tf.sparse.add

TensorFlow 1 version

View source on GitHub

Generates 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

tf.sparse.cross(
    inputs, name=None, separator=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]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"

Customized separator "Y":

inp_0 = tf.constant([['a'], ['b']])
inp_1 = tf.constant([['c'], ['d']])
output = tf.sparse.cross([inp_0, inp_1], separator='_Y_')
output.values
<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a_Y_c', b'b_Y_d'],
  dtype=object)>

Args
`inputs`	An iterable of `Tensor` or `SparseTensor`.
`name`	Optional name for the op.
`separator`	A string added between each string being joined. Defaults to 'X'.

Returns
A `SparseTensor` of type `string`.

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 / add

View aliases

Args

Returns