tf.sparse.concat

TensorFlow 1 version

View source on GitHub

Generates hashed sparse cross from a list of sparse and dense tensors.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.sparse.cross_hashed

tf.sparse.cross_hashed(
    inputs, num_buckets=0, hash_key=None, name=None
)

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]: 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
`inputs`	An iterable of `Tensor` or `SparseTensor`.
`num_buckets`	An `int` that is `>= 0`. output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
`hash_key`	Integer hash_key that will be used by the `FingerprintCat64` function. If not given, will use a default key.
`name`	Optional name for the op.

Returns
A `SparseTensor` of type `int64`.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.sparse / concat

View aliases

Args

Returns