tf.keras.layers.Hashing

A preprocessing layer which hashes and bins categorical features.

Inherits From: Layer, Module

tf.keras.layers.Hashing(
    num_bins,
    mask_value=None,
    salt=None,
    output_mode='int',
    sparse=False,
    **kwargs
)

This layer transforms categorical inputs to hashed output. It element-wise converts a ints or strings to ints in a fixed range. The stable hash function uses tensorflow::ops::Fingerprint to produce the same output consistently across all 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.

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

Example (FarmHash64)

layer = tf.keras.layers.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 a mask value

layer = tf.keras.layers.Hashing(num_bins=3, mask_value=&#x27;')
inp = [[&#x27;A'], ['B'], [''], ['C'], ['D']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[1],
         [1],
         [0],
         [2],
         [2]])>

Example (SipHash64)

layer = tf.keras.layers.Hashing(num_bins=3, salt=[133, 137])
inp = [[&#x27;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.Hashing(num_bins=3, salt=133)
inp = [[&#x27;A'], ['B'], ['C'], ['D'], ['E']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
  array([[0],
         [0],
         [2],
         [1],
         [0]])>

num_bins Number of hash bins. Note that this includes the mask_value bin, so the effective number of bins is (num_bins - 1) if mask_value is set.
mask_value A value that represents masked inputs, which are mapped to index 0. None means no mask term will be added and the hashing will start at index 0. Defaults to None.
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. If None, uses the FarmHash64 hash function. It also supports tuple/list of 2 unsigned integer numbers, see reference paper for details. Defaults to None.
output_mode Specification for the output of the layer. Values can bes "int", "one_hot", "multi_hot", or "count" 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. If the last dimension is size 1, will encode on that dimension. If the last dimension is not size 1, will append a new dimension for the encoded output.
  • "multi_hot": Encodes each sample in the input into a single array the same size as num_bins, containing a 1 for each bin index index present in the sample. Treats the last dimension as the sample dimension, if input shape is (..., sample_length), output shape will be (..., num_tokens).
  • "count": As "multi_hot", but the int array contains a count of the number of times the bin index appeared in the sample. Defaults to "int".
sparse Boolean. Only applicable to "one_hot", "multi_hot", and "count" output modes. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.
**kwargs Keyword arguments to construct a layer.

A single or list of string, int32 or int64 Tensor, SparseTensor or RaggedTensor of shape (batch_size, ...,)

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.