tf.keras.layers.HashedCrossing

A preprocessing layer which crosses features using the "hashing trick".

Inherits From: Layer, Module

tf.keras.layers.HashedCrossing(
    num_bins, output_mode='int', sparse=False, **kwargs
)

This layer performs crosses of categorical features using the "hasing trick". Conceptually, the transformation can be thought of as: hash(concatenation of features) % num_bins.

This layer currently only performs crosses of scalar inputs and batches of scalar inputs. Valid input shapes are (batch_size, 1), (batch_size,) and ().

For an overview and full list of preprocessing layers, see the preprocessing guide.

num_bins Number of hash bins.
output_mode Specification for the output of the layer. Defaults to "int". Values can be "int", or "one_hot" configuring the layer as follows:

  • "int": Return the integer bin indices directly.
  • "one_hot": Encodes each individual element in the input into an array the same size as num_bins, containing a 1 at the input's bin index.
sparse Boolean. Only applicable to "one_hot" mode. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.
**kwargs Keyword arguments to construct a layer.

Examples:

Crossing two scalar features.

layer = tf.keras.layers.HashedCrossing(
    num_bins=5)
feat1 = tf.constant(['A', 'B', 'A', 'B', 'A'])
feat2 = tf.constant([101, 101, 101, 102, 102])
layer((feat1, feat2))
<tf.Tensor: shape=(5,), dtype=int64, numpy=array([1, 4, 1, 1, 3])>

Crossing and one-hotting two scalar features.

layer = tf.keras.layers.HashedCrossing(
    num_bins=5, output_mode='one_hot')
feat1 = tf.constant(['A', 'B', 'A', 'B', 'A'])
feat2 = tf.constant([101, 101, 101, 102, 102])
layer((feat1, feat2))
<tf.Tensor: shape=(5, 5), dtype=float32, numpy=
  array([[0., 1., 0., 0., 0.],
         [0., 0., 0., 0., 1.],
         [0., 1., 0., 0., 0.],
         [0., 1., 0., 0., 0.],
         [0., 0., 0., 1., 0.]], dtype=float32)>