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.
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 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.
Defaults to "int".
sparse
Boolean. Only applicable to "one_hot", "multi_hot",
and "count" output modes. Only supported with TensorFlow
backend. If True, returns a SparseTensor instead of
a dense Tensor. Defaults to False.
**kwargs
Keyword arguments to construct a layer.
Input shape
A single string, a list of strings, or an int32 or int64 tensor
of shape (batch_size, ...,).
This method is the reverse of get_config,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights).
Args
config
A Python dictionary, typically the
output of get_config.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[]]