tf.raw_ops.SparseCrossHashed
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Generates sparse cross from a list of sparse and dense tensors.
tf.raw_ops.SparseCrossHashed(
indices,
values,
shapes,
dense_inputs,
num_buckets,
strong_hash,
salt,
name=None
)
The op takes two lists, one of 2D SparseTensor
and one of 2D Tensor
, each
representing features of one feature column. It outputs a 2D SparseTensor
with
the batchwise crosses of these features.
For example, if the inputs are
inputs[0]: SparseTensor with shape = [2, 2]
[0, 0]: "a"
[1, 0]: "b"
[1, 1]: "c"
inputs[1]: SparseTensor with shape = [2, 1]
[0, 0]: "d"
[1, 0]: "e"
inputs[2]: Tensor [["f"], ["g"]]
then the output will be
shape = [2, 2]
[0, 0]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
if hashed_output=true then the output will be
shape = [2, 2]
[0, 0]: FingerprintCat64(
Fingerprint64("f"), FingerprintCat64(
Fingerprint64("d"), Fingerprint64("a")))
[1, 0]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("b")))
[1, 1]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("c")))
Args |
indices
|
A list of Tensor objects with type int64 .
2-D. Indices of each input SparseTensor .
|
values
|
A list of Tensor objects with types from: int64 , string .
1-D. values of each SparseTensor .
|
shapes
|
A list with the same length as indices of Tensor objects with type int64 .
1-D. Shapes of each SparseTensor .
|
dense_inputs
|
A list of Tensor objects with types from: int64 , string .
2-D. Columns represented by dense Tensor .
|
num_buckets
|
A Tensor of type int64 .
It is used if hashed_output is true.
output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
|
strong_hash
|
A Tensor of type bool .
boolean, if true, siphash with salt will be used instead of farmhash.
|
salt
|
A Tensor of type int64 .
Specify the salt that will be used by the siphash function.
|
name
|
A name for the operation (optional).
|
Returns |
A tuple of Tensor objects (output_indices, output_values, output_shape).
|
output_indices
|
A Tensor of type int64 .
|
output_values
|
A Tensor of type int64 .
|
output_shape
|
A Tensor of type int64 .
|
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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.raw_ops.SparseCrossHashed\n\n\u003cbr /\u003e\n\nGenerates sparse cross from a list of sparse and dense tensors.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.SparseCrossHashed`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseCrossHashed)\n\n\u003cbr /\u003e\n\n tf.raw_ops.SparseCrossHashed(\n indices,\n values,\n shapes,\n dense_inputs,\n num_buckets,\n strong_hash,\n salt,\n name=None\n )\n\nThe op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each\nrepresenting features of one feature column. It outputs a 2D `SparseTensor` with\nthe batchwise crosses of these features.\n\nFor example, if the inputs are \n\n inputs[0]: SparseTensor with shape = [2, 2]\n [0, 0]: \"a\"\n [1, 0]: \"b\"\n [1, 1]: \"c\"\n\n inputs[1]: SparseTensor with shape = [2, 1]\n [0, 0]: \"d\"\n [1, 0]: \"e\"\n\n inputs[2]: Tensor [[\"f\"], [\"g\"]]\n\nthen the output will be \n\n shape = [2, 2]\n [0, 0]: \"a_X_d_X_f\"\n [1, 0]: \"b_X_e_X_g\"\n [1, 1]: \"c_X_e_X_g\"\n\nif hashed_output=true then the output will be \n\n shape = [2, 2]\n [0, 0]: FingerprintCat64(\n Fingerprint64(\"f\"), FingerprintCat64(\n Fingerprint64(\"d\"), Fingerprint64(\"a\")))\n [1, 0]: FingerprintCat64(\n Fingerprint64(\"g\"), FingerprintCat64(\n Fingerprint64(\"e\"), Fingerprint64(\"b\")))\n [1, 1]: FingerprintCat64(\n Fingerprint64(\"g\"), FingerprintCat64(\n Fingerprint64(\"e\"), Fingerprint64(\"c\")))\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|-------------------------------------------------------------------------------------------------------------------------------------------|\n| `indices` | A list of `Tensor` objects with type `int64`. 2-D. Indices of each input `SparseTensor`. |\n| `values` | A list of `Tensor` objects with types from: `int64`, `string`. 1-D. values of each `SparseTensor`. |\n| `shapes` | A list with the same length as `indices` of `Tensor` objects with type `int64`. 1-D. Shapes of each `SparseTensor`. |\n| `dense_inputs` | A list of `Tensor` objects with types from: `int64`, `string`. 2-D. Columns represented by dense `Tensor`. |\n| `num_buckets` | A `Tensor` of type `int64`. It is used if hashed_output is true. output = hashed_value%num_buckets if num_buckets \\\u003e 0 else hashed_value. |\n| `strong_hash` | A `Tensor` of type `bool`. boolean, if true, siphash with salt will be used instead of farmhash. |\n| `salt` | A `Tensor` of type `int64`. Specify the salt that will be used by the siphash function. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|------------------|-----------------------------|\n| A tuple of `Tensor` objects (output_indices, output_values, output_shape). ||\n| `output_indices` | A `Tensor` of type `int64`. |\n| `output_values` | A `Tensor` of type `int64`. |\n| `output_shape` | A `Tensor` of type `int64`. |\n\n\u003cbr /\u003e"]]