View source on GitHub |
A preprocessing layer which hashes and bins categorical features.
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='')
inp = [['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 = [['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 = [['A'], ['B'], ['C'], ['D'], ['E']]
layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[1],
[0]])>
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 .
|
Reference | |
---|---|