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|
Computes metrics across top K candidates surfaced by a retrieval model.
tfrs.metrics.Factorized(
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:
|
*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. |
reset_states
reset_states() -> None
Resets the metrics.
result
result() -> List[tf.Tensor]
Returns a list of metric results.
update_state
@abc.abstractmethodupdate_state( query_embeddings: tf.Tensor, true_candidate_embeddings: tf.Tensor, true_candidate_ids: Optional[tf.Tensor] = None ) -> tf.Operation
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