tfrs.layers.loss.SamplingProbablityCorrection
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Sampling probability correction.
tfrs.layers.loss.SamplingProbablityCorrection(
trainable=True, name=None, dtype=None, dynamic=False, **kwargs
)
Methods
call
call(
inputs, *args, **kwargs
)
This is where the layer's logic lives.
The call() method may not create state (except in its first
invocation, wrapping the creation of variables or other resources in
tf.init_scope()). It is recommended to create state, including
tf.Variable instances and nested Layer instances,
in __init__(), or in the build() method that is
called automatically before call() executes for the first time.
| Args |
|---|
inputs
|
Input tensor, or dict/list/tuple of input tensors.
The first positional inputs argument is subject to special rules:
inputs must be explicitly passed. A layer cannot have zero
arguments, and inputs cannot be provided via the default value
of a keyword argument.- NumPy array or Python scalar values in
inputs get cast as
tensors.
- Keras mask metadata is only collected from
inputs.
- Layers are built (
build(input_shape) method)
using shape info from inputs only.
input_spec compatibility is only checked against inputs.- Mixed precision input casting is only applied to
inputs.
If a layer has tensor arguments in *args or **kwargs, their
casting behavior in mixed precision should be handled manually.
- The SavedModel input specification is generated using
inputs
only.
- Integration with various ecosystem packages like TFMOT, TFLite,
TF.js, etc is only supported for
inputs and not for tensors in
positional and keyword arguments.
|
*args
|
Additional positional arguments. May contain tensors, although
this is not recommended, for the reasons above.
|
**kwargs
|
Additional keyword arguments. May contain tensors, although
this is not recommended, for the reasons above.
The following optional keyword arguments are reserved:
training: Boolean scalar tensor of Python boolean indicating
whether the call is meant for training or inference.
mask: Boolean input mask. If the layer's call() method takes a
mask argument, its default value will be set to the mask
generated for inputs by the previous layer (if input did come
from a layer that generated a corresponding mask, i.e. if it came
from a Keras layer with masking support).
|
| Returns |
|---|
|
A tensor or list/tuple of tensors.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tfrs.layers.loss.SamplingProbablityCorrection\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/recommenders/blob/v0.7.3/tensorflow_recommenders/layers/loss.py#L150-L158) |\n\nSampling probability correction. \n\n tfrs.layers.loss.SamplingProbablityCorrection(\n trainable=True, name=None, dtype=None, dynamic=False, **kwargs\n )\n\nMethods\n-------\n\n### `call`\n\n call(\n inputs, *args, **kwargs\n )\n\nThis is where the layer's logic lives.\n\nThe `call()` method may not create state (except in its first\ninvocation, wrapping the creation of variables or other resources in\n[`tf.init_scope()`](https://www.tensorflow.org/api_docs/python/tf/init_scope)). It is recommended to create state, including\n[`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable) instances and nested `Layer` instances,\nin `__init__()`, or in the `build()` method that is\ncalled automatically before `call()` executes for the first time.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `inputs` | Input tensor, or dict/list/tuple of input tensors. The first positional `inputs` argument is subject to special rules: \u003cbr /\u003e - `inputs` must be explicitly passed. A layer cannot have zero arguments, and `inputs` cannot be provided via the default value of a keyword argument. - NumPy array or Python scalar values in `inputs` get cast as tensors. - Keras mask metadata is only collected from `inputs`. - Layers are built (`build(input_shape)` method) using shape info from `inputs` only. - `input_spec` compatibility is only checked against `inputs`. - Mixed precision input casting is only applied to `inputs`. If a layer has tensor arguments in `*args` or `**kwargs`, their casting behavior in mixed precision should be handled manually. - The SavedModel input specification is generated using `inputs` only. - Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for `inputs` and not for tensors in positional and keyword arguments. |\n| `*args` | Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above. |\n| `**kwargs` | Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - `training`: Boolean scalar tensor of Python boolean indicating whether the `call` is meant for training or inference. - `mask`: Boolean input mask. If the layer's `call()` method takes a `mask` argument, its default value will be set to the mask generated for `inputs` by the previous layer (if `input` did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A tensor or list/tuple of tensors. ||\n\n\u003cbr /\u003e"]]
View source on GitHub