwards passes.
Attributes |
|---|
function_type
|
Returns a FunctionType describing this callable.
|
inference_fn
|
Returns the original AtomicFunction owned by this ConcreteFunction.
|
Methods
__call__
View source
__call__(
*args, **kwargs
)
Executes this callable.
This behaves like a regular op - in eager mode, it immediately starts
execution, returning results. In graph mode, it creates ops which return
symbolic TensorFlow values (like tf.Tensor, tf.data.Dataset,
etc.). For example, tf.function callables typically generate a
tf.raw_ops.PartitionedCall op, but not always - the
exact operations being generated are an internal implementation detail.
| Args |
|---|
*args
|
positional argument for this call
|
**kwargs
|
keyword arguments for this call
|
| Returns |
|---|
|
The execution results.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.types.experimental.ConcreteFunction\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/types/core.py#L169-L182) |\n\nBase class for differentiable graph functions.\n\nInherits From: [`Callable`](../../../tf/types/experimental/Callable)\n\nA `ConcreteFunction` encapsulates the original graph function definition with\nsupport for differentiability under [`tf.GradientTape`](../../../tf/GradientTape) contexts. In the\nprocess, it may generate new graph functions (using the original) to\nefficiently perform forwards and backwards passes.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-----------------|-----------------------------------------------------------------------|\n| `function_type` | Returns a FunctionType describing this callable. |\n| `inference_fn` | Returns the original `AtomicFunction` owned by this ConcreteFunction. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/types/core.py#L124-L139) \n\n __call__(\n *args, **kwargs\n )\n\nExecutes this callable.\n\nThis behaves like a regular op - in eager mode, it immediately starts\nexecution, returning results. In graph mode, it creates ops which return\nsymbolic TensorFlow values (like [`tf.Tensor`](../../../tf/Tensor), [`tf.data.Dataset`](../../../tf/data/Dataset),\netc.). For example, [`tf.function`](../../../tf/function) callables typically generate a\n[`tf.raw_ops.PartitionedCall`](../../../tf/raw_ops/PartitionedCall) op, but not always - the\nexact operations being generated are an internal implementation detail.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------|-----------------------------------|\n| `*args` | positional argument for this call |\n| `**kwargs` | keyword arguments for this call |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| The execution results. ||\n\n\u003cbr /\u003e"]]
View source on GitHub