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/r2.4/api_docs/python/tf/ragged/cross_hashed