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, mask_value=None, salt=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 a mask value:

layer = tf.keras.layers.experimental.preprocessing.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.experimental.preprocessing.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.experimental.preprocessing.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]])>

Reference: SipHash with salt

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. Defaults to None, meaning no mask term will be added and the hashing will start at index 0.
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.
**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.

is_adapted Whether the layer has been fit to data already.
streaming Whether adapt can be called twice without resetting the state.

Methods

adapt

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adapt(
    data, batch_size=None, steps=None, 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.
batch_size Integer or None. Number of samples per state update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
steps Integer or None. Total number of steps (batches of samples) When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs.
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.

compile

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compile(
    run_eagerly=None, steps_per_execution=None
)

Configures the layer for adapt.

Arguments
run_eagerly Bool. Defaults to False. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function. steps_per_execution: Int. Defaults to 1. The number of batches to run during each tf.function call. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.

finalize_state

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finalize_state()

Finalize the statistics for the preprocessing layer.

This method is called at the end of adapt. This method handles any one-time operations that should occur after all data has been seen.

make_adapt_function

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make_adapt_function()

Creates a function to execute one step of adapt.

This method can be overridden to support custom adapt logic. This method is called by PreprocessingLayer.adapt.

Typically, this method directly controls tf.function settings, and delegates the actual state update logic to PreprocessingLayer.update_state.

This function is cached the first time PreprocessingLayer.adapt is called. The cache is cleared whenever PreprocessingLayer.compile is called.

Returns
Function. The function created by this method should accept a tf.data.Iterator, retrieve a batch, and update the state of the layer.

merge_state

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merge_state(
    layers
)

Merge the statistics of multiple preprocessing layers.

This layer will contain the merged state.

Arguments
layers Layers whose statistics should be merge with the statistics of this layer.

reset_state

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reset_state()

Resets the statistics of the preprocessing layer.

update_state

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update_state(
    data
)

Accumulates statistics for the preprocessing layer.

Arguments
data A mini-batch of inputs to the layer.