tf.keras.layers.HashedCrossing

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

Inherits From: Layer, Operation

tf.keras.layers.HashedCrossing(
    num_bins,
    output_mode='int',
    sparse=False,
    name=None,
    dtype=None,
    **kwargs
)

This layer performs crosses of categorical features using the "hashing trick". Conceptually, the transformation can be thought of as: `hash(concatenate(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 ().

Note: This layer wraps tf.keras.layers.HashedCrossing. It cannot be used as part of the compiled computation graph of a model with any backend other than TensorFlow. It can however be used with any backend when running eagerly. It can also always be used as part of an input preprocessing pipeline with any backend (outside the model itself), which is how we recommend to use this layer.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

Args
num_bins	Number of hash bins.
output_mode	Specification for the output of the layer. 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. Defaults to "int".
sparse	Boolean. Only applicable to "one_hot" mode and only valid when using the TensorFlow backend. 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 = keras.layers.HashedCrossing(
    num_bins=5)
feat1 = np.array(['A', 'B', 'A', 'B', 'A'])
feat2 = np.array([101, 101, 101, 102, 102])
layer((feat1, feat2))
array([1, 4, 1, 1, 3])

Crossing and one-hotting two scalar features.

layer = keras.layers.HashedCrossing(
    num_bins=5, output_mode='one_hot')
feat1 = np.array(['A', 'B', 'A', 'B', 'A'])
feat2 = np.array([101, 101, 101, 102, 102])
layer((feat1, feat2))
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)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)