Module: tfr.utils
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Utility functions for ranking library.
Functions
de_noise(...)
: Returns a float Tensor
as the de-noised counts
.
gather_per_row(...)
: Gathers the values from input tensor based on per-row indices.
is_label_valid(...)
: Returns a boolean Tensor
for label validity.
organize_valid_indices(...)
: Organizes indices in such a way that valid items appear first.
padded_nd_indices(...)
: Pads the invalid entries by valid ones and returns the nd_indices.
parse_keys_and_weights(...)
: Parses the encoded key to keys and weights.
ragged_to_dense(...)
: Converts given inputs from ragged tensors to dense tensors.
reshape_first_ndims(...)
: Reshapes the first n dims of the input tensor
to new shape
.
reshape_to_2d(...)
: Converts the given tensor
to a 2-D Tensor
.
shuffle_valid_indices(...)
: Returns a shuffle of indices with valid ones on top.
sort_by_scores(...)
: Sorts list of features according to per-example scores.
sorted_ranks(...)
: Returns an int Tensor
as the ranks (1-based) after sorting scores.
Type Aliases
LossFunction
MetricFunction
TensorLike
TransformationFunction
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Last updated 2023-08-18 UTC.
[null,null,["Last updated 2023-08-18 UTC."],[],[],null,["# Module: tfr.utils\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/ranking/blob/v0.5.3/tensorflow_ranking/python/utils.py) |\n\nUtility functions for ranking library.\n\nFunctions\n---------\n\n[`de_noise(...)`](../tfr/utils/de_noise): Returns a float `Tensor` as the de-noised `counts`.\n\n[`gather_per_row(...)`](../tfr/utils/gather_per_row): Gathers the values from input tensor based on per-row indices.\n\n[`is_label_valid(...)`](../tfr/utils/is_label_valid): Returns a boolean `Tensor` for label validity.\n\n[`organize_valid_indices(...)`](../tfr/utils/organize_valid_indices): Organizes indices in such a way that valid items appear first.\n\n[`padded_nd_indices(...)`](../tfr/utils/padded_nd_indices): Pads the invalid entries by valid ones and returns the nd_indices.\n\n[`parse_keys_and_weights(...)`](../tfr/utils/parse_keys_and_weights): Parses the encoded key to keys and weights.\n\n[`ragged_to_dense(...)`](../tfr/utils/ragged_to_dense): Converts given inputs from ragged tensors to dense tensors.\n\n[`reshape_first_ndims(...)`](../tfr/utils/reshape_first_ndims): Reshapes the first n dims of the input `tensor` to `new shape`.\n\n[`reshape_to_2d(...)`](../tfr/utils/reshape_to_2d): Converts the given `tensor` to a 2-D `Tensor`.\n\n[`shuffle_valid_indices(...)`](../tfr/utils/shuffle_valid_indices): Returns a shuffle of indices with valid ones on top.\n\n[`sort_by_scores(...)`](../tfr/utils/sort_by_scores): Sorts list of features according to per-example scores.\n\n[`sorted_ranks(...)`](../tfr/utils/sorted_ranks): Returns an int `Tensor` as the ranks (1-based) after sorting scores.\n\nType Aliases\n------------\n\n[`LossFunction`](../tfr/utils/LossFunction)\n\n[`MetricFunction`](../tfr/utils/LossFunction)\n\n[`TensorLike`](../tfr/keras/model/TensorLike)\n\n[`TransformationFunction`](../tfr/keras/utils/GainFunction)"]]