tf.ragged.cross_hashed
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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/ragged/cross_hashed