layer_hashing: A preprocessing layer which hashes and bins categorical...

In keras: R Interface to 'Keras'

A preprocessing layer which hashes and bins categorical features.

Description

A preprocessing layer which hashes and bins categorical features.

Usage

layer_hashing(
  object,
  num_bins,
  mask_value = NULL,
  salt = NULL,
  output_mode = "int",
  sparse = FALSE,
  ...
)

Arguments

object	What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is: missing or NULL, the Layer instance is returned. a Sequential model, the model with an additional layer is returned. a Tensor, the output tensor from layer_instance(object) is returned.
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 NULL, meaning no mask term will be added and the hashing will start at index 0.
salt	A single unsigned integer or NULL. 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 NULL (in that case, the FarmHash64 hash function is used). It also supports list of 2 unsigned integer numbers, see reference paper for details.
output_mode	Specification for the output of the layer. Defaults to "int". Values can be "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.
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.
...	standard layer arguments.

Details

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 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.

Example (FarmHash64)

layer <- layer_hashing(num_bins=3)
inp <- matrix(c('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 <- layer_hashing(num_bins=3, mask_value='')
inp <- matrix(c('A', 'B', 'C', 'D', 'E'))
layer(inp)
# <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
#   array([[1],
#          [1],
#          [0],
#          [2],
#          [2]])>

Example (SipHash64)

layer <- layer_hashing(num_bins=3, salt=c(133, 137))
inp <- matrix(c('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 <- layer_hashing(num_bins=3, salt=133)
inp <- matrix(c('A', 'B', 'C', 'D', 'E'))
layer(inp)
# <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
#   array([[0],
#          [0],
#          [2],
#          [1],
#          [0]])>

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/Hashing

• https://keras.io/api/layers/preprocessing_layers/categorical/hashing/

Other categorical features preprocessing layers: layer_category_encoding(), layer_integer_lookup(), layer_string_lookup()

Other preprocessing layers: layer_category_encoding(), layer_center_crop(), layer_discretization(), layer_integer_lookup(), layer_normalization(), layer_random_brightness(), layer_random_contrast(), layer_random_crop(), layer_random_flip(), layer_random_height(), layer_random_rotation(), layer_random_translation(), layer_random_width(), layer_random_zoom(), layer_rescaling(), layer_resizing(), layer_string_lookup(), layer_text_vectorization()

keras documentation built on May 29, 2024, 3:20 a.m.