Generates hashed feature cross from a list of tensors.
tf.ragged.cross_hashed(
inputs, num_buckets=0, hash_key=None, name=None
)
The input tensors must have rank=2
, and must all have the same number of
rows. The result is a RaggedTensor
with the same number of rows as the
inputs, where result[row]
contains a list of all combinations of values
formed by taking a single value from each input's corresponding row
(inputs[i][row]
). Values are combined by hashing together their
fingerprints. E.g.:
tf.ragged.cross_hashed([tf.ragged.constant([['a'], ['b', 'c']]),
tf.ragged.constant([['d'], ['e']]),
tf.ragged.constant([['f'], ['g']])],
num_buckets=100)
<tf.RaggedTensor [[78], [66, 74]]>
Args |
inputs
|
A list of RaggedTensor or Tensor or SparseTensor .
|
num_buckets
|
A non-negative int that used to bucket the hashed values. If
num_buckets != 0 , then output = hashed_value % num_buckets .
|
hash_key
|
Integer hash_key that will be used by the FingerprintCat64
function. If not given, a default key is used.
|
name
|
Optional name for the op.
|
Returns |
A 2D RaggedTensor of type int64 .
|