tf.keras.layers.experimental.preprocessing.Hashing

Implements categorical feature hashing, also known as "hashing trick".

Inherits From: PreprocessingLayer, Layer, Module

tf.keras.layers.experimental.preprocessing.Hashing(
    num_bins, salt=None, name=None, **kwargs
)

This layer transforms single or multiple categorical inputs to hashed output. It converts a sequence of int or string to a sequence of int. The stable hash function uses tensorflow::ops::Fingerprint to produce universal output that is consistent across platforms.

This layer uses FarmHash64 by default, which provides a consistent hashed output across different platforms and is stable across invocations, regardless of device and context, by mixing the input bits thoroughly.

If you want to obfuscate the hashed output, you can also pass a random salt argument in the constructor. In that case, the layer will use the SipHash64 hash function, with the salt value serving as additional input to the hash function.

Example (FarmHash64):

layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3)
inp = [['A'], ['B'], ['C'], ['D'], ['E']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[1],
         [0],
         [1],
         [1],
         [2]])>

Example (FarmHash64) with list of inputs:

>>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3)
>>> inp_1 = [['A'], ['B'], ['C'], ['D'], ['E']]
>>> inp_2 = np.asarray([[5], [4], [3], [2], [1]])
>>> layer([inp_1, inp_2])
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[1],
         [1],
         [0],
         [2],
         [0]])>

Example (SipHash64):

layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
   salt=[133, 137])
inp = [['A'], ['B'], ['C'], ['D'], ['E']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[1],
         [2],
         [1],
         [0],
         [2]])>

Example (Siphash64 with a single integer, same as salt=[133, 133]

layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
   salt=133)
inp = [['A'], ['B'], ['C'], ['D'], ['E']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[0],
         [0],
         [2],
         [1],
         [0]])>

Reference: SipHash with salt

num_bins Number of hash bins.
salt A single unsigned integer or None. If passed, the hash function used will be SipHash64, with these values used as an additional input (known as a "salt" in cryptography). These should be non-zero. Defaults to None (in that case, the FarmHash64 hash function is used). It also supports tuple/list of 2 unsigned integer numbers, see reference paper for details.
name Name to give to the layer.
**kwargs Keyword arguments to construct a layer.

Input shape: A single or list of string, int32 or int64 Tensor, SparseTensor or RaggedTensor of shape [batch_size, ...,]

Output shape: An int64 Tensor, SparseTensor or RaggedTensor of shape [batch_size, ...]. If any input is RaggedTensor then output is RaggedTensor, otherwise if any input is SparseTensor then output is SparseTensor, otherwise the output is Tensor.

Methods

adapt

View source

adapt(
    data, reset_state=True
)

Fits the state of the preprocessing layer to the data being passed.

Arguments
data The data to train on. It can be passed either as a tf.data Dataset, or as a numpy array.
reset_state Optional argument specifying whether to clear the state of the layer at the start of the call to adapt, or whether to start from the existing state. This argument may not be relevant to all preprocessing layers: a subclass of PreprocessingLayer may choose to throw if 'reset_state' is set to False.