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

Inherits From: `Layer`

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])

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

Arguments | |
---|---|

`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`

.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/layers/experimental/preprocessing/Hashing