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")))
indices
Tensor objects with type int64.
2-D. Indices of each input SparseTensor.
values
Tensor objects with types from: int64, string.
1-D. values of each SparseTensor.
shapes
indices of Tensor objects with type int64.
1-D. Shapes of each SparseTensor.
dense_inputs
Tensor objects with types from: int64, string.
2-D. Columns represented by dense Tensor.
num_buckets
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
Tensor of type bool.
boolean, if true, siphash with salt will be used instead of farmhash.
salt
Tensor of type int64.
Specify the salt that will be used by the siphash function.
name
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.